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state stringlengths 0 159k	srcUpToTactic stringlengths 387 167k	nextTactic stringlengths 3 9k	declUpToTactic stringlengths 22 11.5k	declId stringlengths 38 95	decl stringlengths 16 1.89k	file_tag stringlengths 17 73
case h.e'_6.a 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r hg : EqOn f g (EMetric.ball x r) y : E hy : y ∈ EMetric.ball 0 r ⊢ x + y ∈ EMetric.ball x r	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm	simpa [edist_eq_coe_nnnorm_sub] using hy	theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm	Mathlib.Analysis.Analytic.Basic.422_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r y z : E hz : z ∈ EMetric.ball 0 r ⊢ HasSum (fun n => (p n) fun x => z) (f (x + y + z - y))	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by	convert hf.hasSum hz using 2	/-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by	Mathlib.Analysis.Analytic.Basic.432_0.jQw1fRSE1vGpOll	/-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r	Mathlib_Analysis_Analytic_Basic
case h.e'_6.h.e'_1 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r y z : E hz : z ∈ EMetric.ball 0 r ⊢ x + y + z - y = x + z	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2	abel	/-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2	Mathlib.Analysis.Analytic.Basic.432_0.jQw1fRSE1vGpOll	/-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r	Mathlib_Analysis_Analytic_Basic
case h.e'_6.h.e'_1 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r y z : E hz : z ∈ EMetric.ball 0 r ⊢ x + y + z - y = x + z	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2	abel	/-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2	Mathlib.Analysis.Analytic.Basic.432_0.jQw1fRSE1vGpOll	/-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r y : E hy : y ∈ EMetric.ball x r ⊢ HasSum (fun n => (p n) fun x_1 => y - x) (f y)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by	have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy	theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by	Mathlib.Analysis.Analytic.Basic.443_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y)	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r y : E hy : y ∈ EMetric.ball x r ⊢ y - x ∈ EMetric.ball 0 r	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by	simpa [edist_eq_coe_nnnorm_sub] using hy	theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by	Mathlib.Analysis.Analytic.Basic.443_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y)	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r y : E hy : y ∈ EMetric.ball x r this : y - x ∈ EMetric.ball 0 r ⊢ HasSum (fun n => (p n) fun x_1 => y - x) (f y)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy	simpa only [add_sub_cancel'_right] using hf.hasSum this	theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy	Mathlib.Analysis.Analytic.Basic.443_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y)	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesAt f p x hg : f =ᶠ[𝓝 x] g ⊢ HasFPowerSeriesAt g p x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by	rcases hf with ⟨r₁, h₁⟩	theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by	Mathlib.Analysis.Analytic.Basic.463_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x	Mathlib_Analysis_Analytic_Basic
case intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hg : f =ᶠ[𝓝 x] g r₁ : ℝ≥0∞ h₁ : HasFPowerSeriesOnBall f p x r₁ ⊢ HasFPowerSeriesAt g p x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩	rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩	theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩	Mathlib.Analysis.Analytic.Basic.463_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hg : f =ᶠ[𝓝 x] g r₁ : ℝ≥0∞ h₁ : HasFPowerSeriesOnBall f p x r₁ r₂ : ℝ≥0∞ h₂pos : r₂ > 0 h₂ : EMetric.ball x r₂ ⊆ {x \| (fun x => f x = g x) x} ⊢ HasFPowerSeriesAt g p x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩	exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩	theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩	Mathlib.Analysis.Analytic.Basic.463_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r ⊢ ∀ᶠ (y : E) in 𝓝 0, HasSum (fun n => (p n) fun x => y) (f (x + y))	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by	filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum	theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by	Mathlib.Analysis.Analytic.Basic.479_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y))	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r ⊢ ∀ᶠ (y : E) in 𝓝 x, HasSum (fun n => (p n) fun x_1 => y - x) (f y)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by	filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub	theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by	Mathlib.Analysis.Analytic.Basic.490_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y)	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f 0 x r ⊢ ∀ᶠ (z : E) in 𝓝 x, f z = 0	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by	filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero	theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by	Mathlib.Analysis.Analytic.Basic.501_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ c : F e : E ⊢ HasFPowerSeriesOnBall (fun x => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by	refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩	theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by	Mathlib.Analysis.Analytic.Basic.513_0.jQw1fRSE1vGpOll	theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ c : F e : E ⊢ ⊤ ≤ FormalMultilinearSeries.radius (constFormalMultilinearSeries 𝕜 E c)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by	simp	theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by	Mathlib.Analysis.Analytic.Basic.513_0.jQw1fRSE1vGpOll	theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ c : F e y✝ : E x✝ : y✝ ∈ EMetric.ball 0 ⊤ n : ℕ hn : n ≠ 0 ⊢ ((constFormalMultilinearSeries 𝕜 E c n) fun x => y✝) = 0	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩	simp [constFormalMultilinearSeries_apply hn]	theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩	Mathlib.Analysis.Analytic.Basic.513_0.jQw1fRSE1vGpOll	theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesAt f pf x hg : HasFPowerSeriesAt g pg x ⊢ HasFPowerSeriesAt (f + g) (pf + pg) x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by	rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩	theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by	Mathlib.Analysis.Analytic.Basic.539_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x	Mathlib_Analysis_Analytic_Basic
case intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r' : ℝ≥0∞ hf : HasFPowerSeriesAt f pf x hg : HasFPowerSeriesAt g pg x r : ℝ≥0∞ hr : HasFPowerSeriesOnBall f pf x r ∧ HasFPowerSeriesOnBall g pg x r ⊢ HasFPowerSeriesAt (f + g) (pf + pg) x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩	exact ⟨r, hr.1.add hr.2⟩	theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩	Mathlib.Analysis.Analytic.Basic.539_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r ⊢ r ≤ FormalMultilinearSeries.radius (-pf)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by	rw [pf.radius_neg]	theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by	Mathlib.Analysis.Analytic.Basic.558_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r ⊢ r ≤ FormalMultilinearSeries.radius pf	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg]	exact hf.r_le	theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg]	Mathlib.Analysis.Analytic.Basic.558_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r hg : HasFPowerSeriesOnBall g pg x r ⊢ HasFPowerSeriesOnBall (f - g) (pf - pg) x r	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by	simpa only [sub_eq_add_neg] using hf.add hg.neg	theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by	Mathlib.Analysis.Analytic.Basic.577_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesAt f pf x hg : HasFPowerSeriesAt g pg x ⊢ HasFPowerSeriesAt (f - g) (pf - pg) x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by	simpa only [sub_eq_add_neg] using hf.add hg.neg	theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by	Mathlib.Analysis.Analytic.Basic.582_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : AnalyticAt 𝕜 f x hg : AnalyticAt 𝕜 g x ⊢ AnalyticAt 𝕜 (f - g) x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by	simpa only [sub_eq_add_neg] using hf.add hg.neg	theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by	Mathlib.Analysis.Analytic.Basic.587_0.jQw1fRSE1vGpOll	theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E ⊢ (pf 0) v = f x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by	have v_eq : v = fun i => 0 := Subsingleton.elim _ _	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 ⊢ (pf 0) v = f x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _	have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos]	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 ⊢ 0 ∈ EMetric.ball 0 r	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by	simp [hf.r_pos]	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 zero_mem : 0 ∈ EMetric.ball 0 r ⊢ (pf 0) v = f x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos]	have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos]	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 zero_mem : 0 ∈ EMetric.ball 0 r ⊢ ∀ (i : ℕ), i ≠ 0 → ((pf i) fun j => 0) = 0	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by	intro i hi	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 zero_mem : 0 ∈ EMetric.ball 0 r i : ℕ hi : i ≠ 0 ⊢ ((pf i) fun j => 0) = 0	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi	have : 0 < i := pos_iff_ne_zero.2 hi	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 zero_mem : 0 ∈ EMetric.ball 0 r i : ℕ hi : i ≠ 0 this : 0 < i ⊢ ((pf i) fun j => 0) = 0	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi	exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 zero_mem : 0 ∈ EMetric.ball 0 r this : ∀ (i : ℕ), i ≠ 0 → ((pf i) fun j => 0) = 0 ⊢ (pf 0) v = f x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl	have A := (hf.hasSum zero_mem).unique (hasSum_single _ this)	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f pf x r v : Fin 0 → E v_eq : v = fun i => 0 zero_mem : 0 ∈ EMetric.ball 0 r this : ∀ (i : ℕ), i ≠ 0 → ((pf i) fun j => 0) = 0 A : f (x + 0) = (pf 0) fun x => 0 ⊢ (pf 0) v = f x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this)	simpa [v_eq] using A.symm	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this)	Mathlib.Analysis.Analytic.Basic.621_0.jQw1fRSE1vGpOll	theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g✝ : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ g : F →L[𝕜] G h : HasFPowerSeriesOnBall f p x r y✝ : E hy : y✝ ∈ EMetric.ball 0 r ⊢ HasSum (fun n => (compFormalMultilinearSeries g p n) fun x => y✝) ((⇑g ∘ f) (x + y✝))	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by	simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy)	/-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by	Mathlib.Analysis.Analytic.Basic.639_0.jQw1fRSE1vGpOll	/-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g✝ : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ s : Set E g : F →L[𝕜] G h : AnalyticOn 𝕜 f s ⊢ AnalyticOn 𝕜 (⇑g ∘ f) s	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by	rintro x hx	/-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by	Mathlib.Analysis.Analytic.Basic.652_0.jQw1fRSE1vGpOll	/-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g✝ : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x✝ : E r r' : ℝ≥0∞ s : Set E g : F →L[𝕜] G h : AnalyticOn 𝕜 f s x : E hx : x ∈ s ⊢ AnalyticAt 𝕜 (⇑g ∘ f) x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx	rcases h x hx with ⟨p, r, hp⟩	/-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx	Mathlib.Analysis.Analytic.Basic.652_0.jQw1fRSE1vGpOll	/-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s	Mathlib_Analysis_Analytic_Basic
case intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g✝ : E → F p✝ pf pg : FormalMultilinearSeries 𝕜 E F x✝ : E r✝ r' : ℝ≥0∞ s : Set E g : F →L[𝕜] G h : AnalyticOn 𝕜 f s x : E hx : x ∈ s p : FormalMultilinearSeries 𝕜 E F r : ℝ≥0∞ hp : HasFPowerSeriesOnBall f p x r ⊢ AnalyticAt 𝕜 (⇑g ∘ f) x	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩	exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩	/-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩	Mathlib.Analysis.Analytic.Basic.652_0.jQw1fRSE1vGpOll	/-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r ⊢ ∃ a ∈ Ioo 0 1, ∃ C > 0, ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by	obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le)	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n ⊢ ∃ a ∈ Ioo 0 1, ∃ C > 0, ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le)	refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le)	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ ⊢ ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C / (1 - a) * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩	have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ ⊢ ‖y‖ < ↑r'	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by	rw [ball_zero_eq] at hy	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ {x \| ‖x‖ < ↑r'} n : ℕ ⊢ ‖y‖ < ↑r'	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy	exact hy	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' ⊢ ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C / (1 - a) * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy	have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr'	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' ⊢ ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C / (1 - a) * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr'	have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr'	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr'	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' ⊢ y ∈ EMetric.ball 0 r	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by	refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h)	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' ⊢ ↑‖y‖₊ < ↑r'	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h)	exact mod_cast yr'	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h)	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ⊢ ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C / (1 - a) * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr'	rw [norm_sub_rev, ← mul_div_right_comm]	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr'	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ⊢ ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm]	have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm]	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a ⊢ ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)	suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2]	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this✝ : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a this : ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a * (‖y‖ / ↑r')) ⊢ ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by	refine' this.trans _	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this✝ : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a this : ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a * (‖y‖ / ↑r')) ⊢ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a * (‖y‖ / ↑r')) ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _	have : 0 < a := ha.1	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this✝¹ : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a this✝ : ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a * (‖y‖ / ↑r')) this : 0 < a ⊢ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a * (‖y‖ / ↑r')) ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1	gcongr	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case hc 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this✝¹ : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a this✝ : ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a * (‖y‖ / ↑r')) this : 0 < a ⊢ 0 < 1 - a	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr	apply_rules [sub_pos.2, ha.2]	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a ⊢ ‖FormalMultilinearSeries.partialSum p n y - f (x + y)‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n / (1 - a * (‖y‖ / ↑r'))	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2]	apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this)	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2]	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a ⊢ ∀ (n : ℕ), ‖(p n) fun x => y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this)	intro n	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this)	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n✝ : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a n : ℕ ⊢ ‖(p n) fun x => y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n	calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc]	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n✝ : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a n : ℕ ⊢ ‖p n‖ * ∏ _i : Fin n, ‖y‖ = ‖p n‖ * ↑r' ^ n * (‖y‖ / ↑r') ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by	field_simp [mul_right_comm]	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n✝ : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a n : ℕ ⊢ ‖p n‖ * ↑r' ^ n * (‖y‖ / ↑r') ^ n ≤ C * a ^ n * (‖y‖ / ↑r') ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by	gcongr ?_ * _	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
case h 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n✝ : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a n : ℕ ⊢ ‖p n‖ * ↑r' ^ n ≤ C * a ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _;	apply hp	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _;	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n✝ : ℕ yr' : ‖y‖ < ↑r' hr'0 : 0 < ↑r' this : y ∈ EMetric.ball 0 r ya : a * (‖y‖ / ↑r') ≤ a n : ℕ ⊢ C * a ^ n * (‖y‖ / ↑r') ^ n ≤ C * (a * (‖y‖ / ↑r')) ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by	rw [mul_pow, mul_assoc]	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by	Mathlib.Analysis.Analytic.Basic.661_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r ⊢ ∃ a ∈ Ioo 0 1, ∃ C > 0, ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * a ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by	obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by	Mathlib.Analysis.Analytic.Basic.698_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n ⊢ ∃ a ∈ Ioo 0 1, ∃ C > 0, ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * a ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h	refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h	Mathlib.Analysis.Analytic.Basic.698_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ ⊢ C * (a * (‖y‖ / ↑r')) ^ n ≤ C * a ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩	have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩	Mathlib.Analysis.Analytic.Basic.698_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ ⊢ ‖y‖ < ↑r'	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by	rwa [ball_zero_eq] at hy	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by	Mathlib.Analysis.Analytic.Basic.698_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' ⊢ C * (a * (‖y‖ / ↑r')) ^ n ≤ C * a ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy	gcongr	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy	Mathlib.Analysis.Analytic.Basic.698_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro.h.ha 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' ⊢ 0 ≤ a * (‖y‖ / ↑r') case intro.intro.intro.intro.h.hab 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r'✝ : ℝ≥0∞ r' : ℝ≥0 hf : HasFPowerSeriesOnBall f p x r h : ↑r' < r a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : C > 0 hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n y : E hy : y ∈ Metric.ball 0 ↑r' n : ℕ yr' : ‖y‖ < ↑r' ⊢ a * (‖y‖ / ↑r') ≤ a	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr	exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)]	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr	Mathlib.Analysis.Analytic.Basic.698_0.jQw1fRSE1vGpOll	/-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesAt f p x n : ℕ ⊢ (fun y => f (x + y) - FormalMultilinearSeries.partialSum p n y) =O[𝓝 0] fun y => ‖y‖ ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by	rcases hf with ⟨r, hf⟩	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
case intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r' : ℝ≥0∞ n : ℕ r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r ⊢ (fun y => f (x + y) - FormalMultilinearSeries.partialSum p n y) =O[𝓝 0] fun y => ‖y‖ ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩	rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r'✝ : ℝ≥0∞ n : ℕ r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 r'0 : 0 < ↑r' h : ↑r' < r ⊢ (fun y => f (x + y) - FormalMultilinearSeries.partialSum p n y) =O[𝓝 0] fun y => ‖y‖ ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩	obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r'✝ : ℝ≥0∞ n : ℕ r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 r'0 : 0 < ↑r' h : ↑r' < r a C : ℝ hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n ⊢ (fun y => f (x + y) - FormalMultilinearSeries.partialSum p n y) =O[𝓝 0] fun y => ‖y‖ ^ n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h	refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r'✝ : ℝ≥0∞ n : ℕ r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 r'0 : 0 < ↑r' h : ↑r' < r a C : ℝ hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n ⊢ ∀ᶠ (x_1 : E) in 𝓝 0, ‖f (x + x_1) - FormalMultilinearSeries.partialSum p n x_1‖ ≤ C * (a / ↑r') ^ n * ‖‖x_1‖ ^ n‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩	replace r'0 : 0 < (r' : ℝ)	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
case r'0 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r'✝ : ℝ≥0∞ n : ℕ r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 r'0 : 0 < ↑r' h : ↑r' < r a C : ℝ hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n ⊢ 0 < ↑r'	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); ·	exact mod_cast r'0	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); ·	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
case intro.intro.intro.intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r'✝ : ℝ≥0∞ n : ℕ r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 h : ↑r' < r a C : ℝ hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n r'0 : 0 < ↑r' ⊢ ∀ᶠ (x_1 : E) in 𝓝 0, ‖f (x + x_1) - FormalMultilinearSeries.partialSum p n x_1‖ ≤ C * (a / ↑r') ^ n * ‖‖x_1‖ ^ n‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0	filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
case h 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r✝ r'✝ : ℝ≥0∞ n : ℕ r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 h : ↑r' < r a C : ℝ hp : ∀ y ∈ Metric.ball 0 ↑r', ∀ (n : ℕ), ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a * (‖y‖ / ↑r')) ^ n r'0 : 0 < ↑r' y : E hy : y ∈ Metric.ball 0 ↑r' ⊢ ‖f (x + y) - FormalMultilinearSeries.partialSum p n y‖ ≤ C * (a / ↑r') ^ n * ‖‖y‖ ^ n‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy	simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy	Mathlib.Analysis.Analytic.Basic.714_0.jQw1fRSE1vGpOll	/-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r r' : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r hr : r' < r ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by	lift r' to ℝ≥0 using ne_top_of_lt hr	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) ↑r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr	rcases (zero_le r').eq_or_lt with (rfl \| hr'0)	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case intro.inl 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r hr : ↑0 < r ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) ↑0)] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) ·	simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero]	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) ·	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case intro.inr 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) ↑r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero]	obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero]	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' ⊢ ∃ a ∈ Ioo 0 1, ∃ C > 0, ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n case intro.inr.intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) ↑r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n	exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le)	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case intro.inr.intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ * ↑r' ^ n ≤ C * a ^ n ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) ↑r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le)	simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le)	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case intro.inr.intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) ↑r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp	set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a))	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case intro.inr.intro.intro.intro.intro 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) ⊢ (fun y => f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) ↑r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a))	have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr · apply hp · apply hy'.le _ = B n := by -- porting note: in the original, `B` was in the `field_simp`, but now Lean does not -- accept it. The current proof works in Lean 4, but does not in Lean 3. field_simp [pow_succ] simp only [mul_assoc, mul_comm, mul_left_comm] have hBL : HasSum B (L y) := by apply HasSum.mul_left simp only [add_mul] have : ‖a‖ < 1 := by simp only [Real.norm_eq_abs, abs_of_pos ha.1, ha.2] rw [div_eq_mul_inv, div_eq_mul_inv] exact (hasSum_coe_mul_geometric_of_norm_lt_1 this).add -- porting note: was `convert`! ((hasSum_geometric_of_norm_lt_1 this).mul_left 2) exact hA.norm_le_of_bounded hBL hAB	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a))	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) ⊢ ∀ y ∈ EMetric.ball (x, x) ↑r', ‖f y.1 - f y.2 - (p 1) fun x => y.1 - y.2‖ ≤ L y	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by	intro y hy'	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' ⊢ ‖f y.1 - f y.2 - (p 1) fun x => y.1 - y.2‖ ≤ L y	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy'	have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy'	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy'	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' ⊢ y ∈ EMetric.ball x r ×ˢ EMetric.ball x r	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by	rw [EMetric.ball_prod_same]	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' ⊢ y ∈ EMetric.ball (x, x) r	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same]	exact EMetric.ball_subset_ball hr.le hy'	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same]	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r ⊢ ‖f y.1 - f y.2 - (p 1) fun x => y.1 - y.2‖ ≤ L y	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy'	set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy'	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x ⊢ ‖f y.1 - f y.2 - (p 1) fun x => y.1 - y.2‖ ≤ L y	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x	have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right]	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x ⊢ HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by	convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case h.e'_6 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x ⊢ (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) = f y.1 - f y.2 - ∑ i in Finset.range 2, (((p i) fun x_1 => y.1 - x) - (p i) fun x_1 => y.2 - x)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1	rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right]	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : y ∈ EMetric.ball (x, x) ↑r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) ⊢ ‖f y.1 - f y.2 - (p 1) fun x => y.1 - y.2‖ ≤ L y	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right]	rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy'	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right]	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) ⊢ ‖f y.1 - f y.2 - (p 1) fun x => y.1 - y.2‖ ≤ L y	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy'	set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n)	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy'	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) ⊢ ‖f y.1 - f y.2 - (p 1) fun x => y.1 - y.2‖ ≤ L y	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n)	have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr · apply hp · apply hy'.le _ = B n := by -- porting note: in the original, `B` was in the `field_simp`, but now Lean does not -- accept it. The current proof works in Lean 4, but does not in Lean 3. field_simp [pow_succ] simp only [mul_assoc, mul_comm, mul_left_comm]	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n)	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ ⊢ ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)`	simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)`	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ ⊢ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by	rw [pow_succ ‖y - (x, x)‖]	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ ⊢ ‖p (n + 2)‖ * ↑(n + 2) * (‖y - (x, x)‖ * ‖y - (x, x)‖ ^ n) * ‖y.1 - y.2‖ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖]	ring	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖]	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ ⊢ ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by	have : 0 < a := ha.1	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ this : 0 < a ⊢ ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1	gcongr	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case h.h₁ 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ this : 0 < a ⊢ ‖p (n + 2)‖ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2)	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr ·	apply hp	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr ·	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
case h.h₂.hab 𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ this : 0 < a ⊢ ‖y - (x, x)‖ ≤ ↑r'	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr · apply hp ·	apply hy'.le	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr · apply hp ·	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic
𝕜 : Type u_1 E : Type u_2 F : Type u_3 G : Type u_4 inst✝⁶ : NontriviallyNormedField 𝕜 inst✝⁵ : NormedAddCommGroup E inst✝⁴ : NormedSpace 𝕜 E inst✝³ : NormedAddCommGroup F inst✝² : NormedSpace 𝕜 F inst✝¹ : NormedAddCommGroup G inst✝ : NormedSpace 𝕜 G f g : E → F p pf pg : FormalMultilinearSeries 𝕜 E F x : E r : ℝ≥0∞ hf : HasFPowerSeriesOnBall f p x r r' : ℝ≥0 hr : ↑r' < r hr'0 : 0 < r' a : ℝ ha : a ∈ Ioo 0 1 C : ℝ hC : 0 < C hp : ∀ (n : ℕ), ‖p n‖ ≤ C * a ^ n / ↑r' ^ n L : E × E → ℝ := fun y => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) y : E × E hy' : ‖y - (x, x)‖₊ < r' hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r A : ℕ → F := fun n => ((p n) fun x_1 => y.1 - x) - (p n) fun x_1 => y.2 - x hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - (p 1) fun x => y.1 - y.2) B : ℕ → ℝ := fun n => C * (a / ↑r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((↑n + 2) * a ^ n) n : ℕ ⊢ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) = B n	/- Copyright (c) 2020 Sébastien Gouëzel. All rights reserved. Released under Apache 2.0 license as described in the file LICENSE. Authors: Sébastien Gouëzel, Yury Kudryashov -/ import Mathlib.Analysis.Calculus.FormalMultilinearSeries import Mathlib.Analysis.SpecificLimits.Normed import Mathlib.Logic.Equiv.Fin import Mathlib.Topology.Algebra.InfiniteSum.Module #align_import analysis.analytic.basic from "leanprover-community/mathlib"@"32253a1a1071173b33dc7d6a218cf722c6feb514" /-! # Analytic functions A function is analytic in one dimension around `0` if it can be written as a converging power series `Σ pₙ zⁿ`. This definition can be extended to any dimension (even in infinite dimension) by requiring that `pₙ` is a continuous `n`-multilinear map. In general, `pₙ` is not unique (in two dimensions, taking `p₂ (x, y) (x', y') = x y'` or `y x'` gives the same map when applied to a vector `(x, y) (x, y)`). A way to guarantee uniqueness is to take a symmetric `pₙ`, but this is not always possible in nonzero characteristic (in characteristic 2, the previous example has no symmetric representative). Therefore, we do not insist on symmetry or uniqueness in the definition, and we only require the existence of a converging series. The general framework is important to say that the exponential map on bounded operators on a Banach space is analytic, as well as the inverse on invertible operators. ## Main definitions Let `p` be a formal multilinear series from `E` to `F`, i.e., `p n` is a multilinear map on `E^n` for `n : ℕ`. * `p.radius`: the largest `r : ℝ≥0∞` such that `‖p n‖ * r^n` grows subexponentially. * `p.le_radius_of_bound`, `p.le_radius_of_bound_nnreal`, `p.le_radius_of_isBigO`: if `‖p n‖ * r ^ n` is bounded above, then `r ≤ p.radius`; * `p.isLittleO_of_lt_radius`, `p.norm_mul_pow_le_mul_pow_of_lt_radius`, `p.isLittleO_one_of_lt_radius`, `p.norm_mul_pow_le_of_lt_radius`, `p.nnnorm_mul_pow_le_of_lt_radius`: if `r < p.radius`, then `‖p n‖ * r ^ n` tends to zero exponentially; * `p.lt_radius_of_isBigO`: if `r ≠ 0` and `‖p n‖ * r ^ n = O(a ^ n)` for some `-1 < a < 1`, then `r < p.radius`; * `p.partialSum n x`: the sum `∑_{i = 0}^{n-1} pᵢ xⁱ`. * `p.sum x`: the sum `∑'_{i = 0}^{∞} pᵢ xⁱ`. Additionally, let `f` be a function from `E` to `F`. * `HasFPowerSeriesOnBall f p x r`: on the ball of center `x` with radius `r`, `f (x + y) = ∑'_n pₙ yⁿ`. * `HasFPowerSeriesAt f p x`: on some ball of center `x` with positive radius, holds `HasFPowerSeriesOnBall f p x r`. * `AnalyticAt 𝕜 f x`: there exists a power series `p` such that holds `HasFPowerSeriesAt f p x`. * `AnalyticOn 𝕜 f s`: the function `f` is analytic at every point of `s`. We develop the basic properties of these notions, notably: * If a function admits a power series, it is continuous (see `HasFPowerSeriesOnBall.continuousOn` and `HasFPowerSeriesAt.continuousAt` and `AnalyticAt.continuousAt`). * In a complete space, the sum of a formal power series with positive radius is well defined on the disk of convergence, see `FormalMultilinearSeries.hasFPowerSeriesOnBall`. * If a function admits a power series in a ball, then it is analytic at any point `y` of this ball, and the power series there can be expressed in terms of the initial power series `p` as `p.changeOrigin y`. See `HasFPowerSeriesOnBall.changeOrigin`. It follows in particular that the set of points at which a given function is analytic is open, see `isOpen_analyticAt`. ## Implementation details We only introduce the radius of convergence of a power series, as `p.radius`. For a power series in finitely many dimensions, there is a finer (directional, coordinate-dependent) notion, describing the polydisk of convergence. This notion is more specific, and not necessary to build the general theory. We do not define it here. -/ noncomputable section variable {𝕜 E F G : Type} open Topology Classical BigOperators NNReal Filter ENNReal open Set Filter Asymptotics namespace FormalMultilinearSeries variable [Ring 𝕜] [AddCommGroup E] [AddCommGroup F] [Module 𝕜 E] [Module 𝕜 F] variable [TopologicalSpace E] [TopologicalSpace F] variable [TopologicalAddGroup E] [TopologicalAddGroup F] variable [ContinuousConstSMul 𝕜 E] [ContinuousConstSMul 𝕜 F] /-- Given a formal multilinear series `p` and a vector `x`, then `p.sum x` is the sum `Σ pₙ xⁿ`. A priori, it only behaves well when `‖x‖ < p.radius`. -/ protected def sum (p : FormalMultilinearSeries 𝕜 E F) (x : E) : F := ∑' n : ℕ, p n fun _ => x #align formal_multilinear_series.sum FormalMultilinearSeries.sum /-- Given a formal multilinear series `p` and a vector `x`, then `p.partialSum n x` is the sum `Σ pₖ xᵏ` for `k ∈ {0,..., n-1}`. -/ def partialSum (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) (x : E) : F := ∑ k in Finset.range n, p k fun _ : Fin k => x #align formal_multilinear_series.partial_sum FormalMultilinearSeries.partialSum /-- The partial sums of a formal multilinear series are continuous. -/ theorem partialSum_continuous (p : FormalMultilinearSeries 𝕜 E F) (n : ℕ) : Continuous (p.partialSum n) := by unfold partialSum -- Porting note: added continuity #align formal_multilinear_series.partial_sum_continuous FormalMultilinearSeries.partialSum_continuous end FormalMultilinearSeries /-! ### The radius of a formal multilinear series -/ variable [NontriviallyNormedField 𝕜] [NormedAddCommGroup E] [NormedSpace 𝕜 E] [NormedAddCommGroup F] [NormedSpace 𝕜 F] [NormedAddCommGroup G] [NormedSpace 𝕜 G] namespace FormalMultilinearSeries variable (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} /-- The radius of a formal multilinear series is the largest `r` such that the sum `Σ ‖pₙ‖ ‖y‖ⁿ` converges for all `‖y‖ < r`. This implies that `Σ pₙ yⁿ` converges for all `‖y‖ < r`, but these definitions are not* equivalent in general. -/ def radius (p : FormalMultilinearSeries 𝕜 E F) : ℝ≥0∞ := ⨆ (r : ℝ≥0) (C : ℝ) (_ : ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C), (r : ℝ≥0∞) #align formal_multilinear_series.radius FormalMultilinearSeries.radius /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound (C : ℝ) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖ * (r : ℝ) ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := le_iSup_of_le r <\| le_iSup_of_le C <\| le_iSup (fun _ => (r : ℝ≥0∞)) h #align formal_multilinear_series.le_radius_of_bound FormalMultilinearSeries.le_radius_of_bound /-- If `‖pₙ‖ rⁿ` is bounded in `n`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_bound_nnreal (C : ℝ≥0) {r : ℝ≥0} (h : ∀ n : ℕ, ‖p n‖₊ * r ^ n ≤ C) : (r : ℝ≥0∞) ≤ p.radius := p.le_radius_of_bound C fun n => mod_cast h n #align formal_multilinear_series.le_radius_of_bound_nnreal FormalMultilinearSeries.le_radius_of_bound_nnreal /-- If `‖pₙ‖ rⁿ = O(1)`, as `n → ∞`, then the radius of `p` is at least `r`. -/ theorem le_radius_of_isBigO (h : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : ↑r ≤ p.radius := Exists.elim (isBigO_one_nat_atTop_iff.1 h) fun C hC => p.le_radius_of_bound C fun n => (le_abs_self _).trans (hC n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.le_radius_of_is_O FormalMultilinearSeries.le_radius_of_isBigO theorem le_radius_of_eventually_le (C) (h : ∀ᶠ n in atTop, ‖p n‖ * (r : ℝ) ^ n ≤ C) : ↑r ≤ p.radius := p.le_radius_of_isBigO <\| IsBigO.of_bound C <\| h.mono fun n hn => by simpa #align formal_multilinear_series.le_radius_of_eventually_le FormalMultilinearSeries.le_radius_of_eventually_le theorem le_radius_of_summable_nnnorm (h : Summable fun n => ‖p n‖₊ * r ^ n) : ↑r ≤ p.radius := p.le_radius_of_bound_nnreal (∑' n, ‖p n‖₊ * r ^ n) fun _ => le_tsum' h _ #align formal_multilinear_series.le_radius_of_summable_nnnorm FormalMultilinearSeries.le_radius_of_summable_nnnorm theorem le_radius_of_summable (h : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_summable_nnnorm <\| by simp only [← coe_nnnorm] at h exact mod_cast h #align formal_multilinear_series.le_radius_of_summable FormalMultilinearSeries.le_radius_of_summable theorem radius_eq_top_of_forall_nnreal_isBigO (h : ∀ r : ℝ≥0, (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] fun _ => (1 : ℝ)) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_isBigO (h r) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.radius_eq_top_of_forall_nnreal_is_O FormalMultilinearSeries.radius_eq_top_of_forall_nnreal_isBigO theorem radius_eq_top_of_eventually_eq_zero (h : ∀ᶠ n in atTop, p n = 0) : p.radius = ∞ := p.radius_eq_top_of_forall_nnreal_isBigO fun r => (isBigO_zero _ _).congr' (h.mono fun n hn => by simp [hn]) EventuallyEq.rfl #align formal_multilinear_series.radius_eq_top_of_eventually_eq_zero FormalMultilinearSeries.radius_eq_top_of_eventually_eq_zero theorem radius_eq_top_of_forall_image_add_eq_zero (n : ℕ) (hn : ∀ m, p (m + n) = 0) : p.radius = ∞ := p.radius_eq_top_of_eventually_eq_zero <\| mem_atTop_sets.2 ⟨n, fun _ hk => tsub_add_cancel_of_le hk ▸ hn _⟩ #align formal_multilinear_series.radius_eq_top_of_forall_image_add_eq_zero FormalMultilinearSeries.radius_eq_top_of_forall_image_add_eq_zero @[simp] theorem constFormalMultilinearSeries_radius {v : F} : (constFormalMultilinearSeries 𝕜 E v).radius = ⊤ := (constFormalMultilinearSeries 𝕜 E v).radius_eq_top_of_forall_image_add_eq_zero 1 (by simp [constFormalMultilinearSeries]) #align formal_multilinear_series.const_formal_multilinear_series_radius FormalMultilinearSeries.constFormalMultilinearSeries_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1`, `‖p n‖ rⁿ = o(aⁿ)`. -/ theorem isLittleO_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (a ^ ·) := by have := (TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4 rw [this] -- Porting note: was -- rw [(TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 4] simp only [radius, lt_iSup_iff] at h rcases h with ⟨t, C, hC, rt⟩ rw [ENNReal.coe_lt_coe, ← NNReal.coe_lt_coe] at rt have : 0 < (t : ℝ) := r.coe_nonneg.trans_lt rt rw [← div_lt_one this] at rt refine' ⟨_, rt, C, Or.inr zero_lt_one, fun n => _⟩ calc \|‖p n‖ * (r : ℝ) ^ n\| = ‖p n‖ * (t : ℝ) ^ n * (r / t : ℝ) ^ n := by field_simp [mul_right_comm, abs_mul] _ ≤ C * (r / t : ℝ) ^ n := by gcongr; apply hC #align formal_multilinear_series.is_o_of_lt_radius FormalMultilinearSeries.isLittleO_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ = o(1)`. -/ theorem isLittleO_one_of_lt_radius (h : ↑r < p.radius) : (fun n => ‖p n‖ * (r : ℝ) ^ n) =o[atTop] (fun _ => 1 : ℕ → ℝ) := let ⟨_, ha, hp⟩ := p.isLittleO_of_lt_radius h hp.trans <\| (isLittleO_pow_pow_of_lt_left ha.1.le ha.2).congr (fun _ => rfl) one_pow #align formal_multilinear_series.is_o_one_of_lt_radius FormalMultilinearSeries.isLittleO_one_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` tends to zero exponentially: for some `0 < a < 1` and `C > 0`, `‖p n‖ * r ^ n ≤ C * a ^ n`. -/ theorem norm_mul_pow_le_mul_pow_of_lt_radius (h : ↑r < p.radius) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C * a ^ n := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 1 5).mp (p.isLittleO_of_lt_radius h) rcases this with ⟨a, ha, C, hC, H⟩ exact ⟨a, ha, C, hC, fun n => (le_abs_self _).trans (H n)⟩ #align formal_multilinear_series.norm_mul_pow_le_mul_pow_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_mul_pow_of_lt_radius /-- If `r ≠ 0` and `‖pₙ‖ rⁿ = O(aⁿ)` for some `-1 < a < 1`, then `r < p.radius`. -/ theorem lt_radius_of_isBigO (h₀ : r ≠ 0) {a : ℝ} (ha : a ∈ Ioo (-1 : ℝ) 1) (hp : (fun n => ‖p n‖ * (r : ℝ) ^ n) =O[atTop] (a ^ ·)) : ↑r < p.radius := by -- Porting note: moved out of `rcases` have := ((TFAE_exists_lt_isLittleO_pow (fun n => ‖p n‖ * (r : ℝ) ^ n) 1).out 2 5) rcases this.mp ⟨a, ha, hp⟩ with ⟨a, ha, C, hC, hp⟩ rw [← pos_iff_ne_zero, ← NNReal.coe_pos] at h₀ lift a to ℝ≥0 using ha.1.le have : (r : ℝ) < r / a := by simpa only [div_one] using (div_lt_div_left h₀ zero_lt_one ha.1).2 ha.2 norm_cast at this rw [← ENNReal.coe_lt_coe] at this refine' this.trans_le (p.le_radius_of_bound C fun n => _) rw [NNReal.coe_div, div_pow, ← mul_div_assoc, div_le_iff (pow_pos ha.1 n)] exact (le_abs_self _).trans (hp n) set_option linter.uppercaseLean3 false in #align formal_multilinear_series.lt_radius_of_is_O FormalMultilinearSeries.lt_radius_of_isBigO /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ * (r : ℝ) ^ n ≤ C := let ⟨_, ha, C, hC, h⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h ⟨C, hC, fun n => (h n).trans <\| mul_le_of_le_one_right hC.lt.le (pow_le_one _ ha.1.le ha.2.le)⟩ #align formal_multilinear_series.norm_mul_pow_le_of_lt_radius FormalMultilinearSeries.norm_mul_pow_le_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem norm_le_div_pow_of_pos_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h0 : 0 < r) (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖ ≤ C / (r : ℝ) ^ n := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨C, hC, fun n => Iff.mpr (le_div_iff (pow_pos h0 _)) (hp n)⟩ #align formal_multilinear_series.norm_le_div_pow_of_pos_of_lt_radius FormalMultilinearSeries.norm_le_div_pow_of_pos_of_lt_radius /-- For `r` strictly smaller than the radius of `p`, then `‖pₙ‖ rⁿ` is bounded. -/ theorem nnnorm_mul_pow_le_of_lt_radius (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : (r : ℝ≥0∞) < p.radius) : ∃ C > 0, ∀ n, ‖p n‖₊ * r ^ n ≤ C := let ⟨C, hC, hp⟩ := p.norm_mul_pow_le_of_lt_radius h ⟨⟨C, hC.lt.le⟩, hC, mod_cast hp⟩ #align formal_multilinear_series.nnnorm_mul_pow_le_of_lt_radius FormalMultilinearSeries.nnnorm_mul_pow_le_of_lt_radius theorem le_radius_of_tendsto (p : FormalMultilinearSeries 𝕜 E F) {l : ℝ} (h : Tendsto (fun n => ‖p n‖ * (r : ℝ) ^ n) atTop (𝓝 l)) : ↑r ≤ p.radius := p.le_radius_of_isBigO (h.isBigO_one _) #align formal_multilinear_series.le_radius_of_tendsto FormalMultilinearSeries.le_radius_of_tendsto theorem le_radius_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : ↑r ≤ p.radius := p.le_radius_of_tendsto hs.tendsto_atTop_zero #align formal_multilinear_series.le_radius_of_summable_norm FormalMultilinearSeries.le_radius_of_summable_norm theorem not_summable_norm_of_radius_lt_nnnorm (p : FormalMultilinearSeries 𝕜 E F) {x : E} (h : p.radius < ‖x‖₊) : ¬Summable fun n => ‖p n‖ * ‖x‖ ^ n := fun hs => not_le_of_lt h (p.le_radius_of_summable_norm hs) #align formal_multilinear_series.not_summable_norm_of_radius_lt_nnnorm FormalMultilinearSeries.not_summable_norm_of_radius_lt_nnnorm theorem summable_norm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖ * (r : ℝ) ^ n := by obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius h exact .of_nonneg_of_le (fun n => mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg _)) hp ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) #align formal_multilinear_series.summable_norm_mul_pow FormalMultilinearSeries.summable_norm_mul_pow theorem summable_norm_apply (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => ‖p n fun _ => x‖ := by rw [mem_emetric_ball_zero_iff] at hx refine' .of_nonneg_of_le (fun _ => norm_nonneg _) (fun n => ((p n).le_op_norm _).trans_eq _) (p.summable_norm_mul_pow hx) simp #align formal_multilinear_series.summable_norm_apply FormalMultilinearSeries.summable_norm_apply theorem summable_nnnorm_mul_pow (p : FormalMultilinearSeries 𝕜 E F) {r : ℝ≥0} (h : ↑r < p.radius) : Summable fun n : ℕ => ‖p n‖₊ * r ^ n := by rw [← NNReal.summable_coe] push_cast exact p.summable_norm_mul_pow h #align formal_multilinear_series.summable_nnnorm_mul_pow FormalMultilinearSeries.summable_nnnorm_mul_pow protected theorem summable [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : Summable fun n : ℕ => p n fun _ => x := (p.summable_norm_apply hx).of_norm #align formal_multilinear_series.summable FormalMultilinearSeries.summable theorem radius_eq_top_of_summable_norm (p : FormalMultilinearSeries 𝕜 E F) (hs : ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n) : p.radius = ∞ := ENNReal.eq_top_of_forall_nnreal_le fun r => p.le_radius_of_summable_norm (hs r) #align formal_multilinear_series.radius_eq_top_of_summable_norm FormalMultilinearSeries.radius_eq_top_of_summable_norm theorem radius_eq_top_iff_summable_norm (p : FormalMultilinearSeries 𝕜 E F) : p.radius = ∞ ↔ ∀ r : ℝ≥0, Summable fun n => ‖p n‖ * (r : ℝ) ^ n := by constructor · intro h r obtain ⟨a, ha : a ∈ Ioo (0 : ℝ) 1, C, - : 0 < C, hp⟩ := p.norm_mul_pow_le_mul_pow_of_lt_radius (show (r : ℝ≥0∞) < p.radius from h.symm ▸ ENNReal.coe_lt_top) refine' .of_norm_bounded (fun n => (C : ℝ) * a ^ n) ((summable_geometric_of_lt_1 ha.1.le ha.2).mul_left _) fun n => _ specialize hp n rwa [Real.norm_of_nonneg (mul_nonneg (norm_nonneg _) (pow_nonneg r.coe_nonneg n))] · exact p.radius_eq_top_of_summable_norm #align formal_multilinear_series.radius_eq_top_iff_summable_norm FormalMultilinearSeries.radius_eq_top_iff_summable_norm /-- If the radius of `p` is positive, then `‖pₙ‖` grows at most geometrically. -/ theorem le_mul_pow_of_radius_pos (p : FormalMultilinearSeries 𝕜 E F) (h : 0 < p.radius) : ∃ (C r : _) (hC : 0 < C) (_ : 0 < r), ∀ n, ‖p n‖ ≤ C * r ^ n := by rcases ENNReal.lt_iff_exists_nnreal_btwn.1 h with ⟨r, r0, rlt⟩ have rpos : 0 < (r : ℝ) := by simp [ENNReal.coe_pos.1 r0] rcases norm_le_div_pow_of_pos_of_lt_radius p rpos rlt with ⟨C, Cpos, hCp⟩ refine' ⟨C, r⁻¹, Cpos, by simp only [inv_pos, rpos], fun n => _⟩ -- Porting note: was `convert` rw [inv_pow, ← div_eq_mul_inv] exact hCp n #align formal_multilinear_series.le_mul_pow_of_radius_pos FormalMultilinearSeries.le_mul_pow_of_radius_pos /-- The radius of the sum of two formal series is at least the minimum of their two radii. -/ theorem min_radius_le_radius_add (p q : FormalMultilinearSeries 𝕜 E F) : min p.radius q.radius ≤ (p + q).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ rw [lt_min_iff] at hr have := ((p.isLittleO_one_of_lt_radius hr.1).add (q.isLittleO_one_of_lt_radius hr.2)).isBigO refine' (p + q).le_radius_of_isBigO ((isBigO_of_le _ fun n => _).trans this) rw [← add_mul, norm_mul, norm_mul, norm_norm] exact mul_le_mul_of_nonneg_right ((norm_add_le _ _).trans (le_abs_self _)) (norm_nonneg _) #align formal_multilinear_series.min_radius_le_radius_add FormalMultilinearSeries.min_radius_le_radius_add @[simp] theorem radius_neg (p : FormalMultilinearSeries 𝕜 E F) : (-p).radius = p.radius := by simp only [radius, neg_apply, norm_neg] #align formal_multilinear_series.radius_neg FormalMultilinearSeries.radius_neg protected theorem hasSum [CompleteSpace F] (p : FormalMultilinearSeries 𝕜 E F) {x : E} (hx : x ∈ EMetric.ball (0 : E) p.radius) : HasSum (fun n : ℕ => p n fun _ => x) (p.sum x) := (p.summable hx).hasSum #align formal_multilinear_series.has_sum FormalMultilinearSeries.hasSum theorem radius_le_radius_continuousLinearMap_comp (p : FormalMultilinearSeries 𝕜 E F) (f : F →L[𝕜] G) : p.radius ≤ (f.compFormalMultilinearSeries p).radius := by refine' ENNReal.le_of_forall_nnreal_lt fun r hr => _ apply le_radius_of_isBigO apply (IsBigO.trans_isLittleO _ (p.isLittleO_one_of_lt_radius hr)).isBigO refine' IsBigO.mul (@IsBigOWith.isBigO _ _ _ _ _ ‖f‖ _ _ _ _) (isBigO_refl _ _) refine IsBigOWith.of_bound (eventually_of_forall fun n => ?_) simpa only [norm_norm] using f.norm_compContinuousMultilinearMap_le (p n) #align formal_multilinear_series.radius_le_radius_continuous_linear_map_comp FormalMultilinearSeries.radius_le_radius_continuousLinearMap_comp end FormalMultilinearSeries /-! ### Expanding a function as a power series -/ section variable {f g : E → F} {p pf pg : FormalMultilinearSeries 𝕜 E F} {x : E} {r r' : ℝ≥0∞} /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series on the ball of radius `r > 0` around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `‖y‖ < r`. -/ structure HasFPowerSeriesOnBall (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) (r : ℝ≥0∞) : Prop where r_le : r ≤ p.radius r_pos : 0 < r hasSum : ∀ {y}, y ∈ EMetric.ball (0 : E) r → HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) #align has_fpower_series_on_ball HasFPowerSeriesOnBall /-- Given a function `f : E → F` and a formal multilinear series `p`, we say that `f` has `p` as a power series around `x` if `f (x + y) = ∑' pₙ yⁿ` for all `y` in a neighborhood of `0`. -/ def HasFPowerSeriesAt (f : E → F) (p : FormalMultilinearSeries 𝕜 E F) (x : E) := ∃ r, HasFPowerSeriesOnBall f p x r #align has_fpower_series_at HasFPowerSeriesAt variable (𝕜) /-- Given a function `f : E → F`, we say that `f` is analytic at `x` if it admits a convergent power series expansion around `x`. -/ def AnalyticAt (f : E → F) (x : E) := ∃ p : FormalMultilinearSeries 𝕜 E F, HasFPowerSeriesAt f p x #align analytic_at AnalyticAt /-- Given a function `f : E → F`, we say that `f` is analytic on a set `s` if it is analytic around every point of `s`. -/ def AnalyticOn (f : E → F) (s : Set E) := ∀ x, x ∈ s → AnalyticAt 𝕜 f x #align analytic_on AnalyticOn variable {𝕜} theorem HasFPowerSeriesOnBall.hasFPowerSeriesAt (hf : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesAt f p x := ⟨r, hf⟩ #align has_fpower_series_on_ball.has_fpower_series_at HasFPowerSeriesOnBall.hasFPowerSeriesAt theorem HasFPowerSeriesAt.analyticAt (hf : HasFPowerSeriesAt f p x) : AnalyticAt 𝕜 f x := ⟨p, hf⟩ #align has_fpower_series_at.analytic_at HasFPowerSeriesAt.analyticAt theorem HasFPowerSeriesOnBall.analyticAt (hf : HasFPowerSeriesOnBall f p x r) : AnalyticAt 𝕜 f x := hf.hasFPowerSeriesAt.analyticAt #align has_fpower_series_on_ball.analytic_at HasFPowerSeriesOnBall.analyticAt theorem HasFPowerSeriesOnBall.congr (hf : HasFPowerSeriesOnBall f p x r) (hg : EqOn f g (EMetric.ball x r)) : HasFPowerSeriesOnBall g p x r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {y} hy => by convert hf.hasSum hy using 1 apply hg.symm simpa [edist_eq_coe_nnnorm_sub] using hy } #align has_fpower_series_on_ball.congr HasFPowerSeriesOnBall.congr /-- If a function `f` has a power series `p` around `x`, then the function `z ↦ f (z - y)` has the same power series around `x + y`. -/ theorem HasFPowerSeriesOnBall.comp_sub (hf : HasFPowerSeriesOnBall f p x r) (y : E) : HasFPowerSeriesOnBall (fun z => f (z - y)) p (x + y) r := { r_le := hf.r_le r_pos := hf.r_pos hasSum := fun {z} hz => by convert hf.hasSum hz using 2 abel } #align has_fpower_series_on_ball.comp_sub HasFPowerSeriesOnBall.comp_sub theorem HasFPowerSeriesOnBall.hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) {y : E} (hy : y ∈ EMetric.ball x r) : HasSum (fun n : ℕ => p n fun _ => y - x) (f y) := by have : y - x ∈ EMetric.ball (0 : E) r := by simpa [edist_eq_coe_nnnorm_sub] using hy simpa only [add_sub_cancel'_right] using hf.hasSum this #align has_fpower_series_on_ball.has_sum_sub HasFPowerSeriesOnBall.hasSum_sub theorem HasFPowerSeriesOnBall.radius_pos (hf : HasFPowerSeriesOnBall f p x r) : 0 < p.radius := lt_of_lt_of_le hf.r_pos hf.r_le #align has_fpower_series_on_ball.radius_pos HasFPowerSeriesOnBall.radius_pos theorem HasFPowerSeriesAt.radius_pos (hf : HasFPowerSeriesAt f p x) : 0 < p.radius := let ⟨_, hr⟩ := hf hr.radius_pos #align has_fpower_series_at.radius_pos HasFPowerSeriesAt.radius_pos theorem HasFPowerSeriesOnBall.mono (hf : HasFPowerSeriesOnBall f p x r) (r'_pos : 0 < r') (hr : r' ≤ r) : HasFPowerSeriesOnBall f p x r' := ⟨le_trans hr hf.1, r'_pos, fun hy => hf.hasSum (EMetric.ball_subset_ball hr hy)⟩ #align has_fpower_series_on_ball.mono HasFPowerSeriesOnBall.mono theorem HasFPowerSeriesAt.congr (hf : HasFPowerSeriesAt f p x) (hg : f =ᶠ[𝓝 x] g) : HasFPowerSeriesAt g p x := by rcases hf with ⟨r₁, h₁⟩ rcases EMetric.mem_nhds_iff.mp hg with ⟨r₂, h₂pos, h₂⟩ exact ⟨min r₁ r₂, (h₁.mono (lt_min h₁.r_pos h₂pos) inf_le_left).congr fun y hy => h₂ (EMetric.ball_subset_ball inf_le_right hy)⟩ #align has_fpower_series_at.congr HasFPowerSeriesAt.congr protected theorem HasFPowerSeriesAt.eventually (hf : HasFPowerSeriesAt f p x) : ∀ᶠ r : ℝ≥0∞ in 𝓝[>] 0, HasFPowerSeriesOnBall f p x r := let ⟨_, hr⟩ := hf mem_of_superset (Ioo_mem_nhdsWithin_Ioi (left_mem_Ico.2 hr.r_pos)) fun _ hr' => hr.mono hr'.1 hr'.2.le #align has_fpower_series_at.eventually HasFPowerSeriesAt.eventually theorem HasFPowerSeriesOnBall.eventually_hasSum (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := by filter_upwards [EMetric.ball_mem_nhds (0 : E) hf.r_pos] using fun _ => hf.hasSum #align has_fpower_series_on_ball.eventually_has_sum HasFPowerSeriesOnBall.eventually_hasSum theorem HasFPowerSeriesAt.eventually_hasSum (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 0, HasSum (fun n : ℕ => p n fun _ : Fin n => y) (f (x + y)) := let ⟨_, hr⟩ := hf hr.eventually_hasSum #align has_fpower_series_at.eventually_has_sum HasFPowerSeriesAt.eventually_hasSum theorem HasFPowerSeriesOnBall.eventually_hasSum_sub (hf : HasFPowerSeriesOnBall f p x r) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := by filter_upwards [EMetric.ball_mem_nhds x hf.r_pos] with y using hf.hasSum_sub #align has_fpower_series_on_ball.eventually_has_sum_sub HasFPowerSeriesOnBall.eventually_hasSum_sub theorem HasFPowerSeriesAt.eventually_hasSum_sub (hf : HasFPowerSeriesAt f p x) : ∀ᶠ y in 𝓝 x, HasSum (fun n : ℕ => p n fun _ : Fin n => y - x) (f y) := let ⟨_, hr⟩ := hf hr.eventually_hasSum_sub #align has_fpower_series_at.eventually_has_sum_sub HasFPowerSeriesAt.eventually_hasSum_sub theorem HasFPowerSeriesOnBall.eventually_eq_zero (hf : HasFPowerSeriesOnBall f (0 : FormalMultilinearSeries 𝕜 E F) x r) : ∀ᶠ z in 𝓝 x, f z = 0 := by filter_upwards [hf.eventually_hasSum_sub] with z hz using hz.unique hasSum_zero #align has_fpower_series_on_ball.eventually_eq_zero HasFPowerSeriesOnBall.eventually_eq_zero theorem HasFPowerSeriesAt.eventually_eq_zero (hf : HasFPowerSeriesAt f (0 : FormalMultilinearSeries 𝕜 E F) x) : ∀ᶠ z in 𝓝 x, f z = 0 := let ⟨_, hr⟩ := hf hr.eventually_eq_zero #align has_fpower_series_at.eventually_eq_zero HasFPowerSeriesAt.eventually_eq_zero theorem hasFPowerSeriesOnBall_const {c : F} {e : E} : HasFPowerSeriesOnBall (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e ⊤ := by refine' ⟨by simp, WithTop.zero_lt_top, fun _ => hasSum_single 0 fun n hn => _⟩ simp [constFormalMultilinearSeries_apply hn] #align has_fpower_series_on_ball_const hasFPowerSeriesOnBall_const theorem hasFPowerSeriesAt_const {c : F} {e : E} : HasFPowerSeriesAt (fun _ => c) (constFormalMultilinearSeries 𝕜 E c) e := ⟨⊤, hasFPowerSeriesOnBall_const⟩ #align has_fpower_series_at_const hasFPowerSeriesAt_const theorem analyticAt_const {v : F} : AnalyticAt 𝕜 (fun _ => v) x := ⟨constFormalMultilinearSeries 𝕜 E v, hasFPowerSeriesAt_const⟩ #align analytic_at_const analyticAt_const theorem analyticOn_const {v : F} {s : Set E} : AnalyticOn 𝕜 (fun _ => v) s := fun _ _ => analyticAt_const #align analytic_on_const analyticOn_const theorem HasFPowerSeriesOnBall.add (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f + g) (pf + pg) x r := { r_le := le_trans (le_min_iff.2 ⟨hf.r_le, hg.r_le⟩) (pf.min_radius_le_radius_add pg) r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).add (hg.hasSum hy) } #align has_fpower_series_on_ball.add HasFPowerSeriesOnBall.add theorem HasFPowerSeriesAt.add (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f + g) (pf + pg) x := by rcases (hf.eventually.and hg.eventually).exists with ⟨r, hr⟩ exact ⟨r, hr.1.add hr.2⟩ #align has_fpower_series_at.add HasFPowerSeriesAt.add theorem AnalyticAt.congr (hf : AnalyticAt 𝕜 f x) (hg : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 g x := let ⟨_, hpf⟩ := hf (hpf.congr hg).analyticAt theorem analyticAt_congr (h : f =ᶠ[𝓝 x] g) : AnalyticAt 𝕜 f x ↔ AnalyticAt 𝕜 g x := ⟨fun hf ↦ hf.congr h, fun hg ↦ hg.congr h.symm⟩ theorem AnalyticAt.add (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f + g) x := let ⟨_, hpf⟩ := hf let ⟨_, hqf⟩ := hg (hpf.add hqf).analyticAt #align analytic_at.add AnalyticAt.add theorem HasFPowerSeriesOnBall.neg (hf : HasFPowerSeriesOnBall f pf x r) : HasFPowerSeriesOnBall (-f) (-pf) x r := { r_le := by rw [pf.radius_neg] exact hf.r_le r_pos := hf.r_pos hasSum := fun hy => (hf.hasSum hy).neg } #align has_fpower_series_on_ball.neg HasFPowerSeriesOnBall.neg theorem HasFPowerSeriesAt.neg (hf : HasFPowerSeriesAt f pf x) : HasFPowerSeriesAt (-f) (-pf) x := let ⟨_, hrf⟩ := hf hrf.neg.hasFPowerSeriesAt #align has_fpower_series_at.neg HasFPowerSeriesAt.neg theorem AnalyticAt.neg (hf : AnalyticAt 𝕜 f x) : AnalyticAt 𝕜 (-f) x := let ⟨_, hpf⟩ := hf hpf.neg.analyticAt #align analytic_at.neg AnalyticAt.neg theorem HasFPowerSeriesOnBall.sub (hf : HasFPowerSeriesOnBall f pf x r) (hg : HasFPowerSeriesOnBall g pg x r) : HasFPowerSeriesOnBall (f - g) (pf - pg) x r := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_on_ball.sub HasFPowerSeriesOnBall.sub theorem HasFPowerSeriesAt.sub (hf : HasFPowerSeriesAt f pf x) (hg : HasFPowerSeriesAt g pg x) : HasFPowerSeriesAt (f - g) (pf - pg) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align has_fpower_series_at.sub HasFPowerSeriesAt.sub theorem AnalyticAt.sub (hf : AnalyticAt 𝕜 f x) (hg : AnalyticAt 𝕜 g x) : AnalyticAt 𝕜 (f - g) x := by simpa only [sub_eq_add_neg] using hf.add hg.neg #align analytic_at.sub AnalyticAt.sub theorem AnalyticOn.mono {s t : Set E} (hf : AnalyticOn 𝕜 f t) (hst : s ⊆ t) : AnalyticOn 𝕜 f s := fun z hz => hf z (hst hz) #align analytic_on.mono AnalyticOn.mono theorem AnalyticOn.congr' {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 g s := fun z hz => (hf z hz).congr (mem_nhdsSet_iff_forall.mp hg z hz) theorem analyticOn_congr' {s : Set E} (h : f =ᶠ[𝓝ˢ s] g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr' h, fun hg => hg.congr' h.symm⟩ theorem AnalyticOn.congr {s : Set E} (hs : IsOpen s) (hf : AnalyticOn 𝕜 f s) (hg : s.EqOn f g) : AnalyticOn 𝕜 g s := hf.congr' $ mem_nhdsSet_iff_forall.mpr (fun _ hz => eventuallyEq_iff_exists_mem.mpr ⟨s, hs.mem_nhds hz, hg⟩) theorem analyticOn_congr {s : Set E} (hs : IsOpen s) (h : s.EqOn f g) : AnalyticOn 𝕜 f s ↔ AnalyticOn 𝕜 g s := ⟨fun hf => hf.congr hs h, fun hg => hg.congr hs h.symm⟩ theorem AnalyticOn.add {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f + g) s := fun z hz => (hf z hz).add (hg z hz) #align analytic_on.add AnalyticOn.add theorem AnalyticOn.sub {s : Set E} (hf : AnalyticOn 𝕜 f s) (hg : AnalyticOn 𝕜 g s) : AnalyticOn 𝕜 (f - g) s := fun z hz => (hf z hz).sub (hg z hz) #align analytic_on.sub AnalyticOn.sub theorem HasFPowerSeriesOnBall.coeff_zero (hf : HasFPowerSeriesOnBall f pf x r) (v : Fin 0 → E) : pf 0 v = f x := by have v_eq : v = fun i => 0 := Subsingleton.elim _ _ have zero_mem : (0 : E) ∈ EMetric.ball (0 : E) r := by simp [hf.r_pos] have : ∀ i, i ≠ 0 → (pf i fun j => 0) = 0 := by intro i hi have : 0 < i := pos_iff_ne_zero.2 hi exact ContinuousMultilinearMap.map_coord_zero _ (⟨0, this⟩ : Fin i) rfl have A := (hf.hasSum zero_mem).unique (hasSum_single _ this) simpa [v_eq] using A.symm #align has_fpower_series_on_ball.coeff_zero HasFPowerSeriesOnBall.coeff_zero theorem HasFPowerSeriesAt.coeff_zero (hf : HasFPowerSeriesAt f pf x) (v : Fin 0 → E) : pf 0 v = f x := let ⟨_, hrf⟩ := hf hrf.coeff_zero v #align has_fpower_series_at.coeff_zero HasFPowerSeriesAt.coeff_zero /-- If a function `f` has a power series `p` on a ball and `g` is linear, then `g ∘ f` has the power series `g ∘ p` on the same ball. -/ theorem ContinuousLinearMap.comp_hasFPowerSeriesOnBall (g : F →L[𝕜] G) (h : HasFPowerSeriesOnBall f p x r) : HasFPowerSeriesOnBall (g ∘ f) (g.compFormalMultilinearSeries p) x r := { r_le := h.r_le.trans (p.radius_le_radius_continuousLinearMap_comp _) r_pos := h.r_pos hasSum := fun hy => by simpa only [ContinuousLinearMap.compFormalMultilinearSeries_apply, ContinuousLinearMap.compContinuousMultilinearMap_coe, Function.comp_apply] using g.hasSum (h.hasSum hy) } #align continuous_linear_map.comp_has_fpower_series_on_ball ContinuousLinearMap.comp_hasFPowerSeriesOnBall /-- If a function `f` is analytic on a set `s` and `g` is linear, then `g ∘ f` is analytic on `s`. -/ theorem ContinuousLinearMap.comp_analyticOn {s : Set E} (g : F →L[𝕜] G) (h : AnalyticOn 𝕜 f s) : AnalyticOn 𝕜 (g ∘ f) s := by rintro x hx rcases h x hx with ⟨p, r, hp⟩ exact ⟨g.compFormalMultilinearSeries p, r, g.comp_hasFPowerSeriesOnBall hp⟩ #align continuous_linear_map.comp_analytic_on ContinuousLinearMap.comp_analyticOn /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. This version provides an upper estimate that decreases both in `‖y‖` and `n`. See also `HasFPowerSeriesOnBall.uniform_geometric_approx` for a weaker version. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx' {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n := p.norm_mul_pow_le_mul_pow_of_lt_radius (h.trans_le hf.r_le) refine' ⟨a, ha, C / (1 - a), div_pos hC (sub_pos.2 ha.2), fun y hy n => _⟩ have yr' : ‖y‖ < r' := by rw [ball_zero_eq] at hy exact hy have hr'0 : 0 < (r' : ℝ) := (norm_nonneg _).trans_lt yr' have : y ∈ EMetric.ball (0 : E) r := by refine' mem_emetric_ball_zero_iff.2 (lt_trans _ h) exact mod_cast yr' rw [norm_sub_rev, ← mul_div_right_comm] have ya : a * (‖y‖ / ↑r') ≤ a := mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg) suffices ‖p.partialSum n y - f (x + y)‖ ≤ C * (a * (‖y‖ / r')) ^ n / (1 - a * (‖y‖ / r')) by refine' this.trans _ have : 0 < a := ha.1 gcongr apply_rules [sub_pos.2, ha.2] apply norm_sub_le_of_geometric_bound_of_hasSum (ya.trans_lt ha.2) _ (hf.hasSum this) intro n calc ‖(p n) fun _ : Fin n => y‖ _ ≤ ‖p n‖ * ∏ _i : Fin n, ‖y‖ := ContinuousMultilinearMap.le_op_norm _ _ _ = ‖p n‖ * (r' : ℝ) ^ n * (‖y‖ / r') ^ n := by field_simp [mul_right_comm] _ ≤ C * a ^ n * (‖y‖ / r') ^ n := by gcongr ?_ * _; apply hp _ ≤ C * (a * (‖y‖ / r')) ^ n := by rw [mul_pow, mul_assoc] #align has_fpower_series_on_ball.uniform_geometric_approx' HasFPowerSeriesOnBall.uniform_geometric_approx' /-- If a function admits a power series expansion, then it is exponentially close to the partial sums of this power series on strict subdisks of the disk of convergence. -/ theorem HasFPowerSeriesOnBall.uniform_geometric_approx {r' : ℝ≥0} (hf : HasFPowerSeriesOnBall f p x r) (h : (r' : ℝ≥0∞) < r) : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * a ^ n := by obtain ⟨a, ha, C, hC, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' ⟨a, ha, C, hC, fun y hy n => (hp y hy n).trans _⟩ have yr' : ‖y‖ < r' := by rwa [ball_zero_eq] at hy gcongr exacts [mul_nonneg ha.1.le (div_nonneg (norm_nonneg y) r'.coe_nonneg), mul_le_of_le_one_right ha.1.le (div_le_one_of_le yr'.le r'.coe_nonneg)] #align has_fpower_series_on_ball.uniform_geometric_approx HasFPowerSeriesOnBall.uniform_geometric_approx /-- Taylor formula for an analytic function, `IsBigO` version. -/ theorem HasFPowerSeriesAt.isBigO_sub_partialSum_pow (hf : HasFPowerSeriesAt f p x) (n : ℕ) : (fun y : E => f (x + y) - p.partialSum n y) =O[𝓝 0] fun y => ‖y‖ ^ n := by rcases hf with ⟨r, hf⟩ rcases ENNReal.lt_iff_exists_nnreal_btwn.1 hf.r_pos with ⟨r', r'0, h⟩ obtain ⟨a, -, C, -, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ y ∈ Metric.ball (0 : E) r', ∀ n, ‖f (x + y) - p.partialSum n y‖ ≤ C * (a * (‖y‖ / r')) ^ n := hf.uniform_geometric_approx' h refine' isBigO_iff.2 ⟨C * (a / r') ^ n, _⟩ replace r'0 : 0 < (r' : ℝ); · exact mod_cast r'0 filter_upwards [Metric.ball_mem_nhds (0 : E) r'0] with y hy simpa [mul_pow, mul_div_assoc, mul_assoc, div_mul_eq_mul_div] using hp y hy n set_option linter.uppercaseLean3 false in #align has_fpower_series_at.is_O_sub_partial_sum_pow HasFPowerSeriesAt.isBigO_sub_partialSum_pow /-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr · apply hp · apply hy'.le _ = B n := by -- porting note: in the original, `B` was in the `field_simp`, but now Lean does not -- accept it. The current proof works in Lean 4, but does not in Lean 3.	field_simp [pow_succ]	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖ := by lift r' to ℝ≥0 using ne_top_of_lt hr rcases (zero_le r').eq_or_lt with (rfl \| hr'0) · simp only [isBigO_bot, EMetric.ball_zero, principal_empty, ENNReal.coe_zero] obtain ⟨a, ha, C, hC : 0 < C, hp⟩ : ∃ a ∈ Ioo (0 : ℝ) 1, ∃ C > 0, ∀ n : ℕ, ‖p n‖ * (r' : ℝ) ^ n ≤ C * a ^ n exact p.norm_mul_pow_le_mul_pow_of_lt_radius (hr.trans_le hf.r_le) simp only [← le_div_iff (pow_pos (NNReal.coe_pos.2 hr'0) _)] at hp set L : E × E → ℝ := fun y => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * (a / (1 - a) ^ 2 + 2 / (1 - a)) have hL : ∀ y ∈ EMetric.ball (x, x) r', ‖f y.1 - f y.2 - p 1 fun _ => y.1 - y.2‖ ≤ L y := by intro y hy' have hy : y ∈ EMetric.ball x r ×ˢ EMetric.ball x r := by rw [EMetric.ball_prod_same] exact EMetric.ball_subset_ball hr.le hy' set A : ℕ → F := fun n => (p n fun _ => y.1 - x) - p n fun _ => y.2 - x have hA : HasSum (fun n => A (n + 2)) (f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) := by convert (hasSum_nat_add_iff' 2).2 ((hf.hasSum_sub hy.1).sub (hf.hasSum_sub hy.2)) using 1 rw [Finset.sum_range_succ, Finset.sum_range_one, hf.coeff_zero, hf.coeff_zero, sub_self, zero_add, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.1 - x), Pi.single, ← Subsingleton.pi_single_eq (0 : Fin 1) (y.2 - x), Pi.single, ← (p 1).map_sub, ← Pi.single, Subsingleton.pi_single_eq, sub_sub_sub_cancel_right] rw [EMetric.mem_ball, edist_eq_coe_nnnorm_sub, ENNReal.coe_lt_coe] at hy' set B : ℕ → ℝ := fun n => C * (a / r') ^ 2 * (‖y - (x, x)‖ * ‖y.1 - y.2‖) * ((n + 2) * a ^ n) have hAB : ∀ n, ‖A (n + 2)‖ ≤ B n := fun n => calc ‖A (n + 2)‖ ≤ ‖p (n + 2)‖ * ↑(n + 2) * ‖y - (x, x)‖ ^ (n + 1) * ‖y.1 - y.2‖ := by -- porting note: `pi_norm_const` was `pi_norm_const (_ : E)` simpa only [Fintype.card_fin, pi_norm_const, Prod.norm_def, Pi.sub_def, Prod.fst_sub, Prod.snd_sub, sub_sub_sub_cancel_right] using (p <\| n + 2).norm_image_sub_le (fun _ => y.1 - x) fun _ => y.2 - x _ = ‖p (n + 2)‖ * ‖y - (x, x)‖ ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by rw [pow_succ ‖y - (x, x)‖] ring -- porting note: the two `↑` in `↑r'` are new, without them, Lean fails to synthesize -- instances `HDiv ℝ ℝ≥0 ?m` or `HMul ℝ ℝ≥0 ?m` _ ≤ C * a ^ (n + 2) / ↑r' ^ (n + 2) * ↑r' ^ n * (↑(n + 2) * ‖y - (x, x)‖ * ‖y.1 - y.2‖) := by have : 0 < a := ha.1 gcongr · apply hp · apply hy'.le _ = B n := by -- porting note: in the original, `B` was in the `field_simp`, but now Lean does not -- accept it. The current proof works in Lean 4, but does not in Lean 3.	Mathlib.Analysis.Analytic.Basic.729_0.jQw1fRSE1vGpOll	/-- If `f` has formal power series `∑ n, pₙ` on a ball of radius `r`, then for `y, z` in any smaller ball, the norm of the difference `f y - f z - p 1 (fun _ ↦ y - z)` is bounded above by `C * (max ‖y - x‖ ‖z - x‖) * ‖y - z‖`. This lemma formulates this property using `IsBigO` and `Filter.principal` on `E × E`. -/ theorem HasFPowerSeriesOnBall.isBigO_image_sub_image_sub_deriv_principal (hf : HasFPowerSeriesOnBall f p x r) (hr : r' < r) : (fun y : E × E => f y.1 - f y.2 - p 1 fun _ => y.1 - y.2) =O[𝓟 (EMetric.ball (x, x) r')] fun y => ‖y - (x, x)‖ * ‖y.1 - y.2‖	Mathlib_Analysis_Analytic_Basic

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