tylerjthomas9/JuliaCode · Datasets at Hugging Face

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[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	5485	# opt: x1 = 0.0, x2 = 1.0 # y1 = 1.2727272727272727, y2 = 0.36363636363636365, y3 = 0.0 # mlp = 3.909090909090909 # sub-opt: x1 = 1.0, x2 = 0.6666666666666666 # y1 = 0.8484848484848484, y2 = 0.5757575757575757, y3 = 0.0 # mlp = 5.9393939393939394 # infeasible: x1 = x2 = 0 function benders_form_D() #using JuMP, GLPK #m = Model(GLPK.Optimizer) #@variable(m, x[1:2] >= 0) #@variable(m, y[1:3] >= 0) #@constraint(m, x[1] + x[2] >= 1) ok #@constraint(m, 2x[1] - x[2] + 5y[1] - y[2] >= 5) ok #@constraint(m, x[1] + 3x[2] - 2y[3] >= 3) ok #@constraint(m, y[1] + 2y[2] + y[3] >= 2) ok #@objective(m, Min, 3x[1] + 1x[2] + 2y[1] + y[2] + y[3]) ok #optimize!(m) #@show objective_value(m) #@show value.(x) #@show value.(y) form = """ master min 3x1 + 1x2 + z s.t. x1 + x2 >= 1 benders_sp min 0x1 + 0x2 + 2y1 + y2 + y3 + art1 + art2 + art3 + z s.t. y1 + 2y2 + y3 + art1 >= 2 2x1 - x2 + 5y1 - y2 + art2 >= 5 {BendTechConstr} x1 + 3x2 - 2y3 + art3 >= 3 {BendTechConstr} integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2, y3 second_stage_artificial art1, art2, art3 bounds -Inf <= z <= Inf 0 <= x1 <= 1 1 <= x2 <= 1 1 <= y1 <= 2 0 <= y2 <= 2 0 <= y3 <= 2 0 <= art1 <= Inf 0 <= art2 <= Inf 0 <= art3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end # function test_benders_form_D() # env, reform = benders_form_D() # master = Coluna.MathProg.getmaster(reform) # master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty # ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) # sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty # ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # end # # alg = Coluna.Algorithm.BendersCutGeneration( # max_nb_iterations = 10, # restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() # ) # ctx = Coluna.Algorithm.BendersContext( # reform, alg; # ) # Coluna.set_optim_start_time!(env) # # result = Coluna.Benders.run_benders_loop!(ctx, env) # @test result.mlp ≈ 3.909090909090909 # #end #register!(unit_tests, "benders_default", test_benders_form_D) function get_name_to_constrids(form) d = Dict{String, ClMP.ConstrId}() for (constrid, constr) in ClMP.getconstrs(form) d[ClMP.getname(form, constr)] = constrid end return d end function get_name_to_varsids(form) d = Dict{String, ClMP.VarId}() for (varid, var) in ClMP.getvars(form) d[ClMP.getname(form, var)] = varid end return d end function test_benders_cut_lhs() _, reform = benders_form_D() master = Coluna.MathProg.getmaster(reform) sps = Coluna.MathProg.get_benders_sep_sps(reform) ##one sp, spid = 4 sp = sps[4] cids = get_name_to_constrids(sp) vids = get_name_to_varsids(sp) alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100, ) ctx = Coluna.Algorithm.BendersContext( reform, alg, ) dual_sol = Coluna.MathProg.DualSolution( master, [cids["sp_c1"], cids["sp_c2"], cids["sp_c3"]], [2.0, 4.0, 1.0], ##dumb dual sol Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) coeff_cut_lhs = Coluna.Algorithm._compute_cut_lhs(ctx, sp, dual_sol, false) ##opt cut @test coeff_cut_lhs[vids["x1"]] ≈ 9.0 @test coeff_cut_lhs[vids["x2"]] ≈ -1.0 @test coeff_cut_lhs[sp.duty_data.second_stage_cost_var] ≈ 1.0 ## η coeff_cut_lhs = Coluna.Algorithm._compute_cut_lhs(ctx, sp, dual_sol, true) ##feas cut @test coeff_cut_lhs[vids["x1"]] ≈ 9.0 @test coeff_cut_lhs[vids["x2"]] ≈ -1.0 @test coeff_cut_lhs[sp.duty_data.second_stage_cost_var] ≈ 0.0 ## η end register!(unit_tests, "benders_default", test_benders_cut_lhs) function test_benders_cut_rhs() _, reform = benders_form_D() master = Coluna.MathProg.getmaster(reform) sps = Coluna.MathProg.get_benders_sep_sps(reform) ##one sp, spid = 4 sp = sps[4] cids = get_name_to_constrids(sp) vids = get_name_to_varsids(sp) alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100, ) ctx = Coluna.Algorithm.BendersContext( reform, alg, ) dual_sol = Coluna.MathProg.DualSolution( master, [cids["sp_c1"], cids["sp_c2"], cids["sp_c3"]], [2.0, 4.0, 1.0], ##dumb dual sol Coluna.MathProg.VarId[vids["y1"], vids["x1"], vids["y2"], vids["x2"]], Float64[10.0, 5.0, 2.0, 3.0], Coluna.MathProg.ActiveBound[MathProg.LOWER, MathProg.UPPER, MathProg.UPPER, MathProg.LOWER], ## x2 fixed to 1.0 0.0, Coluna.MathProg.FEASIBLE_SOL ) coeff_cut_rhs = Coluna.Algorithm._compute_cut_rhs_contrib(ctx, sp, dual_sol) @test coeff_cut_rhs == 27.0 + (110.0 + 15.0 + 2*2.0 + 3.0) ## πr + bounding_constraints ##TODO: update when we know how to deal with equalities in the rhs computation end register!(unit_tests, "benders_default", test_benders_cut_rhs)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	38432	function _get_benders_var_ids(reform::Reformulation) varids = Dict{String,VarId}() master = Coluna.MathProg.getmaster(reform) for (varid, _) in Coluna.MathProg.getvars(master) varids[Coluna.MathProg.getname(master, varid)] = varid end for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) for (varid, _) in Coluna.MathProg.getvars(sp) varids[Coluna.MathProg.getname(sp, varid)] = varid end end return varids end function benders_form_A() # using JuMP, GLPK # m = Model(GLPK.Optimizer) # @variable(m, x[1:2]>= 0) # @variable(m, y[1:2] >= 0) # @constraint(m, -x[1] + 4x[2] + 2y[1] + 3y[2] >= 2) # @constraint(m, x[1] + 3x[2] + y[1] + y[2] >= 3) # @objective(m, Min, x[1] + 4x[2] + 2y[1] + 3y[2]) # optimize!(m) # objective_value(m) # value.(x) # value.(y) form = """ master min x1 + 4x2 + z s.t. x1 + x2 >= 0 benders_sp min 0x1 + 0x2 + 2y1 + 3y2 + z s.t. -x1 + 4x2 + 2y1 + 3y2 >= 2 {BendTechConstr} x1 + 3x2 + y1 + y2 >= 3 {BendTechConstr} y1 + y2 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_B() #using JuMP, GLPK #m = Model(GLPK.Optimizer) #@variable(m, x[1:2] >= 0) #@variable(m, y[1:2] >= 0) #@constraint(m, -x[1] + x[2] + y[1] - 0.5y[2] >= 4) #@constraint(m, 2x[1] + 1.5x[2] + y[1] + y[2] >= 5) #@objective(m, Min, x[1] + 2x[2] + 1.5y[1] + y[2]) #optimize!(m) #objective_value(m) #value.(x) #value.(y) form = """ master min x1 + 2x2 + z s.t. x1 + x2 >= 0 benders_sp min 0x1 + 0x2 + 1.5y1 + y2 + z s.t. -x1 + x2 + y1 - 0.5y2 >= 4 {BendTechConstr} 2x1 + 1.5x2 + y1 + y2 >= 5 {BendTechConstr} y1 + y2 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_C() #using JuMP, GLPK #m = Model(GLPK.Optimizer) #@variable(m, x[1:2] >= 0) #@variable(m, y[1:4] >= 0) #y1 y2 -> 1st sp, y3, y4 -> 2nd sp #@constraint(m, 2x[1] - x[2] + 0.5y[1] - y[2] >= 5) #@constraint(m, x[1] + 3x[2] - 1.5y[3] + y[4] >= 3) #@objective(m, Min, 6x[1] + x[2] + 1.5y[1] + y[2] + 1.5y[3] + 0.5y[4]) #optimize!(m) #objective_value(m) #value.(x) #value.(y) form = """ master min 6x1 + 1x2 + z1 + z2 s.t. x1 + x2 >= 0 benders_sp min 0x1 + 0x2 + 1.5y1 + y2 + z1 s.t. 2x1 - x2 + 0.5y1 - y2 >= 5 {BendTechConstr} y1 + y2 >= 0 benders_sp min 0x1 + 0x2 + 1.5y3 + 0.5y4 + z2 s.t. 1x1 + 3x2 - 1.5y3 + 1y4 >= 3 {BendTechConstr} y3 + y4 >= 0 integer first_stage x1, x2 continuous second_stage_cost z1, z2 second_stage y1, y2, y3, y4 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 y3 >= 0 y4 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_max() #using JuMP, GLPK #m = Model(GLPK.Optimizer) #@variable(m, x[1:2] >= 0) #@variable(m, y[1:2] >= 0) #@constraint(m, x[1] - x[2] - y[1] + 0.5y[2] <= -4) #@constraint(m, -2x[1] - 1.5x[2] - y[1] - y[2] <= -5) #@objective(m, Max, -x[1] - 2x[2] - 1.5y[1] - y[2]) #optimize!(m) #objective_value(m) #value.(x) #value.(y) form = """ master max -x1 - 2x2 + z s.t. x1 + x2 >= 0 benders_sp max 0x1 + 0x2 - 1.5y1 - y2 + z s.t. x1 - x2 - y1 + 0.5y2 <= -4 {BendTechConstr} -2x1 - 1.5x2 - y1 - y2 <= -5 {BendTechConstr} y1 + y2 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_infeasible_master() #A infeasible master #using JuMP, GLPK #m = Model(GLPK.Optimizer) #@variable(m, x[1:2] >= 0, Int) #@variable(m, y[1:2] >= 0) #@constraint(m, x[1] + x[2] <= -1) #@constraint(m, -x[1] + 4x[2] + 2y[1] + 3y[2] >= 2) #@constraint(m, x[1] + 3x[2] + y[1] + y[2] >= 3) #@objective(m, Min, x[1] + 4x[2] + 2y[1] + 3y[2]) #optimize!(m) #objective_value(m) #value.(x) #value.(y) form = """ master min x1 + 4x2 + z s.t. x1 + x2 >= 0 x1 + x2 <= -1 benders_sp min 0x1 + 0x2 + 2y1 + 3y2 + z s.t. -x1 + 4x2 + 2y1 + 3y2 >= 2 {BendTechConstr} x1 + 3x2 + y1 + y2 >= 3 {BendTechConstr} y1 + y2 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_infeasible_sp() #A infeasible subproblem # using JuMP, GLPK # m = Model(GLPK.Optimizer) # @variable(m, x[1:2]>= 0, Int) # @variable(m, y[1:2] >= 0) # @constraint(m, -x[1] + 4x[2] + 2y[1] + 3y[2] >= 2) # @constraint(m, x[1] + 3x[2] + y[1] + y[2] >= 3) # @constraint(m, 7x[2] + 3y[1] + 4y[2] <= 4) # @objective(m, Min, x[1] + 4x[2] + 2y[1] + 3y[2]) # optimize!(m) # objective_value(m) # value.(x) # value.(y) form = """ master min x1 + 4x2 + z s.t. x1 + x2 >= 0 benders_sp min 0x1 + 0x2 + 2y1 + 3y2 + a1 + a2 + a3 + a4 + z s.t. -x1 + 4x2 + 2y1 + 3y2 + a1 >= 2 {BendTechConstr} x1 + 3x2 + y1 + y2 + a2 >= 3 {BendTechConstr} 7x2 + 3y1 + 4y2 - a3 <= 4 {BendTechConstr} y1 + y2 + a4 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 second_stage_artificial a1, a2, a3, a4 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 a1 >= 0 a2 >= 0 a3 >= 0 a4 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_lower_bound() #A with high lower bound on y #using JuMP, GLPK #m = Model(GLPK.Optimizer) #@variable(m, x[1:2]>= 0) #@variable(m, y[1:2] >= 5) #@constraint(m, -x[1] + 4x[2] + 2y[1] + 3y[2] >= 2) #@constraint(m, x[1] + 3x[2] + y[1] + y[2] >= 3) #@objective(m, Min, x[1] + 4x[2] + 2y[1] + 3y[2]) #optimize!(m) #objective_value(m) #value.(x) #value.(y) form = """ master min x1 + 4x2 + z s.t. x1 + x2 >= 0 benders_sp min 0x1 + 0x2 + 2y1 + 3y2 + z s.t. -x1 + 4x2 + 2y1 + 3y2 >= 2 {BendTechConstr} x1 + 3x2 + y1 + y2 >= 3 {BendTechConstr} y1 + y2 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 5 y2 >= 5 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_upper_bound() # using JuMP, GLPK # m = Model(GLPK.Optimizer) # @variable(m, x[1:2] >= 0) # @variable(m, 1 >= y[1:2] >= 0) # @constraint(m, x[1] - x[2] - y[1] + 0.5y[2] <= -4) # @constraint(m, -2x[1] - 1.5x[2] - y[1] - y[2] <= -5) # @objective(m, Max, -x[1] - 2x[2] - 1.5y[1] - y[2]) # optimize!(m) # objective_value(m) # value.(x) # value.(y) form = """ master max -x1 - 2x2 - 1.5y1 - y2 + z s.t. x1 + x2 >= 0 benders_sp max 0x1 + 0x2 - 1.5y1 - y2 + z - a1 - a2 - a3 s.t. x1 - x2 - y1 + 0.5y2 - a1 <= -4 {BendTechConstr} -2x1 - 1.5x2 - y1 - y2 - a2 <= -5 {BendTechConstr} y1 + y2 + a3 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 second_stage_artificial a1, a2, a3 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 1 >= y1 >= 0 1 >= y2 >= 0 a1 >= 0 a2 >= 0 a3 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_unbounded_master() form = """ master min -1x1 + 4x2 + z s.t. x1 + x2 >= 0 benders_sp min 0x1 + 0x2 + 2y1 + 3y2 + z s.t. x1 + 4x2 + 2y1 + 3y2 >= 2 {BendTechConstr} x1 + 3x2 + y1 + y2 >= 3 {BendTechConstr} y1 + y2 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end function benders_form_unbounded_sp() form = """ master min x1 + 4x2 + z s.t. x1 + x2 >= 0 benders_sp min 0x1 + 0x2 - 2y1 + 3y2 + z s.t. -x1 + 4x2 + 2y1 + 3y2 >= 2 {BendTechConstr} x1 + 3x2 + y1 + y2 >= 3 {BendTechConstr} y1 + y2 >= 0 integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2 bounds -Inf <= z <= Inf x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env, _, _, _, reform = reformfromstring(form) return env, reform, _get_benders_var_ids(reform) end # A with continuous first stage finds optimal solution function benders_iter_default_A_continuous() #env, reform = benders_simple_example() env, reform, varids = benders_form_A() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 3.7142857142857144 @test result.ip_primal_sol[varids["x1"]] ≈ 0.8571428571428571 @test result.ip_primal_sol[varids["x2"]] ≈ 0.7142857142857143 @test result.ip_primal_sol[varids["y1"]] ≈ 0.0 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 end register!(unit_tests, "benders_default", benders_iter_default_A_continuous) # A with integer first stage finds optimal solution function benders_iter_default_A_integer() #env, reform = benders_simple_example() env, reform, varids = benders_form_A() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 4.0 @test result.ip_primal_sol[varids["x1"]] ≈ 0.0 @test result.ip_primal_sol[varids["x2"]] ≈ 1.0 @test result.ip_primal_sol[varids["y1"]] ≈ 0.0 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 end register!(unit_tests, "benders_default", benders_iter_default_A_integer) # B with continuous first stage finds optimal solution function benders_iter_default_B_continuous() #env, reform = benders_simple_example() env, reform, varids = benders_form_B() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 6.833333333333333 @test result.ip_primal_sol[varids["x1"]] ≈ 0.33333333333333337 @test result.ip_primal_sol[varids["x2"]] ≈ 0.0 @test result.ip_primal_sol[varids["y1"]] ≈ 4.333333333333333 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 end register!(unit_tests, "benders_default", benders_iter_default_B_continuous) # B with integer first stage finds optimal solution function benders_iter_default_B_integer() #env, reform = benders_simple_example() env, reform, varids = benders_form_B() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 7 @test result.ip_primal_sol[varids["x1"]] ≈ 0.0 @test result.ip_primal_sol[varids["x2"]] ≈ 2.0 @test result.ip_primal_sol[varids["y1"]] ≈ 2.0 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 end register!(unit_tests, "benders_default", benders_iter_default_B_integer) # C with continuous first stage finds optimal solution function benders_sp_C_continuous() env, reform, varids = benders_form_C() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 20 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 15.25 @test result.ip_primal_sol[varids["x1"]] ≈ 2.5 @test result.ip_primal_sol[varids["x2"]] ≈ 0.0 @test result.ip_primal_sol[varids["y1"]] ≈ 0.0 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 @test result.ip_primal_sol[varids["y3"]] ≈ 0.0 @test result.ip_primal_sol[varids["y4"]] ≈ 0.5 end register!(unit_tests, "benders_default", benders_sp_C_continuous) # C with integer first stage finds optimal solution function benders_sp_C_integer() env, reform, varids = benders_form_C() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 15.5 @test result.ip_primal_sol[varids["x1"]] ≈ 2.0 @test result.ip_primal_sol[varids["x2"]] ≈ 0.0 @test result.ip_primal_sol[varids["y1"]] ≈ 2.0 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 @test result.ip_primal_sol[varids["y3"]] ≈ 0.0 @test result.ip_primal_sol[varids["y4"]] ≈ 1.0 end register!(unit_tests, "benders_default", benders_sp_C_integer) function benders_default_max_form_continuous() env, reform, varids = benders_form_max() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ -6.833333333333333 @test result.ip_primal_sol[varids["x1"]] ≈ 0.33333333333333337 @test result.ip_primal_sol[varids["x2"]] ≈ 0.0 @test result.ip_primal_sol[varids["y1"]] ≈ 4.333333333333333 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 end register!(unit_tests, "benders_default", benders_default_max_form_continuous) function benders_default_max_form_integer() env, reform, varids = benders_form_max() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ -7 @test result.ip_primal_sol[varids["x1"]] ≈ 0.0 @test result.ip_primal_sol[varids["x2"]] ≈ 2.0 @test result.ip_primal_sol[varids["y1"]] ≈ 2.0000000000000004 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 end register!(unit_tests, "benders_default", benders_default_max_form_integer) # A formulation with infeasible master constraint function benders_default_infeasible_master() env, reform, _ = benders_form_infeasible_master() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.infeasible == true end register!(unit_tests, "benders_default", benders_default_infeasible_master) # A formulation with infeasible master constraint function benders_default_infeasible_master_integer() env, reform, _ = benders_form_infeasible_master() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.infeasible == true end register!(unit_tests, "benders_default", benders_default_infeasible_master_integer) # A formulation with infeasible sp constraint function benders_default_infeasible_sp() env, reform, _ = benders_form_infeasible_sp() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.infeasible == true end register!(unit_tests, "benders_default", benders_default_infeasible_sp) # A formulation with infeasible sp constraint function benders_default_infeasible_sp_integer() env, reform, _ = benders_form_infeasible_sp() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.infeasible == true end register!(unit_tests, "benders_default", benders_default_infeasible_sp_integer) # form A with lower bound on y variables equal to 5 function benders_min_lower_bound() env, reform, varids = benders_form_lower_bound() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 25 @test result.ip_primal_sol[varids["x1"]] ≈ 0.0 @test result.ip_primal_sol[varids["x2"]] ≈ 0.0 @test result.ip_primal_sol[varids["y1"]] ≈ 5.0 @test result.ip_primal_sol[varids["y2"]] ≈ 5.0 end register!(unit_tests, "benders_default", benders_min_lower_bound) # max form B with upper bound on y variables equal to 1 function benders_max_upper_bound() env, reform, varids = benders_form_upper_bound() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ -7.5 @test result.ip_primal_sol[varids["x1"]] ≈ 0.0 @test result.ip_primal_sol[varids["x2"]] ≈ 3.0 @test result.ip_primal_sol[varids["y1"]] ≈ 1.0 @test result.ip_primal_sol[varids["y2"]] ≈ 0.0 end register!(unit_tests, "benders_default", benders_max_upper_bound) # benders throws error function benders_default_unbounded_master() env, reform, _ = benders_form_unbounded_master() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersPrinterContext(reform, alg; print = false ) Coluna.set_optim_start_time!(env) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_loop!(ctx, env) end register!(unit_tests, "benders_default", benders_default_unbounded_master) # benders throws error function benders_default_unbounded_master_integer() env, reform, _ = benders_form_unbounded_master() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersPrinterContext(reform, alg; print = false ) Coluna.set_optim_start_time!(env) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_loop!(ctx, env) end register!(unit_tests, "benders_default", benders_default_unbounded_master_integer) # benders throws error function benders_default_unbounded_sp() env, reform, _ = benders_form_unbounded_sp() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = Coluna.Algorithm.BendersPrinterContext(reform, alg; print = false) Coluna.set_optim_start_time!(env) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_loop!(ctx, env) end register!(unit_tests, "benders_default", benders_default_unbounded_sp) # benders throws error function benders_default_unbounded_sp_integer() env, reform, _ = benders_form_unbounded_sp() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersPrinterContext(reform, alg; print = false) Coluna.set_optim_start_time!(env) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_loop!(ctx, env) end register!(unit_tests, "benders_default", benders_default_unbounded_sp_integer) function benders_default_loc_routing_continuous() env, reform = benders_form_location_routing() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100 ) ctx = Coluna.Algorithm.BendersPrinterContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 293.5 end register!(unit_tests, "benders_default", benders_default_loc_routing_continuous) function benders_default_loc_routing() env, reform = benders_form_location_routing() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersPrinterContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 385.0 end register!(unit_tests, "benders_default", benders_default_loc_routing) function benders_default_loc_routing_infeasible_continuous() env, reform = benders_form_location_routing_infeasible() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100 ) ctx = Coluna.Algorithm.BendersPrinterContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.infeasible == true end register!(unit_tests, "benders_default", benders_default_loc_routing_infeasible_continuous) function benders_default_loc_routing_infeasible() env, reform = benders_form_location_routing_infeasible() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersPrinterContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.infeasible == true end register!(unit_tests, "benders_default", benders_default_loc_routing_infeasible) function benders_default_location_routing_subopt_continuous() env, reform = benders_form_location_routing_subopt() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) ClMP.relax_integrality!(master) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100 ) ctx = Coluna.Algorithm.BendersPrinterContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 386.0 end register!(unit_tests, "benders_default", benders_default_location_routing_subopt_continuous) function benders_default_location_routing_subopt() env, reform = benders_form_location_routing_subopt() master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100, restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() ) ctx = Coluna.Algorithm.BendersPrinterContext( reform, alg; ) Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_loop!(ctx, env) @test result.mlp ≈ 517.0 end register!(unit_tests, "benders_default", benders_default_location_routing_subopt) function test_two_identicals_cut_at_two_iterations_failure() env, reform = benders_form_A() master = ClMP.getmaster(reform) sps = ClMP.get_benders_sep_sps(reform) spform = sps[4] spconstrids = Dict(CL.getname(spform, constr) => constrid for (constrid, constr) in CL.getconstrs(spform)) spvarids = Dict(CL.getname(spform, var) => varid for (varid, var) in CL.getvars(spform)) alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 2 ) ctx = Coluna.Algorithm.BendersContext( reform, alg; ) cut1 = ClMP.DualSolution( spform, map(x -> spconstrids[x], ["sp_c1", "sp_c2", "sp_c3"]), [1.5, 2.0, 4.0], map(x -> spvarids[x], ["y1", "y2"]), [1.0, 1.0], [ClMP.LOWER, ClMP.UPPER], 1.0, ClB.FEASIBLE_SOL ) lhs1 = Dict{ClMP.VarId, Float64}() rhs1 = 1.0 cut2 = ClMP.DualSolution( spform, map(x -> spconstrids[x], ["sp_c1", "sp_c2", "sp_c3"]), [1.5, 2.0, 4.0], map(x -> spvarids[x], ["y1", "y2"]), [1.0, 1.0], [ClMP.LOWER, ClMP.UPPER], 1.0, ClB.FEASIBLE_SOL ) lhs2 = Dict{ClMP.VarId, Float64}() rhs2 = 1.5 cuts = Coluna.Benders.set_of_cuts(ctx) for (sol, lhs, rhs) in Iterators.zip([cut1, cut2], [lhs1, lhs2], [rhs1, rhs2]) cut = ClA.GeneratedCut(true, lhs, rhs, sol) sep_res = ClA.BendersSeparationResult(2.0, 3.0, nothing, false, false, cut, false, false) Coluna.Benders.push_in_set!(ctx, cuts, sep_res) end Coluna.Benders.insert_cuts!(reform, ctx, cuts) @test_throws Coluna.Algorithm.CutAlreadyInsertedBendersWarning Coluna.Benders.insert_cuts!(reform, ctx, cuts) # Coluna.set_optim_start_time!(env) # result = Coluna.Benders.run_benders_loop!(ctx, env) # @test result.mlp ≈ 3.7142857142857144 end register!(unit_tests, "benders_default", test_two_identicals_cut_at_two_iterations_failure)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	9026	#################### tests with flags #################### struct TestBendersMaster end struct TestBendersSubproblem end mutable struct TestBendersMasterRes ## mock of a master opt. result infeasible_master::Bool unbounded_master::Bool is_certificate::Bool end function Coluna.Benders.is_unbounded(master_res::TestBendersMasterRes) return master_res.unbounded_master end function Coluna.Benders.is_infeasible(master_res::TestBendersMasterRes) return master_res.infeasible_master end function Coluna.Benders.is_certificate(master_res::TestBendersMasterRes) return master_res.is_certificate end function Coluna.Benders.get_primal_sol(master_res::TestBendersMasterRes) return nothing end function Coluna.Benders.get_obj_val(sep_res::TestBendersMasterRes) return 0.0 end struct TestBendersSepRes ## mock of a sep. problem opt. result infeasible_sp::Bool unbounded_sp::Bool end function Coluna.Benders.is_infeasible(sep_res::TestBendersSepRes) return sep_res.infeasible_sp end function Coluna.Benders.is_unbounded(sep_res::TestBendersSepRes) return sep_res.unbounded_sp end function Coluna.Benders.get_obj_val(sep_res::TestBendersSepRes) return 0.0 end mutable struct TestBendersFlowFlagContext <: Coluna.Benders.AbstractBendersContext master_opt_res::TestBendersMasterRes sp_opt_res::TestBendersSepRes flag_unbounded_master::Bool ## check we enter treat_unbounded_master_problem_case! flag_unbounded_master_sp::Bool ## check we enter setup_separation_for_unbounded_master_case! flag_infeasible_sp::Bool ## check we enter treat_infeasible_separation_problem_case! end function Coluna.Benders.get_master(ctx::TestBendersFlowFlagContext) return TestBendersMaster() end function Coluna.Benders.get_reform(ctx::TestBendersFlowFlagContext) return nothing end function Coluna.Benders.is_minimization(ctx::TestBendersFlowFlagContext) return true end function Coluna.Benders.benders_iteration_output_type(ctx::TestBendersFlowFlagContext) return Coluna.Algorithm.BendersIterationOutput end function Coluna.Benders.optimize_master_problem!(master, ctx::TestBendersFlowFlagContext, env) return ctx.master_opt_res end function Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx::TestBendersFlowFlagContext, env) ctx.flag_unbounded_master = true ctx.master_opt_res.unbounded_master = false return ctx.master_opt_res end function Coluna.Benders.get_benders_subprobs(ctx::TestBendersFlowFlagContext) return [(1, TestBendersSubproblem())] end function Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx::TestBendersFlowFlagContext, sp, mast_primal_sol) ctx.flag_unbounded_master_sp = true return end function Coluna.Benders.update_sp_rhs!(ctx::TestBendersFlowFlagContext, sp, mast_primal_sol) return end function Coluna.Benders.optimize_separation_problem!(ctx::TestBendersFlowFlagContext, sp, env, unbounded_master) return ctx.sp_opt_res end function Coluna.Benders.treat_infeasible_separation_problem_case!(ctx::TestBendersFlowFlagContext, sp, env, unbounded_master_case) ctx.flag_infeasible_sp = true return ctx.sp_opt_res end function Coluna.Benders.master_is_unbounded(ctx::TestBendersFlowFlagContext, second_stage_cost, unbounded_master_case) return ctx.master_opt_res.unbounded_master ## TODO check end function Coluna.Benders.insert_cuts!(reform, ctx::TestBendersFlowFlagContext, cuts) return [] end function Coluna.Benders.build_primal_solution(ctx::TestBendersFlowFlagContext, mast_primal_sol, sep_sp_sols) return end function Coluna.Benders.set_of_cuts(ctx::TestBendersFlowFlagContext) return [] end function Coluna.Benders.set_of_sep_sols(ctx::TestBendersFlowFlagContext) return [] end function Coluna.Benders.push_in_set!(ctx::TestBendersFlowFlagContext, set, elem) return false end function benders_flow_infeasible_master() ctx = TestBendersFlowFlagContext( ## bounded master TestBendersMasterRes( true, false, false ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test res.infeasible == true @test ctx.flag_unbounded_master == false @test ctx.flag_unbounded_master_sp == false @test ctx.flag_infeasible_sp == false ctx = TestBendersFlowFlagContext( ## unbounded master with certificate = true to ensure we stop before entering setup_separation_for_unbounded_master_case! TestBendersMasterRes( true, true, true ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test res.infeasible == true @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == false @test ctx.flag_infeasible_sp == false end register!(unit_tests, "benders_default", benders_flow_infeasible_master) function benders_flow_unbounded_master() ctx = TestBendersFlowFlagContext( TestBendersMasterRes( false, true, false ## with certificate = false ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == false @test ctx.flag_infeasible_sp == false ctx = TestBendersFlowFlagContext( TestBendersMasterRes( false, true, true ## with certificate = true ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == true @test ctx.flag_infeasible_sp == false end register!(unit_tests, "benders_default", benders_flow_unbounded_master) function benders_flow_infeasible_sp() ctx = TestBendersFlowFlagContext( ## bounded sp, bounded master TestBendersMasterRes( false, false, false ), TestBendersSepRes( true, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true @test ctx.flag_unbounded_master == false @test ctx.flag_unbounded_master_sp == false ctx = TestBendersFlowFlagContext( ## bounded sp, unbounded master TestBendersMasterRes( false, true, false ), TestBendersSepRes( true, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == false ctx = TestBendersFlowFlagContext( ## bounded sp, unbounded master with certificate TestBendersMasterRes( false, true, true ), TestBendersSepRes( true, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == true end register!(unit_tests, "benders_default", benders_flow_infeasible_sp) ## test unbounded sp flow when sp is either feasible or infeasible -> in both cases an error should be thrown function benders_flow_unbounded_sp() ctx = TestBendersFlowFlagContext( ## feasible unbounded sp TestBendersMasterRes( false, false, false ), TestBendersSepRes( false, true ), false, false, false ) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) ctx = TestBendersFlowFlagContext( ## infeasible unbounded sp TestBendersMasterRes( false, false, false ), TestBendersSepRes( true, true ), false, false, false ) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true end register!(unit_tests, "benders_default", benders_flow_unbounded_sp)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	6485	#################### tests with formulations #################### mutable struct TestBendersFlowFormContext <: Coluna.Benders.AbstractBendersContext context::ClA.BendersContext flag_infeasible_master::Bool flag_unbounded_master::Bool##flag to check that we enter treat_unbounded_master_problem_case! flag_infeasible_sp::Bool ## flag to check that we enter treat_infeasible_separation_problem_case end Coluna.Benders.get_master(ctx::TestBendersFlowFormContext) = Coluna.Benders.get_master(ctx.context) Coluna.Benders.get_reform(ctx::TestBendersFlowFormContext) = Coluna.Benders.get_reform(ctx.context) Coluna.Benders.is_minimization(ctx::TestBendersFlowFormContext) = Coluna.Benders.is_minimization(ctx.context) Coluna.Benders.get_benders_subprobs(ctx::TestBendersFlowFormContext) = Coluna.Benders.get_benders_subprobs(ctx.context) Coluna.Benders.optimize_master_problem!(master, ctx::TestBendersFlowFormContext, env) = Coluna.Benders.optimize_master_problem!(master, ctx.context, env) function Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx::TestBendersFlowFormContext, env) output = Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx.context, env) ctx.flag_unbounded_master = true return output end Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx::TestBendersFlowFormContext, sp, mast_primal_sol) = Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx.context, sp, mast_primal_sol) Coluna.Benders.optimize_separation_problem!(ctx::TestBendersFlowFormContext, sp::Formulation{BendersSp}, env, unbounded_master) = Coluna.Benders.optimize_separation_problem!(ctx.context, sp, env, unbounded_master) Coluna.Benders.master_is_unbounded(ctx::TestBendersFlowFormContext, second_stage_cost, unbounded_master_case) = Coluna.Benders.master_is_unbounded(ctx.context, second_stage_cost, unbounded_master_case) function Coluna.Benders.treat_infeasible_separation_problem_case!(ctx::TestBendersFlowFormContext, sp::Formulation{BendersSp}, env, unbounded_master_case) output = Coluna.Benders.treat_infeasible_separation_problem_case!(ctx.context, sp, env, unbounded_master_case) ctx.flag_infeasible_sp = true return output end Coluna.Benders.push_in_set!(ctx::TestBendersFlowFormContext, set::Coluna.Algorithm.CutsSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.push_in_set!(ctx::TestBendersFlowFormContext, set::Coluna.Algorithm.SepSolSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.insert_cuts!(reform, ctx::TestBendersFlowFormContext, cuts) = Coluna.Benders.insert_cuts!(reform, ctx.context, cuts) Coluna.Benders.build_primal_solution(ctx::TestBendersFlowFormContext, mast_primal_sol, sep_sp_sols) = Coluna.Benders.build_primal_solution(ctx.context, mast_primal_sol, sep_sp_sols) Coluna.Benders.benders_iteration_output_type(ctx::TestBendersFlowFormContext) = Coluna.Benders.benders_iteration_output_type(ctx.context) Coluna.Benders.update_sp_rhs!(ctx::TestBendersFlowFormContext, sp, mast_primal_sol) = Coluna.Benders.update_sp_rhs!(ctx.context, sp, mast_primal_sol) Coluna.Benders.set_of_cuts(ctx::TestBendersFlowFormContext) = Coluna.Benders.set_of_cuts(ctx.context) Coluna.Benders.set_of_sep_sols(ctx::TestBendersFlowFormContext) = Coluna.Benders.set_of_sep_sols(ctx.context) function benders_flow_form_unbounded_master() env, reform, _ = benders_form_unbounded_master() alg = Coluna.Algorithm.BendersCutGeneration() ctx = TestBendersFlowFormContext( Coluna.Algorithm.BendersContext( reform, alg ), false, false, false ) master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_iteration!(ctx, nothing, env, nothing) @test ctx.flag_unbounded_master == true end register!(unit_tests, "benders_default", benders_flow_form_unbounded_master) ## x2 fixed to zero ## z cost fixed to dumb value function benders_infeasible_sp() form = """ master min x1 + 4x2 + z s.t. x1 + x2 >= 1 benders_sp min 0x1 + 0x2 + 2y1 + 3y2 + y3 + art1 + art2 + z s.t. x1 + x2 + y1 + 2y3 + art1 >= 1 {BendTechConstr} x2 + y2 + art2 >= 2 {BendTechConstr} integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2, y3 second_stage_artificial art1, art2 bounds 10 <= z <= 10 0 <= x1 <= 1 0 <= x2 <= 0 0 <= y1 <= 1 0 <= y2 <= 1 0 <= y3 <= 1 0 <= art1 <= Inf 0 <= art2 <= Inf 0 <= art3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_flow_form_infeasible_sp() env, reform = benders_infeasible_sp() alg = Coluna.Algorithm.BendersCutGeneration() master = ClMP.getmaster(reform) sps = ClMP.get_benders_sep_sps(reform) #@show master #for sp in sps # @show sp #end ctx = TestBendersFlowFormContext( Coluna.Algorithm.BendersContext( reform, alg ), false, false, false ) master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) Coluna.Algorithm._deactivate_art_vars(sp) end Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_iteration!(ctx, nothing, env, nothing) @test ctx.flag_infeasible_sp == true end register!(unit_tests, "benders_default", benders_flow_form_infeasible_sp)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	6004	## original MIP: ## min cx + dy s.t. ## Ax >= b ## Tx + Qy >= r ## x, y >= 0, x ∈ Z^n ## master: ## min cx + η ## Ax >= B ## < benders cuts > ## SP: ## min dy ## Tx* + Qy >= r ## y >= 0 ## π: dual sol ## η: contribution to the objective of the second-level variables ## feasibility cut: πTx >= πr ## optimality cut: η + πTx >= πr struct TestBendersIterationContext <: Coluna.Benders.AbstractBendersContext context::ClA.BendersContext master ##Formulation{Benders...} sps ##Dict{Int16, Coluna.ColunaBase.AbstractModel} first_stage_sol::Dict{String, Float64} ## id of variable, value second_stage_sols::Dict{String, Float64} ##id of variable, value end Coluna.Benders.get_master(ctx::TestBendersIterationContext) = Coluna.Benders.get_master(ctx.context) Coluna.Benders.get_reform(ctx::TestBendersIterationContext) = Coluna.Benders.get_reform(ctx.context) Coluna.Benders.is_minimization(ctx::TestBendersIterationContext) = Coluna.Benders.is_minimization(ctx.context) Coluna.Benders.get_benders_subprobs(ctx::TestBendersIterationContext) = Coluna.Benders.get_benders_subprobs(ctx.context) ## where to check stop condition ? ## re-def if need to check something Coluna.Benders.optimize_master_problem!(master, ctx::TestBendersIterationContext, env) = Coluna.Benders.optimize_master_problem!(master, ctx.context, env) Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx::TestBendersIterationContext, env) = Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx.context, env) Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx::TestBendersIterationContext, sp, mast_primal_sol) = Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx.context, sp, mast_primal_sol) ## TODO: redef to check cuts Coluna.Benders.optimize_separation_problem!(ctx::TestBendersIterationContext, sp::Formulation{BendersSp}, env, unbounded_master) = Coluna.Benders.optimize_separation_problem!(ctx.context, sp, env, unbounded_master) Coluna.Benders.master_is_unbounded(ctx::TestBendersIterationContext, second_stage_cost, unbounded_master_case) = Coluna.Benders.master_is_unbounded(ctx.context, second_stage_cost, unbounded_master_case) ## same Coluna.Benders.treat_infeasible_separation_problem_case!(ctx::TestBendersIterationContext, sp::Formulation{BendersSp}, env, unbounded_master_case) = Coluna.Benders.treat_infeasible_separation_problem_case!(ctx.context, sp, env, unbounded_master_case) Coluna.Benders.push_in_set!(ctx::TestBendersIterationContext, set::Coluna.Algorithm.CutsSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.push_in_set!(ctx::TestBendersIterationContext, set::Coluna.Algorithm.SepSolSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.insert_cuts!(reform, ctx::TestBendersIterationContext, cuts) = Coluna.Benders.insert_cuts!(reform, ctx.context, cuts) function Coluna.Benders.build_primal_solution(ctx::TestBendersIterationContext, mast_primal_sol, sep_sp_sols) output = Coluna.Benders.build_primal_solution(ctx.context, mast_primal_sol, sep_sp_sols) for (varid, val) in mast_primal_sol name = getname(ctx.master, varid) if haskey(ctx.first_stage_sol, name) @test ctx.first_stage_sol[name] ≈ val else @test 0.0 <= val <= 1.0e-4 end end for (_, sp) in ctx.sps for sp_sol in sep_sp_sols.sols for (varid, val) in sp_sol name = getname(sp, varid) if haskey(ctx.second_stage_sols, name) @test ctx.second_stage_sols[name] ≈ val else @test 0.0 <= val <= 1.0e-4 end end end end return output end Coluna.Benders.benders_iteration_output_type(ctx::TestBendersIterationContext) = Coluna.Benders.benders_iteration_output_type(ctx.context) Coluna.Benders.update_sp_rhs!(ctx::TestBendersIterationContext, sp, mast_primal_sol) = Coluna.Benders.update_sp_rhs!(ctx.context, sp, mast_primal_sol) Coluna.Benders.set_of_cuts(ctx::TestBendersIterationContext) = Coluna.Benders.set_of_cuts(ctx.context) Coluna.Benders.set_of_sep_sols(ctx::TestBendersIterationContext) = Coluna.Benders.set_of_sep_sols(ctx.context) ## checks cuts ## stop criterion because of opt. sol found is matched function benders_iter_opt_stop() env, reform = benders_form_location_routing_fixed_opt_continuous() master = ClMP.getmaster(reform) sps = ClMP.get_benders_sep_sps(reform) ClMP.relax_integrality!(master) ## sol fully fixed ##expected sol first_stage_sol = Dict( "y1" => 0.5, "y2" => 0.0, "y3" => 0.3333, "z" => 175.16666666666666 ) second_stage_sols = Dict( "x11" => 0.5, "x12" => 0.5, "x13" => 0.49999, "x14" => 0.5, "x31" => 0.33333, "x32" => 0.33333, "x33" => 0.16666, "x34" => 0.33333 ) alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = TestBendersIterationContext( Coluna.Algorithm.BendersContext( reform, alg ), master, sps, first_stage_sol, second_stage_sols ) master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_iteration!(ctx, nothing, env, nothing) @test result.master ≈ 293.4956666 end register!(unit_tests, "benders_default", benders_iter_opt_stop)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	18933	## optimal solution is found with 1st level variables y equals to: ## y1 = 1.0, y2 = 0.0, y3 = 1.0 ## with 2nd level variables: ## x12 = x14 = x32 = x33 = 1.0 ## mlp = 385.0 ## a sub-optimal solution can be found with 1st level variables: ## y1 = 1.0, y2 = 1.0, y3 = 0.0 (fix y3 to zero) ## with 2nd level variables: ## x12 = x22 = x23 = 1.0 ## mlp = 517.0 ## if y1 is fixed to zero, the problem is infeasible ## [Relaxation] ## optimal solution is found with 1st level variables y equals to: ## y1 = 0.5, y2 = 0.0, y3 = 0.33333 ## with 2nd level variables: ## x11 = 0.5, x12 = 0.5, x13 = 0.49999, x14 = 0.5, x31 = 1/3, x32 = 1/3, x33 = 0.16666, x34 = 1/3 ## mlp = 293.5 ## a sub-optimal solution can be found with 1st level variables: ## y1 = 0.5, y2 = 0.5, y3 = 0.0 (fix y3 to zero) ## with 2nd level variables: ## x11 = 0.5, x12 = 0.5, x13 = 0.5, x21 = 0.5, x22 = 0.5, x23 = 0.5 ## mlp = 386.00 ## if y1 is fixed to zero, the problem is infeasible function benders_form_location_routing() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 integer first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds -Inf <= z <= Inf 0 <= x11 <= 1 0 <= x12 <= 1 0 <= x13 <= 1 0 <= x14 <= 1 0 <= x21 <= 1 0 <= x22 <= 1 0 <= x23 <= 1 0 <= x24 <= 1 0 <= x31 <= 1 0 <= x32 <= 1 0 <= x33 <= 1 0 <= x34 <= 1 0 <= y1 <= 1 0 <= y2 <= 1 0 <= y3 <= 1 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_form_location_routing_fixed_opt_continuous() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 continuous first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds 175.16666666666666 <= z <= 175.16666666666666 0.5 <= x11 <= 0.5 0.5 <= x12 <= 0.5 0.49999 <= x13 <= 0.49999 0.5 <= x14 <= 0.5 0 <= x21 <= 0 0 <= x22 <= 0 0 <= x23 <= 0 0 <= x24 <= 0 0.33333 <= x31 <= 0.33333 0.33333 <= x32 <= 0.33333 0.16666 <= x33 <= 0.16666 0.33333 <= x34 <= 0.33333 0.5 <= y1 <= 0.5 0.0 <= y2 <= 0.0 0.3333 <= y3 <= 0.3333 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_form_location_routing_infeasible() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 integer first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds -Inf <= z <= Inf 0 <= x11 <= 1 0 <= x12 <= 1 0 <= x13 <= 1 0 <= x14 <= 1 0 <= x21 <= 1 0 <= x22 <= 1 0 <= x23 <= 1 0 <= x24 <= 1 0 <= x31 <= 1 0 <= x32 <= 1 0 <= x33 <= 1 0 <= x34 <= 1 0 <= y1 <= 0 0 <= y2 <= 1 0 <= y3 <= 1 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_form_location_routing_subopt() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 integer first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds -Inf <= z <= Inf 0 <= x11 <= 1 0 <= x12 <= 1 0 <= x13 <= 1 0 <= x14 <= 1 0 <= x21 <= 1 0 <= x22 <= 1 0 <= x23 <= 1 0 <= x24 <= 1 0 <= x31 <= 1 0 <= x32 <= 1 0 <= x33 <= 1 0 <= x34 <= 1 0 <= y1 <= 1 0 <= y2 <= 1 0 <= y3 <= 0 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	11226	function strong_branching_simple_form() # All the tests are based on the Generalized Assignment problem. # x_mj = 1 if job j is assigned to machine m form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 51.0 MC_30 + 38.0 MC_31 + 31.0 MC_32 + 35.0 MC_33 + 48.0 MC_34 + 13.0 MC_35 + 53.0 MC_36 + 28.0 MC_37 + 2.0 MC_38 + 3.0 MC_39 + 2.0 MC_40 + 2.0 MC_41 + 4.0 MC_42 + 3.0 MC_43 + 3.0 MC_44 + 2.0 MC_45 + 4.0 MC_46 + 3.0 MC_47 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_39 + 1.0 MC_44 + 1.0 MC_45 + 1.0 MC_47 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_40 + 1.0 MC_41 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_44 + 1.0 MC_45 + 1.0 MC_46 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_37 + 1.0 MC_38 + 1.0 MC_42 + 1.0 MC_44 + 1.0 MC_46 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_37 + 1.0 MC_46 + 1.0 MC_47 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_31 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_46 + 1.0 MC_47 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_36 + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_43 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_34 + 1.0 MC_36 + 1.0 MC_37 + 1.0 MC_37 + 1.0 MC_40 + 1.0 MC_41 + 1.0 MC_42 + 1.0 MC_43 >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 + 1.0 MC_47 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 + 1.0 MC_47 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 + 1.0 MC_46 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 + 1.0 MC_46 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_30, MC_31, MC_32, MC_33, MC_34, MC_35, MC_36, MC_37, MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45, MC_46, MC_47 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_30 >= 0.0 MC_31 >= 0.0 MC_32 >= 0.0 MC_33 >= 0.0 MC_34 >= 0.0 MC_35 >= 0.0 MC_36 >= 0.0 MC_37 >= 0.0 MC_38 >= 0.0 MC_39 >= 0.0 MC_40 >= 0.0 MC_41 >= 0.0 MC_42 >= 0.0 MC_43 >= 0.0 MC_44 >= 0.0 MC_45 >= 0.0 MC_46 >= 0.0 MC_47 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_strong_branching() env, master, sps, reform = strong_branching_simple_form() env.params.local_art_var_cost = 1.0 Coluna.set_optim_start_time!(env) # Define optimizers for the formulations. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end # Create the master lp solution. vars = Dict{String, Coluna.MathProg.VarId}(Coluna.MathProg.getname(master, var) => varid for (varid, var) in Coluna.MathProg.getvars(master)) _id(id, orig_form_id) = Coluna.MathProg.VarId(id; origin_form_uid = orig_form_id) #### # colgen_output = Coluna.Algorithm.ColGenOutput(Primal solution # \| MC_36 = 0.5 # \| MC_38 = 0.16666667 # \| MC_39 = 0.16666667 # \| MC_42 = 0.33333333 # \| MC_43 = 0.16666667 # \| MC_44 = 0.16666667 # \| MC_46 = 0.33333333 # \| MC_47 = 0.16666667 # └ value = 31.50 extended_sol = Coluna.PrimalSolution( master, [ _id(vars["MC_36"], 4), _id(vars["MC_38"], 4), _id(vars["MC_39"], 5), _id(vars["MC_42"], 4), _id(vars["MC_43"], 5), _id(vars["MC_44"], 4), _id(vars["MC_46"], 4), _id(vars["MC_47"], 5) ], [0.5, 0.16666667, 0.16666667, 0.33333333, 0.16666667, 0.16666667, 0.33333333, 0.16666667], 31.5, Coluna.ColunaBase.FEASIBLE_SOL ) # Pool of first subproblem col_items = Dict( "MC_30" => [4, 5, 6, 7], "MC_31" => [1, 2, 3, 5], "MC_32" => [2, 4, 6], "MC_33" => [2, 3, 4], "MC_34" => [1, 4, 7], "MC_35" => [1, 4], "MC_36" => [1, 4, 6, 7], "MC_37" => [3, 4, 7], "MC_38" => [3, 6], "MC_39" => [1, 5, 7], "MC_40" => [2, 5], "MC_41" => [2, 5], "MC_42" => [2, 3, 5, 6], "MC_43" => [2, 3, 6], "MC_44" => [1, 3, 5, 6], "MC_45" => [1, 5], "MC_46" => [2, 3, 5, 7], "MC_47" => [1, 3, 7] ) pool = Coluna.MathProg.get_primal_sol_pool(sps[1]) col_names = ["MC_31", "MC_33", "MC_35", "MC_37", "MC_39", "MC_41", "MC_43", "MC_45", "MC_47"] vars_sp1 = Dict{String, Coluna.MathProg.VarId}(Coluna.MathProg.getname(sps[1], var) => varid for (varid, var) in Coluna.MathProg.getvars(sps[1])) for col_name in col_names col_id = Coluna.MathProg.VarId(vars[col_name]; duty = Coluna.MathProg.DwSpPrimalSol) var_ids = [vars_sp1["x_2$i"] for i in col_items[col_name]] var_vals = ones(Float64, length(var_ids)) primal_sol = MathProg.PrimalSolution(master, var_ids, var_vals, 0.0, MathProg.FEASIBLE_SOL) MathProg.push_in_pool!(pool, primal_sol, col_id, 1.0) end # Pool of second subproblem pool = Coluna.MathProg.get_primal_sol_pool(sps[2]) col_names =["MC_30", "MC_32", "MC_34", "MC_36", "MC_38", "MC_40", "MC_42", "MC_44", "MC_46"] vars_sp2 = Dict{String, Coluna.MathProg.VarId}(Coluna.MathProg.getname(sps[2], var) => varid for (varid, var) in Coluna.MathProg.getvars(sps[2])) for col_name in col_names col_id = Coluna.MathProg.VarId(vars[col_name]; duty = Coluna.MathProg.DwSpPrimalSol) vars_sp2 = Dict{String, Coluna.MathProg.VarId}(Coluna.MathProg.getname(sps[1], var) => varid for (varid, var) in Coluna.MathProg.getvars(sps[2])) var_ids = [vars_sp2["x_1$i"] for i in col_items[col_name]] var_vals = ones(Float64, length(var_ids)) primal_sol = MathProg.PrimalSolution(master, var_ids, var_vals, 0.0, MathProg.FEASIBLE_SOL) MathProg.push_in_pool!(pool, primal_sol, col_id, 1.0) end ### Algorithm conquer1 = Coluna.Algorithm.RestrMasterLPConquer() conquer2 = Coluna.Algorithm.ColCutGenConquer() phases = [ Coluna.Algorithm.PhasePrinter( Coluna.Algorithm.StrongBranchingPhaseContext( Coluna.Algorithm.BranchingPhase( 3, conquer1, Coluna.Algorithm.ProductScore() ), Coluna.Algorithm.UnitsUsage() ), 1 ), Coluna.Algorithm.PhasePrinter( Coluna.Algorithm.StrongBranchingPhaseContext( Coluna.Algorithm.BranchingPhase( 1, conquer2, Coluna.Algorithm.ProductScore() ), Coluna.Algorithm.UnitsUsage() ), 2 ) ] rules = [ Coluna.Branching.PrioritisedBranchingRule(Coluna.SingleVarBranchingRule(), 1.0, 1.0) ] ctx = Coluna.Algorithm.BranchingPrinter( Coluna.Algorithm.StrongBranchingContext( phases, rules, Coluna.Algorithm.MostFractionalCriterion(), 1e-5 ) ) conquer_output = Coluna.Algorithm.OptimizationState( master; lp_dual_bound = 31.5 ) Coluna.Algorithm.update_lp_primal_sol!(conquer_output, extended_sol) # TODO: interface to register Records. records = Coluna.Algorithm.Records() node = Coluna.Algorithm.Node( 0, "", nothing, MathProg.DualBound(reform), records ) global_primal_handler = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) input = Coluna.Algorithm.DivideInputFromBaB( 0, conquer_output, records, global_primal_handler ) original_sol = Coluna.Algorithm.get_original_sol(reform, conquer_output) Branching.run_branching!(ctx, env, reform, input, extended_sol, original_sol) end register!(unit_tests, "branching", test_strong_branching)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	2347	struct MockCandidate <: Coluna.Branching.AbstractBranchingCandidate local_id::Int64 lhs::Float64 end Coluna.Branching.get_local_id(c::MockCandidate) = c.local_id Coluna.Branching.get_lhs(c::MockCandidate) = c.lhs Coluna.Branching.getdescription(::MockCandidate) = "MockCandidate" function _mock_candidates(lhs::Vector{Float64}) return map(enumerate(lhs)) do (i, lhs) MockCandidate(i, lhs) end end function test_first_found_criterion1() candidates = _mock_candidates([0.1, 0.2, 0.3, 0.4, 0.5]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.FirstFoundCriterion(), 3 ) @test length(selected_candidates) == 3 @test selected_candidates[1].local_id == 1 @test selected_candidates[2].local_id == 2 @test selected_candidates[3].local_id == 3 end register!(unit_tests, "branching", test_first_found_criterion1) function test_most_fractional_criterion1() candidates = _mock_candidates([0.1, 0.2, 0.3, 0.4, 0.5]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.MostFractionalCriterion(), 3 ) @test length(selected_candidates) == 3 @test selected_candidates[1].local_id == 5 @test selected_candidates[2].local_id == 4 @test selected_candidates[3].local_id == 3 end register!(unit_tests, "branching", test_most_fractional_criterion1) function test_least_fractional_criterion1() candidates = _mock_candidates([0.4, 0.3, 0.45, -0.4, -0.8]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.LeastFractionalCriterion(), 2 ) @test length(selected_candidates) == 2 @test selected_candidates[1].local_id == 5 @test selected_candidates[2].local_id == 2 end register!(unit_tests, "branching", test_least_fractional_criterion1) function test_closest_to_non_zero_integer_criterion1() candidates = _mock_candidates([0.4, 0.3, 0.45, -0.4, -0.8]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.ClosestToNonZeroIntegerCriterion(), 2 ) @test length(selected_candidates) == 2 @test selected_candidates[1].local_id == 5 @test selected_candidates[2].local_id == 3 end register!(unit_tests, "branching", test_closest_to_non_zero_integer_criterion1)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	65250	############################################################################################ # Test default implementation of an iteration of Column Generation ############################################################################################ # 1- minimize # 2- maximize # 3- minimize with pure master variables # 4- minimize with objective constant # # TODO: description of the tests. ############################################################################################ using Test # Minimization and test all constraint senses form1() = """ master min 3x1 + 2x2 + 5x3 + 4y1 + 3y2 + 5y3 + z s.t. x1 + x2 + x3 + y1 + y2 + y3 + 2z >= 10 x1 + 2x2 + y1 + 2y2 + z <= 100 x1 + 3x3 + y1 + + 3y3 == 100 z <= 5 dw_sp min x1 + x2 + x3 + y1 + y2 + y3 s.t. x1 + x2 + x3 + y1 + y2 + y3 >= 10 integer representatives x1, x2, x3, y1, y2, y3 pure z bounds x1 >= 0 x2 >= 0 x3 >= 0 y1 >= 0 y2 >= 0 y3 >= 0 z >= 0 """ function get_name_to_varids(form) d = Dict{String, ClMP.VarId}() for (varid, var) in ClMP.getvars(form) d[ClMP.getname(form, var)] = varid end return d end function get_name_to_constrids(form) d = Dict{String, ClMP.ConstrId}() for (constrid, constr) in ClMP.getconstrs(form) d[ClMP.getname(form, constr)] = constrid end return d end # Simple case with only subproblem representatives variables. function test_reduced_costs_calculation_helper() _, master, _, _, _ = reformfromstring(form1()) vids = get_name_to_varids(master) cids = get_name_to_constrids(master) helper = ClA.ReducedCostsCalculationHelper(master) @test helper.dw_subprob_c[vids["x1"]] == 3 @test helper.dw_subprob_c[vids["x2"]] == 2 @test helper.dw_subprob_c[vids["x3"]] == 5 @test helper.dw_subprob_c[vids["y1"]] == 4 @test helper.dw_subprob_c[vids["y2"]] == 3 @test helper.dw_subprob_c[vids["y3"]] == 5 @test helper.dw_subprob_c[vids["z"]] == 0 @test helper.dw_subprob_A[cids["c1"], vids["x1"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["x2"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["x3"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["y1"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["y2"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["y3"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["z"]] == 0 # z is not in the subproblem. @test helper.dw_subprob_A[cids["c2"], vids["x1"]] == 1 @test helper.dw_subprob_A[cids["c2"], vids["x2"]] == 2 @test helper.dw_subprob_A[cids["c2"], vids["y1"]] == 1 @test helper.dw_subprob_A[cids["c2"], vids["y2"]] == 2 @test helper.dw_subprob_A[cids["c2"], vids["z"]] == 0 # z is not in the subproblem. @test helper.dw_subprob_A[cids["c3"], vids["x1"]] == 1 @test helper.dw_subprob_A[cids["c3"], vids["x3"]] == 3 @test helper.dw_subprob_A[cids["c3"], vids["y1"]] == 1 @test helper.dw_subprob_A[cids["c3"], vids["y3"]] == 3 @test helper.dw_subprob_A[cids["c3"], vids["z"]] == 0 # z is not in the subproblem. @test helper.master_c[vids["x1"]] == 0 # x1 is not in the master. @test helper.master_c[vids["x2"]] == 0 # x2 is not in the master. @test helper.master_c[vids["x3"]] == 0 # x3 is not in the master. @test helper.master_c[vids["y1"]] == 0 # y1 is not in the master. @test helper.master_c[vids["y2"]] == 0 # y2 is not in the master. @test helper.master_c[vids["y3"]] == 0 # y3 is not in the master. @test helper.master_c[vids["z"]] == 1 @test helper.master_A[cids["c1"], vids["x1"]] == 0 # x1 is not in the master. @test helper.master_A[cids["c1"], vids["z"]] == 2 @test helper.master_A[cids["c2"], vids["z"]] == 1 @test helper.master_A[cids["c3"], vids["z"]] == 0 @test helper.master_A[cids["c4"], vids["z"]] == 1 end register!(unit_tests, "colgen_default", test_reduced_costs_calculation_helper) # Minimization and test all constraint senses form2() = """ master min 3x1 + 2x2 + 5x3 + 4y1 + 3y2 + 5y3 + z1 + z2 s.t. x1 + x2 + x3 + y1 + y2 + y3 + 2z1 + z2 >= 10 x1 + 2x2 + y1 + 2y2 + z1 <= 100 x1 + 3x3 + y1 + + 3y3 == 100 z1 + z2 <= 5 dw_sp min x1 + x2 + x3 + y1 + y2 + y3 s.t. x1 + x2 + x3 + y1 + y2 + y3 >= 10 integer representatives x1, x2, x3, y1, y2, y3 pure z1, z2 bounds x1 >= 0 x2 >= 0 x3 >= 0 y1 >= 0 y2 >= 0 y3 >= 0 z1 >= 0 z2 >= 3 """ function test_subgradient_calculation_helper() _, master, _, _, _ = reformfromstring(form2()) vids = get_name_to_varids(master) cids = get_name_to_constrids(master) helper = ClA.SubgradientCalculationHelper(master) @test helper.a[cids["c1"]] == 10 @test helper.a[cids["c2"]] == -100 @test helper.a[cids["c3"]] == 100 @test helper.a[cids["c4"]] == -5 @test helper.A[cids["c1"], vids["x1"]] == 1 @test helper.A[cids["c1"], vids["x2"]] == 1 @test helper.A[cids["c1"], vids["x3"]] == 1 @test helper.A[cids["c1"], vids["y1"]] == 1 @test helper.A[cids["c1"], vids["y2"]] == 1 @test helper.A[cids["c1"], vids["y3"]] == 1 @test helper.A[cids["c1"], vids["z1"]] == 2 @test helper.A[cids["c1"], vids["z2"]] == 1 @test helper.A[cids["c2"], vids["x1"]] == -1 @test helper.A[cids["c2"], vids["x2"]] == -2 @test helper.A[cids["c2"], vids["y1"]] == -1 @test helper.A[cids["c2"], vids["y2"]] == -2 @test helper.A[cids["c2"], vids["z1"]] == -1 @test helper.A[cids["c2"], vids["z2"]] == 0 @test helper.A[cids["c3"], vids["x1"]] == 1 @test helper.A[cids["c3"], vids["x3"]] == 3 @test helper.A[cids["c3"], vids["y1"]] == 1 @test helper.A[cids["c3"], vids["y3"]] == 3 @test helper.A[cids["c3"], vids["z1"]] == 0 @test helper.A[cids["c3"], vids["z2"]] == 0 @test helper.A[cids["c4"], vids["z1"]] == -1 @test helper.A[cids["c4"], vids["z2"]] == -1 end register!(unit_tests, "colgen_default", test_subgradient_calculation_helper) # All the tests are based on the Generalized Assignment problem. # x_mj = 1 if job j is assigned to machine m function min_toy_gap() # We introduce variables z1 & z2 to force dual value of constraint c7 to equal to 28. form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 51.0 MC_30 + 38.0 MC_31 + 31.0 MC_32 + 35.0 MC_33 + 48.0 MC_34 + 13.0 MC_35 + 53.0 MC_36 + 28.0 MC_37 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 28 z1 - 28 z2 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_32 + 1.0 MC_33 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_37 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_37 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_31 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_36 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_34 + 1.0 MC_36 + 1.0 MC_37 + z1 - z2 >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_30, MC_31, MC_32, MC_33, MC_34, MC_35, MC_36, MC_37 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var pure z1, z2 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_30 >= 0.0 MC_31 >= 0.0 MC_32 >= 0.0 MC_33 >= 0.0 MC_34 >= 0.0 MC_35 >= 0.0 MC_36 >= 0.0 MC_37 >= 0.0 z1 >= 0.0 z2 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function max_toy_gap() # We introduce variables (z1, z2), (z3, z4) and (z5, z6) to force dual value of constraint c2, c1 and c5 to be equal to 6.0, 3.0 and 22.0 respectively. form = """ master max - 10000.0 local_art_of_cov_5 - 10000.0 local_art_of_cov_4 - 10000.0 local_art_of_cov_6 - 10000.0 local_art_of_cov_7 - 10000.0 local_art_of_cov_2 - 10000.0 local_art_of_cov_3 - 10000.0 local_art_of_cov_1 - 10000.0 local_art_of_sp_lb_5 - 10000.0 local_art_of_sp_ub_5 - 10000.0 local_art_of_sp_lb_4 - 10000.0 local_art_of_sp_ub_4 - 100000.0 global_pos_art_var - 100000.0 global_neg_art_var + 53.0 MC_30 + 49.0 MC_31 + 35.0 MC_32 + 45.0 MC_33 + 27.0 MC_34 + 42.0 MC_35 + 45.0 MC_36 + 12.0 MC_37 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 6.0 z1 - 6.0 z2 + 3.0 z3 - 3.0 z4 + 22.0 z5 - 22.0 z6 s.t. 1.0 x_11 + 1.0 x_21 - 1.0 local_art_of_cov_1 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_34 + z3 - z4 <= 1.0 1.0 x_12 + 1.0 x_22 - 1.0 local_art_of_cov_2 - 1.0 global_neg_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_37 + z1 - z2 <= 1.0 1.0 x_13 + 1.0 x_23 - 1.0 local_art_of_cov_3 - 1.0 global_neg_art_var + 1.0 MC_31 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_35 <= 1.0 1.0 x_14 + 1.0 x_24 - 1.0 local_art_of_cov_4 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_31 + 1.0 MC_36 <= 1.0 1.0 x_15 + 1.0 x_25 - 1.0 local_art_of_cov_5 - 1.0 global_neg_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + z5 - z6 <= 1.0 1.0 x_16 + 1.0 x_26 - 1.0 local_art_of_cov_6 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_33 <= 1.0 1.0 x_17 + 1.0 x_27 - 1.0 local_art_of_cov_7 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 <= 1.0 {MasterConvexityConstr} dw_sp max x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp max x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_30, MC_31, MC_32, MC_33, MC_34, MC_35, MC_36, MC_37 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var pure z1, z2, z3, z4, z5, z6 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_30 >= 0.0 MC_31 >= 0.0 MC_32 >= 0.0 MC_33 >= 0.0 MC_34 >= 0.0 MC_35 >= 0.0 MC_36 >= 0.0 MC_37 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_with_penalties() # We add variables z1 and z2 to fix the dual value of constraint c4 to 21. # We add variables z3 and z4 to fix the dual value of constraint c3 to 18.56666667. # We add variables z5 and z6 to fix the dual value of constraint c7 to 19.26666667. # We add variables z7 and z8 to fix the dual value of constraint c1 to 8.26666667. # We add variables z9 and z10 to fix the dual value of constraint c2 to 17.13333333 form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 21 z1 - 21 z2 + 18.56666667 z3 -18.56666667 z4 + 19.26666667 z5 - 19.26666667 z6 + 8.26666667 z7 - 8.26666667 z8 + 17.13333333 z9 - 17.13333333 z10 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + z7 - z8 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 + z9 - z10 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 + z3 - z4 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 + z1 - z2 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 + z5 - z6 >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7, z1, z2, z3, z4, z5, z6, z7, z8, z9, z10 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 0.0 <= y_1 <= 1.0 0.0 <= y_2 <= 1.0 0.0 <= y_3 <= 1.0 0.0 <= y_4 <= 1.0 0.0 <= y_5 <= 1.0 0.0 <= y_6 <= 1.0 0.0 <= y_7 <= 1.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_with_obj_const() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 51.0 MC_30 + 38.0 MC_31 + 31.0 MC_32 + 35.0 MC_33 + 48.0 MC_34 + 13.0 MC_35 + 53.0 MC_36 + 28.0 MC_37 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 700.0 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_32 + 1.0 MC_33 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_37 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_37 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_31 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_36 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_34 + 1.0 MC_36 + 1.0 MC_37 >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_30, MC_31, MC_32, MC_33, MC_34, MC_35, MC_36, MC_37 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_30 >= 0.0 MC_31 >= 0.0 MC_32 >= 0.0 MC_33 >= 0.0 MC_34 >= 0.0 MC_35 >= 0.0 MC_36 >= 0.0 MC_37 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function check_identical_subproblems() # Used to check the output of identical_subproblem. The two formulations should be equivalent. # Subproblem 5 is introduced twice. form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 8.0 x_21 + 5.0 x_22 + 11.0 x_23 + 21.0 x_24 + 6.0 x_25 + 5.0 x_26 + 19.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 8.0 dw_sp min 8.0 x_21 + 5.0 x_22 + 11.0 x_23 + 21.0 x_24 + 6.0 x_25 + 5.0 x_26 + 19.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 2.0 x_21 + 3.0 x_22 + 3.0 x_23 + 1.0 x_24 + 2.0 x_25 + 1.0 x_26 + 1.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end ### Implementation of ColGen API to test and call the default implementation struct TestColGenIterationContext <: ColGen.AbstractColGenContext context::ClA.ColGenContext master_lp_primal_sol::Dict{String, Float64} master_lp_dual_sol::Dict{String, Float64} master_lp_obj_val::Float64 pricing_var_reduced_costs::Dict{String, Float64} end ColGen.get_reform(ctx::TestColGenIterationContext) = ColGen.get_reform(ctx.context) ColGen.get_master(ctx::TestColGenIterationContext) = ColGen.get_master(ctx.context) ColGen.is_minimization(ctx::TestColGenIterationContext) = ColGen.is_minimization(ctx.context) ColGen.get_pricing_subprobs(ctx::TestColGenIterationContext) = ColGen.get_pricing_subprobs(ctx.context) ColGen.colgen_iteration_output_type(::TestColGenIterationContext) = ClA.ColGenIterationOutput struct TestColGenStage <: ColGen.AbstractColGenStage end ColGen.get_pricing_subprob_optimizer(::TestColGenStage, _) = 1 function ColGen.optimize_master_lp_problem!(master, ctx::TestColGenIterationContext, env) output = ColGen.optimize_master_lp_problem!(master, ctx.context, env) primal_sol = ColGen.get_primal_sol(output) for (var_id, var) in ClMP.getvars(master) name = ClMP.getname(master, var) if !haskey(ctx.master_lp_primal_sol, name) @test primal_sol[var_id] ≈ 0.0 else @test primal_sol[var_id] ≈ ctx.master_lp_primal_sol[name] end end dual_sol = ColGen.get_dual_sol(output) for (constr_id, constr) in ClMP.getconstrs(master) name = ClMP.getname(master, constr) if !haskey(ctx.master_lp_dual_sol, name) @test dual_sol[constr_id] ≈ 0.0 else @test dual_sol[constr_id] ≈ ctx.master_lp_dual_sol[name] end end return output end ColGen.check_primal_ip_feasibility!(master_lp_primal_sol, ::TestColGenIterationContext, phase, env) = nothing, false ColGen.is_unbounded(ctx::TestColGenIterationContext) = ColGen.is_unbounded(ctx.context) ColGen.is_infeasible(ctx::TestColGenIterationContext) = ColGen.is_infeasible(ctx.context) ColGen.update_master_constrs_dual_vals!(ctx::TestColGenIterationContext, master_lp_dual_sol) = ColGen.update_master_constrs_dual_vals!(ctx.context, master_lp_dual_sol) ColGen.update_reduced_costs!(ctx::TestColGenIterationContext, phase, red_costs) = nothing ColGen.get_subprob_var_orig_costs(ctx::TestColGenIterationContext) = ColGen.get_subprob_var_orig_costs(ctx.context) ColGen.get_subprob_var_coef_matrix(ctx::TestColGenIterationContext) = ColGen.get_subprob_var_coef_matrix(ctx.context) function ColGen.update_sp_vars_red_costs!(ctx::TestColGenIterationContext, sp::Formulation{DwSp}, red_costs) ColGen.update_sp_vars_red_costs!(ctx.context, sp, red_costs) for (_, var) in ClMP.getvars(sp) name = ClMP.getname(sp, var) @test ctx.pricing_var_reduced_costs[name] ≈ ClMP.getcurcost(sp, var) end return end ColGen.compute_sp_init_pb(ctx::TestColGenIterationContext, sp::Formulation{DwSp}) = ColGen.compute_sp_init_pb(ctx.context, sp) ColGen.compute_sp_init_db(ctx::TestColGenIterationContext, sp::Formulation{DwSp}) = ColGen.compute_sp_init_db(ctx.context, sp) ColGen.set_of_columns(ctx::TestColGenIterationContext) = ColGen.set_of_columns(ctx.context) ColGen.push_in_set!(ctx::TestColGenIterationContext, set, col) = ColGen.push_in_set!(ctx.context, set, col) # Columns insertion function ColGen.insert_columns!(ctx::TestColGenIterationContext, phase, columns) return ColGen.insert_columns!(ctx.context, phase, columns) end function ColGen.optimize_pricing_problem!(ctx::TestColGenIterationContext, sp::Formulation{DwSp}, env, optimizer, master_dual_sol, stab_changes_mast_dual_sol) output = ColGen.optimize_pricing_problem!(ctx.context, sp, env, optimizer, master_dual_sol, stab_changes_mast_dual_sol) # test here return output end function ColGen.compute_dual_bound(ctx::TestColGenIterationContext, phase, sp_dbs, generated_columns, master_dual_sol) return ColGen.compute_dual_bound(ctx.context, phase, sp_dbs, generated_columns, master_dual_sol) end function test_colgen_iteration_min_gap() env, master, sps, reform = min_toy_gap() # vids = get_name_to_varids(master) # cids = get_name_to_constrids(master) master_lp_primal_sol = Dict( "MC_30" => 1/3, "MC_31" => 2/3, "MC_32" => 1/3, "MC_36" => 1/3, "MC_37" => 1/3, ) master_lp_dual_sol = Dict( "c1" => 11.33333333, "c2" => 17.33333333, "c5" => 9.33333333, "c6" => 13.66666667, "c7" => 28.0, ) master_obj_val = 79.67 pricing_var_reduced_costs = Dict( "x_11" => - 3.3333333300000003, "x_12" => - 12.333333329999999, "x_13" => 11.0, "x_14" => 21.0, "x_15" => - 3.3333333300000003, "x_16" => - 8.66666667, "x_17" => - 9.0, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 10.33333333, "x_22" => - 5.3333333299999985, "x_23" => 11.0, "x_24" => 12.0, "x_25" => 4.66666667, "x_26" => - 5.66666667, "x_27" => - 23.0, "PricingSetupVar_sp_4" => 0.0, ) # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 79.666666667 @test output.db ≈ 21.3333333333 @test output.nb_new_cols == 2 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_min_gap) function test_colgen_iteration_max_gap() env, master, sps, reform = max_toy_gap() master_lp_primal_sol = Dict( "MC_30" => 0.5, "MC_31" => 0.5, "MC_33" => 0.5, "MC_34" => 0.5, ) master_lp_dual_sol = Dict( "c1" => 3.0, # fixed "c2" => 6.0, # fixed "c4" => 15.0, "c5" => 22.0, # fixed "c6" => 11.0, "c7" => 8.0, "c9" => 16.0, "c11" => 6.0 ) master_obj_val = 87.00 pricing_var_reduced_costs = Dict( "x_11" => 5.0, "x_12" => - 1.0, "x_13" => 11.0, "x_14" => 6.0, "x_15" => - 16.0, "x_16" => - 6.0, "x_17" => 11.0, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 2.0, "x_22" => 6.0, "x_23" => 11.0, "x_24" => - 3.0, "x_25" => - 8.0, "x_26" => - 3.0, "x_27" => - 3.0, "PricingSetupVar_sp_4" => 0.0, ) ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 87.00 @test output.db ≈ 110.00 @test output.nb_new_cols == 2 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_max_gap) function test_colgen_iteration_pure_master_vars() env, master, sps, reform = toy_gap_with_penalties() master_lp_primal_sol = Dict( "MC_41" => 1, "MC_42" => 1, "y_2" => 1, ) master_lp_dual_sol = Dict( "c1" => 8.26666667, # fixed "c2" => 17.13333333, # fixed "c3" => 18.56666667, # fixed "c4" => 21.0, # fixed "c5" => 17.86666667, "c6" => 15.41666667, "c7" => 19.26666667, # fixed "c8" => -10.86666667, "c10" => -22.55, "c12" => -30.83333334 ) master_obj_val = 52.95 pricing_var_reduced_costs = Dict( "x_11" => - 0.26666666999999933, "x_12" => - 12.13333333, "x_13" => - 7.56666667, "x_14" => 0.0, "x_15" => - 11.86666667, "x_16" => - 10.41666667, "x_17" => - 0.26666666999999933, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 7.266666669999999, "x_22" => - 5.133333329999999, "x_23" => - 7.56666667, "x_24" => - 9.0, "x_25" => - 3.8666666700000007, "x_26" => - 7.41666667, "x_27" => - 14.26666667, "PricingSetupVar_sp_4" => 0.0, ) # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 52.9500 @test output.db ≈ 51.5 @test output.nb_new_cols == 1 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_pure_master_vars) function test_colgen_iteration_obj_const() env, master, sps, reform = toy_gap_with_obj_const() master_lp_primal_sol = Dict( "MC_30" => 1/3, "MC_31" => 2/3, "MC_32" => 1/3, "MC_36" => 1/3, "MC_37" => 1/3, ) master_lp_dual_sol = Dict( "c1" => 11.33333333, "c3" => 17.33333333, "c5" => 9.33333333, "c6" => 31.0, "c7" => 10.66666667, ) master_obj_val = 779.67 pricing_var_reduced_costs = Dict( "x_11" => - 3.3333333300000003, "x_12" => 5.0, "x_13" => - 6.3333333299999985, "x_14" => 21.0, "x_15" => - 3.3333333300000003, "x_16" => - 26.0, "x_17" => 8.33333333, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 10.33333333, "x_22" => 12.0, "x_23" => - 6.3333333299999985, "x_24" => 12.0, "x_25" => 4.66666667, "x_26" => - 23.0, "x_27" => - 5.66666667, "PricingSetupVar_sp_4" => 0.0, ) # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 779.6666666666667 @test output.db ≈ 717.6666666766668 @test output.nb_new_cols == 2 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_obj_const) ############################################################################################ # Test column insertion ############################################################################################ function insert_cols_form() # We introduce variables z1 & z2 to force dual value of constraint c7 to equal to 28. form = """ master min x1 + x2 + x3 + x4 + x5 s.t. x1 + x2 + x3 + x4 + x5 >= 0.0 dw_sp min x1 + x2 + x3 + x4 + x5 continuous representatives x1, x2, x3, x4, x5 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_two_identicals_cols_at_two_iterations_failure() env, master, sps, reform = insert_cols_form() spform = sps[1] spvarids = Dict(CL.getname(spform, var) => varid for (varid, var) in CL.getvars(spform)) phase = ClA.ColGenPhase0() ## Iteration 1 ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( throw_column_already_inserted_warning = true )) redcosts_spsols = [-2.0, 2.0] col1 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col2 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x2", "x3"]), [5.0, 2.0], 2.5, ClB.FEASIBLE_SOL ) # not inserted because positive reduced cost. columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col1, col2]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end new_cols = ColGen.insert_columns!(ctx, phase, columns) @test length(new_cols) == 1 ## Iteration 2 redcosts_spsols = [-1.0] col3 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 3.0, ClB.FEASIBLE_SOL ) columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col3]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end @test_throws ClA.ColumnAlreadyInsertedColGenWarning ColGen.insert_columns!(ctx, phase, columns) end register!(unit_tests, "colgen_default", test_two_identicals_cols_at_two_iterations_failure) function test_two_identicals_cols_at_same_iteration_ok() env, master, sps, reform = insert_cols_form() spform = sps[1] spvarids = Dict(CL.getname(spform, var) => varid for (varid, var) in CL.getvars(spform)) phase = ClA.ColGenPhase0() redcosts_spsols = [-2.0, -2.0, 2.0] col1 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col2 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 2.0, ClB.FEASIBLE_SOL ) col3 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x2", "x3"]), [5.0, 2.0], 3.5, ClB.FEASIBLE_SOL ) # not inserted because positive reduced cost. ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( throw_column_already_inserted_warning = true )) columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col1, col2, col3]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end new_cols = ColGen.insert_columns!(ctx, phase, columns) @test length(new_cols) == 2 end register!(unit_tests, "colgen_default", test_two_identicals_cols_at_same_iteration_ok) function test_deactivated_column_added_twice_at_same_iteration_ok() env, master, sps, reform = insert_cols_form() spform = sps[1] spvarids = Dict(CL.getname(spform, var) => varid for (varid, var) in CL.getvars(spform)) phase = ClA.ColGenPhase0() ## Add column. col1 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col_id = ClMP.insert_column!(master, col1, "MC") ## Deactivate column. ClMP.deactivate!(master, col_id) redcosts_spsols = [-2.0, -2.0] col2 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col3 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 2.0, ClB.FEASIBLE_SOL ) ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( throw_column_already_inserted_warning = true )) columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col2, col3]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end new_cols = ColGen.insert_columns!(ctx, phase, columns) @test length(new_cols) == 1 end register!(unit_tests, "colgen_default", test_deactivated_column_added_twice_at_same_iteration_ok) ############################################################################################ # Test the column generation loop ############################################################################################ function min_toy_gap_for_colgen_loop() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_colgen_loop() env, master, sps, reform = min_toy_gap_for_colgen_loop() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end phase = ClA.ColGenPhase0() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) ColGen.setup_reformulation!(reform, phase) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_phase!(ctx, phase, ColGenIterationTestStage(), env, input, Coluna.Algorithm.NoColGenStab()) # EXPECTED: # """ # <it= 11> <et=19.92> <mst= 0.00> <sp= 0.00> <cols= 0> <al= 0.00> <DB= 70.3333> <mlp= 70.3333> <PB=89.0000> # [ Info: Column generation algorithm has converged. # """ @test output.mlp ≈ 70.33333333 @test output.db ≈ 70.33333333 @test Coluna.ColunaBase.getvalue(output.master_ip_primal_sol) ≈ 89.0 return end register!(unit_tests, "colgen_default", test_colgen_loop) function min_toy_gap_for_colgen() # We use very large costs to go through phase 1. form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_identical_subproblems() env, master, sps, reform = identical_subproblems() ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 75 @test output.mlp ≈ 75 end register!(unit_tests, "colgen_default", test_identical_subproblems) # Don't run this test because we use it to check the output of the previous test. function expected_output_identical_subproblems() env, master, sps, reform = check_identical_subproblems() ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 75 @test output.db ≈ 75 end register!(unit_tests, "colgen_default", expected_output_identical_subproblems) function test_colgen() env, master, sps, reform = min_toy_gap_for_colgen() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end Coluna.set_optim_start_time!(env) ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 7033.3333333 @test output.db ≈ 7033.3333333 end register!(unit_tests, "colgen", test_colgen) function identical_subproblems() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 1.0 x_11 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 2.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_5 binary representatives x_11, x_12, x_13, x_14, x_15, x_16, x_17 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_17 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function r1c_form() form = """ master min 1.0 MC_1 + 1.0 MC_2 + 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 + 8.0 x_1 + 1.0 x_2 + 3.0 x_3 + 11.0 x_4 + 7.0 x_5 s.t. 1.0 MC_1 + 1.0 MC_2 + 1.0 MC_4 + 1.0 x_1 >= 1.0 1.0 MC_1 + 1.0 MC_2 + 1.0 MC_4 + 1.0 MC_5 + 1.0 x_2 >= 1.0 1.0 MC_2 + 1.0 MC_3 + 1.0 MC_5 + 1.0 x_3 >= 1.0 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 + 1.0 x_4 >= 1.0 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 + 1.0 x_5 >= 1.0 0.0 MC_1 + 1.0 MC_2 + 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 <= 1.0 dw_sp min 8.0 x_1 + 1.0 x_2 + 3.0 x_3 + 11.0 x_4 + 7.0 x_5 s.t. 2.0 x_1 + 3.0 x_2 + 3.0 x_3 <= 8.0 continuous columns MC_1, MC_2, MC_3, MC_4, MC_5 binary representatives x_1, x_2, x_3, x_4, x_5 bounds 0.0 <= x_1 <= 1.0 0.0 <= x_2 <= 1.0 0.0 <= x_3 <= 1.0 0.0 <= x_4 <= 1.0 0.0 <= x_5 <= 1.0 MC_1 >= 0.0 MC_2 >= 0.0 MC_3 >= 0.0 MC_4 >= 0.0 MC_5 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_red_cost_calc_with_non_robust_cuts() var_costs = [ 8.0, 1.0, 3.0, 11.0, 7.0 ] A = [ 1 0 0 0 0; 0 1 0 0 0; 0 0 1 0 0; 0 0 0 1 0; 0 0 0 0 1; 0 0 0 0 0 ] constr_costs = [2.0, 8.0, 1.0, 3.0, 9.0, 4.0] expected_redcosts = var_costs - transpose(A) * constr_costs form = r1c_form() env, master, sps, reform = form constrids = Dict(getname(master, id) => id for (id,_) in ClA.getconstrs(master)) varids = Dict(getname(master, id) => id for (id,_) in ClA.getvars(master)) dual_sol = ClA.DualSolution( master, map(name -> constrids[name], ["c1", "c2", "c3", "c4", "c5", "c6"]), constr_costs, [], [], [], 0.0, FEASIBLE_SOL ) helper = ClA.ReducedCostsCalculationHelper(master) coeffs = transpose(helper.dw_subprob_A) * dual_sol redcosts = helper.dw_subprob_c - coeffs @test redcosts[varids["x_1"]] == expected_redcosts[1] @test redcosts[varids["x_2"]] == expected_redcosts[2] @test redcosts[varids["x_3"]] == expected_redcosts[3] @test redcosts[varids["x_4"]] == expected_redcosts[4] @test redcosts[varids["x_5"]] == expected_redcosts[5] end register!(unit_tests, "colgen", test_red_cost_calc_with_non_robust_cuts) function jet_report_colgen_loop() env, master, sps, reform = min_toy_gap_for_colgen_loop() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end phase = ClA.ColGenPhase0() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) ColGen.setup_reformulation!(reform, phase) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) stage = ColGenIterationTestStage() stab = Coluna.Algorithm.NoColGenStab() @test_call ColGen.run_colgen_phase!(ctx, phase, stage, env, input, stab) # @test output.mlp ≈ 70.33333333 # @test output.db ≈ 70.33333333 return end # Excluded at the moment because too many errors come from DynamicSparseArrays. register!(unit_tests, "colgen_default", jet_report_colgen_loop; x = true)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	15729	# Test using the column generation example for Integer Programming book by Wolsey # See page 191. # There is no so much logic to test in the generic implementation of an iteration of column # generation: # - calculation of reduced costs # - calculation of the master lp dual bound # - flow # - error handling # - output # This is the problems that we consider here: # master # min # 7x_12 + 2x_13 + x_14 + 5x_15 + 3x_23 + 6x_24 + 8x_25 + 4x_34 + 2x_35 + 9x_45 + 28λ1 + 25λ2 + 21λ3 + 19λ4 + 22λ5 + 18λ6 + 28λ7 # s.t. # x_12 + x_13 + x_14 + x_15 + 2λ1 + 2λ2 + 2λ3 + 2λ4 + 2λ5 + 2λ6 + 2λ7 == 2 # x_12 + x_23 + x_24 + x_25 + 2λ1 + 2λ2 + 2λ3 + 1λ4 + 1λ5 + 2λ6 + 3λ7 == 2 # x_13 + x_23 + x_34 + x_35 + 2λ1 + 3λ2 + 2λ3 + 3λ4 + 2λ5 + 3λ6 + 1λ7 == 2 # x_14 + x_24 + x_34 + x_45 + 2λ1 + 2λ2 + 3λ3 + 3λ4 + 3λ5 + 1λ6 + 1λ7 == 2 # x_15 + x_25 + x_35 + x_45 + 2λ1 + 1λ2 + 1λ3 + 1λ4 + 2λ5 + 2λ6 + 3λ7 == 2 # dw_sp # min # 7x_12 + 2x_13 + x_14 + 5x_15 + 3x_23 + 6x_24 + 8x_25 + 4x_34 + 2x_35 + 9x_45 # s.t. # x_12 + x_13 + x_14 + x_15 == 1 # continuous # columns # λ1, λ2, λ3, λ4, λ5, λ6, λ7 # integer # representatives # x_12, x_13, x_14, x_15, x_23, x_24, x_25, x_34, x_35, x_45 # bounds # λ1 >= 0 # λ2 >= 0 # λ3 >= 0 # λ4 >= 0 # λ5 >= 0 # λ6 >= 0 # λ7 >= 0 # x_12 >= 0 # x_13 >= 0 # x_14 >= 0 # x_15 >= 0 # x_23 >= 0 # x_24 >= 0 # x_25 >= 0 # x_34 >= 0 # x_35 >= 0 # x_45 >= 0 mutable struct GlobalTestColGenIterationPrimalHandler global_primal_sol::Union{Nothing, Vector{Float64}} end GlobalTestColGenIterationPrimalHandler() = GlobalTestColGenIterationPrimalHandler(nothing) struct ColGenIterationTestMaster end struct ColGenIterationTestPricing end struct ColGenIterationTestReform end # Column generation context # We have many flags here to test lots of different scenarios that might happens if # we there is a bug in the subsolver or MOI. Base.@kwdef struct ColGenIterationTestContext <: ColGen.AbstractColGenContext master_term_status::ClB.TerminationStatus = ClB.OPTIMAL master_solver_has_no_primal_solution::Bool = false master_solver_has_no_dual_solution::Bool = false pricing_term_status::ClB.TerminationStatus = ClB.OPTIMAL pricing_has_correct_dual_bound::Bool = true pricing_has_incorrect_dual_bound::Bool = false pricing_has_no_dual_bound::Bool = false pricing_solver_has_no_solution::Bool = false new_ip_primal_sol::Bool = false master_has_new_cuts::Bool = false reform = ColGenIterationTestReform() master = ColGenIterationTestMaster() pricing = ColGenIterationTestPricing() end ColGen.get_master(ctx::ColGenIterationTestContext) = ctx.master ColGen.get_reform(ctx::ColGenIterationTestContext) = ctx.reform ColGen.is_minimization(ctx::ColGenIterationTestContext) = true ColGen.get_pricing_subprobs(context) = [(1, context.pricing)] ColGen.colgen_iteration_output_type(::ColGenIterationTestContext) = ClA.ColGenIterationOutput ColGen.colgen_phase_output_type(::ColGenIterationTestContext) = ClA.ColGenPhaseOutput # Stage struct ColGenIterationTestStage <: ColGen.AbstractColGenStage end ColGen.get_pricing_subprob_optimizer(::ColGenIterationTestStage, _) = 1 ColGen.is_exact_stage(::ColGenIterationTestStage) = true # Pricing strategy struct ColGenIterationTestPricingStrategy <: ColGen.AbstractPricingStrategy subprobs::Vector{Tuple{Int, ColGenIterationTestPricing}} end ColGen.get_pricing_strategy(context::ColGenIterationTestContext, _) = ColGenIterationTestPricingStrategy(ColGen.get_pricing_subprobs(context)) ColGen.pricing_strategy_iterate(strategy::ColGenIterationTestPricingStrategy) = iterate(strategy.subprobs) ColGen.pricing_strategy_iterate(strategy::ColGenIterationTestPricingStrategy, state) = iterate(strategy.subprobs, state) # Column generation phase struct ColGenIterationTestPhase <: ColGen.AbstractColGenPhase end # Master struct ColGenIterationTestMasterResult term_status::ClB.TerminationStatus obj_val::Union{Nothing,Float64} primal_sol::Union{Nothing, Vector{Float64}} dual_sol::Union{Nothing, Vector{Float64}} end ColGen.get_primal_sol(res::ColGenIterationTestMasterResult) = res.primal_sol ColGen.get_dual_sol(res::ColGenIterationTestMasterResult) = res.dual_sol ColGen.get_obj_val(res::ColGenIterationTestMasterResult) = res.obj_val ColGen.is_infeasible(res::ColGenIterationTestMasterResult) = res.term_status == ClB.INFEASIBLE ColGen.is_unbounded(res::ColGenIterationTestMasterResult) = res.term_status == ClB.UNBOUNDED ## mock of the master lp solver function ColGen.optimize_master_lp_problem!(master, ctx::ColGenIterationTestContext, env) obj_val = nothing primal_sol = nothing dual_sol = nothing if ctx.master_term_status == ClB.OPTIMAL obj_val = 22.5 primal_sol = [0, 0, 1/4, 0, 1/4, 1/4, 1/4] dual_sol = [151/8, -1, -11/2, -5/4, 0] end return ColGenIterationTestMasterResult(ctx.master_term_status, obj_val, primal_sol, dual_sol) end # Pricing struct ColGenIterationTestPricingResult term_status::ClB.TerminationStatus primal_sols::Vector{Vector{Float64}} primal_bound::Union{Nothing, Float64} dual_bound::Union{Nothing, Float64} end ColGen.get_primal_sols(res::ColGenIterationTestPricingResult) = res.primal_sols ColGen.get_primal_bound(res::ColGenIterationTestPricingResult) = res.primal_bound ColGen.get_dual_bound(res::ColGenIterationTestPricingResult) = res.dual_bound ColGen.compute_sp_init_db(::ColGenIterationTestContext, sp) = -Inf ColGen.compute_sp_init_pb(::ColGenIterationTestContext, sp) = Inf ColGen.set_of_columns(::ColGenIterationTestContext) = Vector{Float64}[] ColGen.is_infeasible(res::ColGenIterationTestPricingResult) = res.term_status == ClB.INFEASIBLE ColGen.is_unbounded(res::ColGenIterationTestPricingResult) = res.term_status == ClB.UNBOUNDED function ColGen.push_in_set!(ctx::ColGenIterationTestContext, set::Vector{Vector{Float64}}, col::Vector) push!(set, col) return true end ## mock of the pricing solver function ColGen.optimize_pricing_problem!(ctx::ColGenIterationTestContext, form, env, optimizer, master_dual_sol, stab_changes_mast_dual_sol) primal_val = nothing dual_val = nothing sols = Vector{Float64}[] if !ctx.pricing_solver_has_no_solution push!(sols, [0, 1, 1, 0, 1, 1, 0, 0, 1, 0]) primal_val = -23/4 end if ctx.pricing_has_correct_dual_bound dual_val = -23/4 elseif ctx.pricing_has_incorrect_dual_bound dual_val = -47 # this value is lower than the correct dual bound (minimization problem!!) else @assert ctx.pricing_has_no_dual_bound end return ColGenIterationTestPricingResult(ctx.pricing_term_status, sols, primal_val, dual_val) end # Reduced costs ColGen.get_subprob_var_orig_costs(::ColGenIterationTestContext) = [7, 2, 1, 5, 3, 6, 8, 4, 2, 9] ColGen.get_subprob_var_coef_matrix(::ColGenIterationTestContext) = [ 1 1 1 1 0 0 0 0 0 0; 1 0 0 0 1 1 1 0 0 0; 0 1 0 0 1 0 0 1 1 0; 0 0 1 0 0 1 0 1 0 1; 0 0 0 1 0 0 1 0 1 1 ] function ColGen.update_sp_vars_red_costs!(::ColGenIterationTestContext, subprob, red_costs) # We check that reduced costs are correct. @test reduce(&, red_costs .== [-87/8, -91/8, -133/8, -111/8, 19/2, 33/4, 9, 43/4, 15/2, 41/4]) return end ColGen.update_reduced_costs!(::ColGenIterationTestContext, phase, red_costs) = nothing function ColGen.check_primal_ip_feasibility!(sol, ctx::ColGenIterationTestContext, ::ColGenIterationTestPhase, env) if ctx.new_ip_primal_sol @assert !ctx.master_has_new_cuts return [7.0, 7.0, 7.0], false end if ctx.master_has_new_cuts @assert !ctx.new_ip_primal_sol return nothing, true end return nothing, false end ColGen.is_better_primal_sol(::Vector{Float64}, p) = isnothing(p.global_primal_sol) function ColGen.update_inc_primal_sol!(::ColGenIterationTestContext, sol::GlobalTestColGenIterationPrimalHandler, new_sol) if isnothing(sol.global_primal_sol) sol.global_primal_sol = new_sol end @test sol.global_primal_sol == [7.0, 7.0, 7.0] end ColGen.update_master_constrs_dual_vals!(::ColGenIterationTestContext, dual_mast_sol) = nothing function ColGen.insert_columns!(::ColGenIterationTestContext, phase, generated_columns) @test length(generated_columns) == 1 @test generated_columns[1] == [0, 1, 1, 0, 1, 1, 0, 0, 1, 0] return [1] end function ColGen.compute_dual_bound(::ColGenIterationTestContext, ::ColGenIterationTestPhase, sp_dbs, generated_columns, mast_dual_sol) return 22.5 - 23/4 end ColGen.update_stabilization_after_pricing_optim!(::Coluna.Algorithm.NoColGenStab, ::ColGenIterationTestContext, _, _, _, _) = nothing struct TestColGenIterationOutput <: ColGen.AbstractColGenIterationOutput min_sense::Bool mlp::Union{Nothing, Float64} db::Union{Nothing, Float64} nb_new_cols::Int new_cut_in_master::Bool infeasible_master::Bool unbounded_master::Bool infeasible_subproblem::Bool unbounded_subproblem::Bool time_limit_reached::Bool master_lp_primal_sol::Union{Nothing, Vector{Float64}} master_ip_primal_sol::Union{Nothing, Vector{Float64}} master_lp_dual_sol::Union{Nothing, Vector{Float64}} end ColGen.colgen_iteration_output_type(::ColGenIterationTestContext) = TestColGenIterationOutput function ColGen.new_iteration_output(::Type{<:TestColGenIterationOutput}, min_sense, mlp, db, nb_new_cols, new_cut_in_master, infeasible_master, unbounded_master, infeasible_subproblem, unbounded_subproblem, time_limit_reached, master_lp_primal_sol, master_ip_primal_sol, master_lp_dual_sol ) master_ip_primal_sol = isnothing(master_ip_primal_sol) ? nothing : master_ip_primal_sol.global_primal_sol return TestColGenIterationOutput( min_sense, mlp, db, nb_new_cols, new_cut_in_master, infeasible_master, unbounded_master, infeasible_subproblem, unbounded_subproblem, time_limit_reached, master_lp_primal_sol, master_ip_primal_sol, master_lp_dual_sol ) end ColGen.update_stabilization_after_pricing_optim!(::Coluna.Algorithm.NoColGenStab, ::TestColGenIterationContext, _, _, _, _) = nothing function colgen_iteration_master_ok_pricing_ok() ctx = ColGenIterationTestContext() output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test output.mlp == 22.5 @test output.db == 22.5 - 23/4 @test output.nb_new_cols == 1 @test output.new_cut_in_master == false @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_iteration_master_ok_pricing_ok) function colgen_iteration_master_infeasible() ctx = ColGenIterationTestContext( master_term_status = ClB.INFEASIBLE, master_solver_has_no_primal_solution = true, master_solver_has_no_dual_solution = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test isnothing(output.mlp) @test output.db == Inf @test output.nb_new_cols == 0 @test output.new_cut_in_master == false @test output.infeasible_master == true @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_iteration_master_infeasible) function colgen_iteration_pricing_infeasible() ctx = ColGenIterationTestContext( pricing_term_status = ClB.INFEASIBLE, pricing_solver_has_no_solution = true, pricing_has_no_dual_bound = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test isnothing(output.mlp) @test output.db == Inf @test output.nb_new_cols == 0 @test output.new_cut_in_master == false @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == true @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_iteration_pricing_infeasible) function colgen_iteration_master_unbounded() ctx = ColGenIterationTestContext( master_term_status = ClB.UNBOUNDED, master_solver_has_no_primal_solution = true, master_solver_has_no_dual_solution = true ) @test_throws ColGen.UnboundedProblemError ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) end register!(unit_tests, "colgen_iteration", colgen_iteration_master_unbounded) function colgen_iteration_pricing_unbounded() ctx = ColGenIterationTestContext( pricing_term_status = ClB.UNBOUNDED, pricing_solver_has_no_solution = true, pricing_has_no_dual_bound = true ) @test_throws ColGen.UnboundedProblemError ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) end register!(unit_tests, "colgen_iteration", colgen_iteration_pricing_unbounded) function colgen_finds_ip_primal_sol() ctx = ColGenIterationTestContext( new_ip_primal_sol = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test output.mlp == 22.5 @test output.db == 22.5 - 23/4 @test output.nb_new_cols == 1 @test output.new_cut_in_master == false @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test output.master_ip_primal_sol == [7.0, 7.0, 7.0] end register!(unit_tests, "colgen_iteration", colgen_finds_ip_primal_sol) function colgen_new_cuts_in_master() ctx = ColGenIterationTestContext( master_has_new_cuts = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test isnothing(output.mlp) @test isnothing(output.db) @test output.nb_new_cols == 0 @test output.new_cut_in_master == true @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_new_cuts_in_master)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	21645	struct TestColGenOutput <: ColGen.AbstractColGenPhaseOutput has_art_vars::Bool new_cuts_in_master::Bool exact_stage::Bool has_converged::Bool end ClA.colgen_master_has_new_cuts(ctx::TestColGenOutput) = ctx.new_cuts_in_master ClA.colgen_mast_lp_sol_has_art_vars(ctx::TestColGenOutput) = ctx.has_art_vars ClA.colgen_uses_exact_stage(ctx::TestColGenOutput) = ctx.exact_stage ClA.colgen_has_converged(ctx::TestColGenOutput) = ctx.has_converged # The two following tests are pretty straightforward. # They are just here to make sure nobody changes the behavior of the phases. function initial_phase_colgen_test() it = ClA.ColunaColGenPhaseIterator() @test ColGen.initial_phase(it) isa ClA.ColGenPhase0 end register!(unit_tests, "colgen_phase", initial_phase_colgen_test) function next_phase_colgen_test() # Classic case where we use exact phase and the algorithm has converged. it = ClA.ColunaColGenPhaseIterator() table = [ # Current phase \| art vars \| new cut \| exact stage \| converged \| next expected phase \| err \| err_type ( ClA.ColGenPhase1() , false , false , false , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , false , false , true , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , false , true , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , false , true , true , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , true , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , false , true , false , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , false , true , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , false , true , true , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , false , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , false , false , true , ClA.ColGenPhase1() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase1() , true , false , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , false , true , true , nothing , false , nothing ), # infeasible ( ClA.ColGenPhase1() , true , true , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , true , false , true , ClA.ColGenPhase1() , false , nothing ), # infeasible ( ClA.ColGenPhase1() , true , true , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , true , true , true , nothing , false , nothing ), # infeasible # Current phase \| art vars \| new cut \| exact stage \| converged \| next expected phase \| err \| err_type ( ClA.ColGenPhase2() , false , false , false , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase2() , false , false , false , true , ClA.ColGenPhase2() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase2() , false , false , true , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase2() , false , false , true , true , nothing , false , nothing ), # end of the column generation algorithm ( ClA.ColGenPhase2() , false , true , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , false , true , false , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , false , true , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , false , true , true , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , true , false , false , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , false , false , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , false , true , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , false , true , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , false , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , false , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , true , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , true , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen # Current phase \| art vars \| new cut \| exact stage \| converged \| next expected phase \| err \| err_type ( ClA.ColGenPhase0() , false , false , false , false , ClA.ColGenPhase0() , false , nothing ), # you should have converged but you may have hit another limit ( ClA.ColGenPhase0() , false , false , false , true , ClA.ColGenPhase0() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase0() , false , false , true , false , ClA.ColGenPhase0() , false , nothing ), # you should have converged but you may have hit another limit ( ClA.ColGenPhase0() , false , false , true , true , nothing , false , nothing ), # end of the column generation algorithm ( ClA.ColGenPhase0() , false , true , false , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , false , true , false , true , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , false , true , true , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , false , true , true , true , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , false , false , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , false , false , true , ClA.ColGenPhase0() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase0() , true , false , true , false , ClA.ColGenPhase1() , false , nothing ), # you should have converged but you may have hit another limit. Let's try phase 1. ( ClA.ColGenPhase0() , true , false , true , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase0() , true , true , false , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , true , false , true , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , true , true , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , true , true , true , ClA.ColGenPhase0() , false , nothing ), ] # Current phase \| art vars \| n dew cut \| exact stage \| converged \| next expected phase \| err \| err_type for (cp, art, cut, exact, conv, exp, err, err_type) in table if !err @test ColGen.next_phase(it, cp, TestColGenOutput(art, cut, exact, conv)) isa typeof(exp) else @test_throws err_type ColGen.next_phase(it, cp, TestColGenOutput(art, cut, exact, conv)) end end end register!(unit_tests, "colgen_phase", next_phase_colgen_test) function get_reform_master_and_vars() form_string1 = """ master min 3x1 + 4x2 + 1000z s.t. x1 + x2 + z >= 1 dw_sp min x1 + x2 integer representatives x1, x2 continuous artificial z """ _, master, _, _, reform = reformfromstring(form_string1) vars_by_name = Dict{String, ClMP.Variable}(ClMP.getname(master, var) => var for (_, var) in ClMP.getvars(master)) return reform, master, vars_by_name end function setup_reformulation_colgen_test() reform, master, vars_by_name = get_reform_master_and_vars() @test ClMP.getcurcost(master, vars_by_name["x1"]) == 3 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 4 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test ClMP.iscuractive(master, vars_by_name["z"]) ColGen.setup_reformulation!(reform, ClA.ColGenPhase1()) @test ClMP.getcurcost(master, vars_by_name["x1"]) == 0 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 0 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test ClMP.iscuractive(master, vars_by_name["z"]) # To make sure that reduced costs will be well calculated: helper = ClA.ReducedCostsCalculationHelper(master) @test helper.master_c[ClMP.getid(vars_by_name["x1"])] == 0 @test helper.master_c[ClMP.getid(vars_by_name["x2"])] == 0 reform, master, vars_by_name = get_reform_master_and_vars() ColGen.setup_reformulation!(reform, ClA.ColGenPhase2()) @test ClMP.getcurcost(master, vars_by_name["x1"]) == 3 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 4 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test !ClMP.iscuractive(master, vars_by_name["z"]) reform, master, vars_by_name = get_reform_master_and_vars() ColGen.setup_reformulation!(reform, ClA.ColGenPhase0()) @test ClMP.getcurcost(master, vars_by_name["x1"]) == 3 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 4 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test ClMP.iscuractive(master, vars_by_name["z"]) end register!(unit_tests, "colgen_phase", setup_reformulation_colgen_test) function test_gap() mlp_db_sense_closed = [ # min sense (250, 10, true, false), (250, 255, true, true), (250.1, 250.1, true, true), (250.11111, 250.111110, true, true), # max sense (250, 10, false, true), (250, 255, false, false), (250.1, 250.1, false, true), (250.11111, 250.111112, false, true) ] for (mlp, db, sense, closed) in mlp_db_sense_closed coeff = sense ? 1 : -1 # minimization @test ClA._colgen_gap_closed(coeff * mlp, coeff * db, 0.001, 0.001) == closed end end register!(unit_tests, "colgen_phase", test_gap) function stop_colgen_phase_if_colgen_converged_eq() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( false, Inf, 99.9998, 99.9999, 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_colgen_converged_eq) function stop_colgen_phase_if_colgen_converged_min() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, # min sense Inf, 99.9998, # mlp 100.12, # greater than mlp means colgen has converged 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_colgen_converged_min) function stop_colgen_phase_if_colgen_converged_max() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( false, # max sense Inf, 99.9998, # mlp 99.9, # lower than mlp means colgen has converged 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_colgen_converged_max) function stop_colgen_phase_if_iterations_limit() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration(max_nb_iterations = 8)) colgen_iteration = 8 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 65.87759, 29.869, 6, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_iterations_limit) function stop_colgen_phase_if_time_limit() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 65.87759, 29.869, 6, false, false, false, false, false, true, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_time_limit) function stop_colgen_phase_if_subproblem_infeasible() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, false, true, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_subproblem_infeasible) function stop_colgen_phase_if_subproblem_unbounded() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, false, false, true, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_subproblem_unbounded) function stop_colgen_phase_if_master_unbounded() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, true, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_master_unbounded) function stop_colgen_phase_if_no_new_column() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_no_new_column) function stop_colgen_phase_if_new_cut_in_master() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, true, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase0(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_new_cut_in_master) function continue_colgen_phase_otherwise() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test !ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", continue_colgen_phase_otherwise) function stop_when_inf_db() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 4578, Inf, 1, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_when_inf_db) function infeasible_phase_output() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_phase_output = ClA.ColGenPhaseOutput( nothing, nothing, nothing, nothing, 167673.9643, #mlp 162469.0291, #db false, true, true, #infeasible true, #exact_stage false, 6, true ) @test ColGen.stop_colgen(ctx, colgen_phase_output) colgen_output = ColGen.new_output(ClA.ColGenOutput, colgen_phase_output) @test colgen_output.infeasible == true @test isnothing(colgen_output.master_lp_primal_sol) @test isnothing(colgen_output.master_ip_primal_sol) @test isnothing(colgen_output.master_lp_dual_sol) @test_broken isnothing(colgen_output.mlp) @test_broken isnothing(colgen_output.db) master = ClA.getmaster(reform) optstate = ClA._colgen_optstate_output(colgen_output, master) @test optstate.termination_status == ClA.INFEASIBLE end register!(unit_tests, "colgen_phase", infeasible_phase_output)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	4177	function printer_colgen_iteration_master_ok_pricing_ok() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, 22.5, 22.5 - 23/4, 1, false, false, false, false, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> <mst= 1.23> <sp= 0.12> <cols= 1> <al= 0.12> <DB= 16.7500> <mlp= 22.5000> <PB=Inf>" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.12) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_master_ok_pricing_ok) function printer_colgen_iteration_master_infeasible() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, Inf, 0, false, true, false, false, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - infeasible master" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_master_infeasible) function printer_colgen_iteration_pricing_infeasible() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, Inf, 0, false, false, false, true, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - infeasible subproblem" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_pricing_infeasible) function printer_colgen_iteration_master_unbounded() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, -Inf, 0, false, false, true, false, false, false, nothing, nothing, nothing ) expected_str = "" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test_broken expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_master_unbounded) function printer_colgen_iteration_pricing_unbounded() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, nothing, 0, false, false, false, false, true, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - unbounded subproblem" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_pricing_unbounded) # function printer_colgen_finds_ip_primal_sol() # output = Coluna.Algorithm.ColGenIterationOutput( # true, # 22.5, # 22.5 - 23/4, # 1, # false, # false, # false, # false, # false, # false, # nothing, # [7.0, 7.0, 7.0] # ) # expected_str = "" # str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 0.12, 1.23, 2.34) # @show str # @test_broken expected_str == str # end # register!(unit_tests, "colgen_printer", printer_colgen_finds_ip_primal_sol) function printer_colgen_new_cuts_in_master() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, nothing, 0, true, false, false, false, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - new essential cut in master" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_new_cuts_in_master)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	39429	################################################################################ # Test the implementation of the stabilization procedure. ################################################################################ # Make sure the value of α is updated correctly after each misprice. # The goal is to tend to 0.0 after a given number of misprices. function test_misprice_schedule() smooth_factor = 1 base_α = 0.8 prev_α = 0.8 for nb_misprices in 1:11 α = Coluna.Algorithm._misprice_schedule(smooth_factor, nb_misprices, base_α) @test (prev_α > α) \|\| (iszero(α) && iszero(prev_α)) prev_α = α end return end register!(unit_tests, "colgen_stabilization", test_misprice_schedule) form_primal_solution() = """ master min 3x_11 + 2x_12 + 5x_13 + 2x_21 + x_22 + x_23 + 4y1 + 3y2 + z1 + z2 + 0s1 + 0s2 s.t. x_11 + x_12 + x_13 + x_21 + y1 + y2 + 2z1 + z2 >= 10 x_11 + 2x_12 + x_21 + 2x_22 + 3x_23 + y1 + 2y2 + z1 <= 100 x_11 + 3x_13 + x_22 + x_23 + y1 + == 100 y1 + + z1 + z2 <= 5 s1 >= 1 {MasterConvexityConstr} s1 <= 2 {MasterConvexityConstr} s2 >= 0 {MasterConvexityConstr} s2 <= 3 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + y1 + 0s1 s.t. x_11 + x_12 + x_13 + y1 >= 10 dw_sp min x_21 + x_22 + x_23 + y2 + 0s2 s.t. x_21 + x_22 + x_23 + y2 >= 10 integer representatives x_11, x_12, x_13, x_21, x_22, x_23, y1, y2 pure z1, z2 pricing_setup s1, s2 bounds x_11 >= 0 x_12 >= 0 x_13 >= 0 x_21 >= 0 x_22 >= 1 x_23 >= 0 1 <= y1 <= 2 3 <= y2 <= 6 z1 >= 0 z2 >= 3 """ function test_primal_solution() _, master, sps, _, _ = reformfromstring(form_primal_solution()) sp1, sp2 = sps[2], sps[1] vids = get_name_to_varids(master) cids = get_name_to_constrids(master) pool = Coluna.Algorithm.ColumnsSet() sol1 = Coluna.MathProg.PrimalSolution( sp1, [vids["x_11"], vids["x_12"], vids["x_13"], vids["y1"], vids["s1"]], [1.0, 2.0, 3.0, 7.0, 1.0], 11.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol1, false) # improving = true sol2 = Coluna.MathProg.PrimalSolution( sp2, [vids["x_21"], vids["x_22"], vids["x_23"], vids["y2"], vids["s2"]], [4.0, 4.0, 5.0, 10.0, 1.0], 13.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol2, true) primal_sol = Coluna.Algorithm._primal_solution(master, pool, true) sp1_lb = 1.0 sp2_ub = 3.0 @test primal_sol[vids["x_11"]] == 1.0 * sp1_lb @test primal_sol[vids["x_12"]] == 2.0 * sp1_lb @test primal_sol[vids["x_13"]] == 3.0 * sp1_lb @test primal_sol[vids["x_21"]] == 4.0 * sp2_ub @test primal_sol[vids["x_22"]] == 4.0 * sp2_ub @test primal_sol[vids["x_23"]] == 5.0 * sp2_ub @test primal_sol[vids["y1"]] == 7.0 * sp1_lb @test primal_sol[vids["y2"]] == 10.0 * sp2_ub @test primal_sol[vids["s1"]] == 1.0 * sp1_lb @test primal_sol[vids["s2"]] == 1.0 * sp2_ub @test primal_sol[vids["z1"]] == 0.0 # TODO: not sure about this test @test primal_sol[vids["z2"]] == 0.0 # TODO: not sure about this test return end register!(unit_tests, "colgen_stabilization", test_primal_solution) form_primal_solution2() = """ master min 3x_11 + 2x_12 + 2x_21 + x_22 + z1 + z2 + 0s1 + 0s2 s.t. x_11 + x_12 + x_21 + 2z1 + z2 >= 10 x_11 + 2x_12 + x_21 + 2x_22 + z1 <= 100 x_11 + x_22 + == 100 z1 + z2 >= 5 s1 >= 1 {MasterConvexityConstr} s1 <= 2 {MasterConvexityConstr} s2 >= 0 {MasterConvexityConstr} s2 <= 3 {MasterConvexityConstr} dw_sp min x_11 + x_12 + 0s1 s.t. x_11 + x_12 >= 10 dw_sp min x_21 + x_22 + 0s2 s.t. x_21 + x_22 >= 10 integer representatives x_11, x_12, x_21, x_22 pure z1, z2 pricing_setup s1, s2 bounds x_11 >= 0 x_12 >= 0 x_21 >= 0 x_22 >= 1 z1 >= 0 z2 >= 3 """ # We consider the master with the following coefficient matrix: # master_coeff_matrix = [ # 1 1 1 0 1 1; # -1 -2 -1 -2 -1 0; # 1 0 0 1 0 0; # is it correct to handle an "==" constraint like this in subgradient computation? # 0 0 0 0 1 1; # ] # the following rhs: rhs = [10, -100, 100, 5] # We consider the primal solution: primal = [1, 2, 12, 12, 0, 0] # The subgradient is therefore: rhs - master_coeff_matrix * primal = [-5, -59, 87, 5] # We use the following stability center: stab = [1, 2, 0, 1] function _test_angle_primal_sol(master, sp1, sp2) vids = get_name_to_varids(master) pool = Coluna.Algorithm.ColumnsSet() sol1 = Coluna.MathProg.PrimalSolution( sp1, [vids["x_11"], vids["x_12"]], [1.0, 2.0], 11.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol1, false) # improving = true sol2 = Coluna.MathProg.PrimalSolution( sp2, [vids["x_21"], vids["x_22"]], [4.0, 4.0], 13.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol2, true) is_minimization = true primal_sol = Coluna.Algorithm._primal_solution(master, pool, is_minimization) return primal_sol end function _test_angle_stab_center(master) cids = get_name_to_constrids(master) return Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [1.0, 2.0, 1.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) end function _data_for_dynamic_schedule_test() _, master, sps, _, _ = reformfromstring(form_primal_solution2()) sp1, sp2 = sps[2], sps[1] cids = get_name_to_constrids(master) cur_stab_center = _test_angle_stab_center(master) h = Coluna.Algorithm.SubgradientCalculationHelper(master) is_minimization = true primal_sol = _test_angle_primal_sol(master, sp1, sp2) return master, cur_stab_center, h, primal_sol, is_minimization end # Make sure the angle is well computed. # Here we test the can where the in and sep points are the same. # In that case, we should decrease the value of α. function test_angle_1() master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [1.0, 2.0, 1.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) increase = Coluna.Algorithm._increase(smooth_dual_sol, cur_stab_center, h, primal_sol, is_minimization) @test increase == false end register!(unit_tests, "colgen_stabilization", test_angle_1) # Let's consider the following sep point: sep = [5, 7, 0, 3] # The direction will be [4, 5, 0, 2] and should lead to a negative cosinus for the angle. # In that case, we need to increase the value of α. function test_angle_2() master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [5.0, 7.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) increase = Coluna.Algorithm._increase(smooth_dual_sol, cur_stab_center, h, primal_sol, is_minimization) @test increase == true end register!(unit_tests, "colgen_stabilization", test_angle_2) # Let's consider the following sep point: sep = [5, 1, 10, 3] # The direction will be [4, 1, 10, 2] and should lead to a positive cosinus for the angle. # In that case, we need to decrease the value of α. function test_angle_3() master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c3"], cids["c4"]], [5.0, 1.0, 10.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) increase = Coluna.Algorithm._increase(smooth_dual_sol, cur_stab_center, h, primal_sol, is_minimization) @test increase == false end register!(unit_tests, "colgen_stabilization", test_angle_3) function test_dynamic_alpha_schedule() for α in 0.1:0.1:0.9 @test Coluna.Algorithm.f_incr(α) > α @test Coluna.Algorithm.f_decr(α) < α end @test Coluna.Algorithm.f_incr(1.0) - 1.0 < 1e-3 @test Coluna.Algorithm.f_decr(0.0) < 1e-3 master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol_for_decrease = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c3"], cids["c4"]], [5.0, 1.0, 10.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) smooth_dual_sol_for_increase = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [5.0, 7.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) α = 0.8 @test α > Coluna.Algorithm._dynamic_alpha_schedule( α, smooth_dual_sol_for_decrease, cur_stab_center, h, primal_sol, is_minimization ) @test α < Coluna.Algorithm._dynamic_alpha_schedule( α, smooth_dual_sol_for_increase, cur_stab_center, h, primal_sol, is_minimization ) end register!(unit_tests, "colgen_stabilization", test_dynamic_alpha_schedule) ################################################################################ # Test to make sure the generic code works ################################################################################ # Mock implementation of the column generation to make sure the stabilization logic works # as expected. mutable struct ColGenStabFlowStab nb_misprice::Int64 nb_update_stab_after_master_done::Int64 nb_update_stab_after_pricing_done::Int64 nb_check_misprice::Int64 nb_misprices_done::Int64 nb_update_stab_after_iter_done::Int64 ColGenStabFlowStab(nb_misprice) = new(nb_misprice, 0, 0, 0, 0, 0) end struct ColGenStabFlowRes end struct ColGenStabFlowOutput end struct ColGenStabFlowDualSol end struct ColGenStabFlowPrimalSol end struct ColGenStabFlowPricingStrategy end mutable struct ColGenStabFlowCtx <: Coluna.ColGen.AbstractColGenContext nb_compute_dual_bound::Int64 ColGenStabFlowCtx() = new(0) end ColGen.get_master(::ColGenStabFlowCtx) = nothing ColGen.is_minimization(::ColGenStabFlowCtx) = true ColGen.optimize_master_lp_problem!(master, ctx::ColGenStabFlowCtx, env) = ColGenStabFlowRes() ColGen.colgen_iteration_output_type(::ColGenStabFlowCtx) = ColGenStabFlowOutput ColGen.is_infeasible(::ColGenStabFlowRes) = false ColGen.is_unbounded(::ColGenStabFlowRes) = false ColGen.get_dual_sol(::ColGenStabFlowRes) = ones(Float64, 3) ColGen.get_primal_sol(::ColGenStabFlowRes) = ColGenStabFlowPrimalSol() ColGen.get_obj_val(::ColGenStabFlowRes) = 0.0 ColGen.is_better_primal_sol(::ColGenStabFlowPrimalSol, p) = false ColGen.get_reform(::ColGenStabFlowCtx) = nothing ColGen.update_master_constrs_dual_vals!(::ColGenStabFlowCtx, dual_sol) = nothing ColGen.get_subprob_var_orig_costs(::ColGenStabFlowCtx) = ones(Float64, 3) ColGen.get_subprob_var_coef_matrix(::ColGenStabFlowCtx) = ones(Float64, 3, 3) ColGen.update_reduced_costs!(::ColGenStabFlowCtx, phase, red_costs) = nothing function ColGen.update_stabilization_after_master_optim!(stab::ColGenStabFlowStab, phase, mast_dual_sol) stab.nb_update_stab_after_master_done += 1 return true end ColGen.get_stab_dual_sol(stab::ColGenStabFlowStab, phase, mast_dual) = [0.5, 0.5, 0.5] ColGen.set_of_columns(::ColGenStabFlowCtx) = [] ColGen.get_pricing_subprobs(::ColGenStabFlowCtx) = [] ColGen.get_pricing_strategy(::ColGenStabFlowCtx, phase) = ColGenStabFlowPricingStrategy() ColGen.pricing_strategy_iterate(::ColGenStabFlowPricingStrategy) = nothing ColGen.compute_dual_bound(ctx::ColGenStabFlowCtx, phase, bounds, generated_columns, mast_dual_sol) = ctx.nb_compute_dual_bound += 1 function ColGen.update_stabilization_after_pricing_optim!(stab::ColGenStabFlowStab, ctx, generated_columns, master, pseudo_db, smooth_dual_sol) @test smooth_dual_sol == [0.5, 0.5, 0.5] # we need the out point in this method. stab.nb_update_stab_after_pricing_done += 1 return true end function ColGen.check_misprice(stab::ColGenStabFlowStab, cols, mast_dual_sol) @test mast_dual_sol == [1.0, 1.0, 1.0] # we need the out point in this method. stab.nb_check_misprice += 1 return stab.nb_check_misprice <= stab.nb_misprice end function ColGen.update_stabilization_after_misprice!(stab::ColGenStabFlowStab, mast_dual_sol) @test mast_dual_sol == [1.0, 1.0, 1.0] # we need the out point in this method. stab.nb_misprices_done += 1 end function ColGen.insert_columns!(context::ColGenStabFlowCtx, phase, generated_columns) return [] end function ColGen.update_stabilization_after_iter!(stab::ColGenStabFlowStab, mast_dual_sol) @test mast_dual_sol == [1.0, 1.0, 1.0] # we need the out point in this method. stab.nb_update_stab_after_iter_done += 1 return true end ColGen.new_iteration_output(::Type{<:ColGenStabFlowOutput}, args...) = nothing function test_stabilization_flow_no_misprice() ctx = ColGenStabFlowCtx() phase = nothing stage = nothing env = nothing ip_primal_sol = nothing stab = ColGenStabFlowStab(0) res = Coluna.ColGen.run_colgen_iteration!(ctx, phase, stage, env, ip_primal_sol, stab) @test stab.nb_check_misprice == 1 @test stab.nb_misprices_done == 0 @test stab.nb_update_stab_after_iter_done == 1 @test stab.nb_update_stab_after_master_done == 1 @test stab.nb_update_stab_after_pricing_done == 1 end register!(unit_tests, "colgen_stabilization", test_stabilization_flow_no_misprice) function test_stabilization_flow_with_misprice() ctx = ColGenStabFlowCtx() phase = nothing stage = nothing env = nothing ip_primal_sol = nothing stab = ColGenStabFlowStab(10) res = Coluna.ColGen.run_colgen_iteration!(ctx, phase, stage, env, ip_primal_sol, stab) @test stab.nb_check_misprice == 10 + 1 @test stab.nb_misprices_done == 10 @test stab.nb_update_stab_after_iter_done == 1 @test stab.nb_update_stab_after_master_done == 1 @test stab.nb_update_stab_after_pricing_done == 10 + 1 end register!(unit_tests, "colgen_stabilization", test_stabilization_flow_with_misprice) ################################################################################ # ################################################################################ function min_toy_gap_for_stab() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function max_toy_gap_for_stab() form = """ master max -100.0 local_art_of_cov_5 - 100.0 local_art_of_cov_4 - 100.0 local_art_of_cov_6 - 100.0 local_art_of_cov_7 - 100.0 local_art_of_cov_2 - 100.0 local_art_of_cov_3 - 100.0 local_art_of_cov_1 - 100.0 local_art_of_sp_lb_5 - 100.0 local_art_of_sp_ub_5 - 100.0 local_art_of_sp_lb_4 - 100.0 local_art_of_sp_ub_4 - 1000.0 global_pos_art_var - 1000.0 global_neg_art_var - 800.0 x_11 - 500.0 x_12 - 1100.0 x_13 - 2100.0 x_14 - 600.0 x_15 - 500.0 x_16 - 1900.0 x_17 - 100.0 x_21 - 1200.0 x_22 - 1100.0 x_23 - 1200.0 x_24 - 1400.0 x_25 - 800.0 x_26 - 500.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp max -800.0 x_11 - 500.0 x_12 - 1100.0 x_13 - 2100.0 x_14 - 600.0 x_15 - 500.0 x_16 - 1900.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp max -100.0 x_21 - 1200.0 x_22 - 1100.0 x_23 - 1200.0 x_24 - 1400.0 x_25 - 800.0 x_26 - 500.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_min_with_penalties_for_stab() form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 0.0 <= y_1 <= 1.0 0.0 <= y_2 <= 1.0 0.0 <= y_3 <= 1.0 0.0 <= y_4 <= 1.0 0.0 <= y_5 <= 1.0 0.0 <= y_6 <= 1.0 0.0 <= y_7 <= 1.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_max_with_penalties_for_stab() form = """ master max - 3.15 y_1 - 5.949999999999999 y_2 - 7.699999999999999 y_3 - 11.549999999999999 y_4 - 7.0 y_5 - 4.55 y_6 - 8.399999999999999 y_7 - 10000.0 local_art_of_cov_5 - 10000.0 local_art_of_cov_4 - 10000.0 local_art_of_cov_6 - 10000.0 local_art_of_cov_7 - 10000.0 local_art_of_cov_2 - 10000.0 local_art_of_limit_pen - 10000.0 local_art_of_cov_3 - 10000.0 local_art_of_cov_1 - 10000.0 local_art_of_sp_lb_5 - 10000.0 local_art_of_sp_ub_5 - 10000.0 local_art_of_sp_lb_4 - 10000.0 local_art_of_sp_ub_4 - 100000.0 global_pos_art_var - 100000.0 global_neg_art_var - 8.0 x_11 - 5.0 x_12 - 11.0 x_13 - 21.0 x_14 - 6.0 x_15 - 5.0 x_16 - 19.0 x_17 - 1.0 x_21 - 12.0 x_22 - 11.0 x_23 - 12.0 x_24 - 14.0 x_25 - 8.0 x_26 - 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp max - 8.0 x_11 - 5.0 x_12 - 11.0 x_13 - 21.0 x_14 - 6.0 x_15 - 5.0 x_16 - 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp max - 1.0 x_21 - 12.0 x_22 - 11.0 x_23 - 12.0 x_24 - 14.0 x_25 - 8.0 x_26 - 5.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 0.0 <= y_1 <= 1.0 0.0 <= y_2 <= 1.0 0.0 <= y_3 <= 1.0 0.0 <= y_4 <= 1.0 0.0 <= y_5 <= 1.0 0.0 <= y_6 <= 1.0 0.0 <= y_7 <= 1.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_stabilization_min_automatic() env, master, sps, reform = min_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 1.0 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 7033.3333333 @test output.db ≈ 7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_min_automatic) function test_stabilization_max_automatic() env, master, sps, reform = max_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 1.0 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -7033.3333333 @test output.db ≈ -7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_max_automatic) function test_stabilization_min() env, master, sps, reform = min_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 7033.3333333 @test output.db ≈ 7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_min) function test_stabilization_max() env, master, sps, reform = max_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -7033.3333333 @test output.db ≈ -7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_max) function test_stabilization_pure_master_vars_min() env, master, sps, reform = toy_gap_min_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 52.95 @test output.db ≈ 52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_min) function test_stabilization_pure_master_vars_min_automatic() env, master, sps, reform = toy_gap_min_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 1.0 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 52.95 @test output.db ≈ 52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_min_automatic) function test_stabilization_pure_master_vars_max() env, master, sps, reform = toy_gap_max_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -52.95 @test output.db ≈ -52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_max) function test_stabilization_pure_master_vars_max_automatic() env, master, sps, reform = toy_gap_max_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -52.95 @test output.db ≈ -52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_max_automatic)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	4506	ClMP.getuid(i::Int) = i # hack avoid creating formulations for the tests. function test_colgen_stage_iterator() nb_optimizers_per_pricing_prob = Dict(1 => [1, 2, 3], 2 => [1, 2], 3 => [1, 2, 3, 4]) it = Coluna.Algorithm.ColGenStageIterator(4, nb_optimizers_per_pricing_prob) stage = ColGen.initial_stage(it) @test ColGen.stage_id(stage) == 4 @test !ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 3 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 2 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 4 stage = ColGen.decrease_stage(it, stage) @test ColGen.stage_id(stage) == 3 @test !ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 2 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 3 stage = ColGen.decrease_stage(it, stage) @test ColGen.stage_id(stage) == 2 @test !ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 2 stage = ColGen.decrease_stage(it, stage) @test ColGen.stage_id(stage) == 1 @test ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 1 stage = ColGen.decrease_stage(it, stage) @test isnothing(stage) end register!(unit_tests, "colgen_stage", test_colgen_stage_iterator) struct TestStageColGenPhaseOutput <: ColGen.AbstractColGenPhaseOutput new_cuts_in_master::Bool no_new_cols::Bool has_converged::Bool end ClA.colgen_master_has_new_cuts(output::TestStageColGenPhaseOutput) = output.new_cuts_in_master ClA.colgen_has_no_new_cols(output::TestStageColGenPhaseOutput) = output.no_new_cols ClA.colgen_has_converged(output::TestStageColGenPhaseOutput) = output.has_converged function test_colgen_next_stage() nb_optimizers_per_pricing_prob = Dict(1 => [1, 2, 3], 2 => [1, 2], 3 => [1, 2, 3, 4]) it = Coluna.Algorithm.ColGenStageIterator(4, nb_optimizers_per_pricing_prob) # ColGen.next_stage always returns the same stage, the next in the decreasing sequence, # or the initial stage. cur_stage = ColGen.initial_stage(it) # 4 heur_stage = ColGen.decrease_stage(it, cur_stage) # 3 cur_stage = ColGen.decrease_stage(it, heur_stage) # 2 exact_stage = ColGen.decrease_stage(it, cur_stage) # 1 @test ColGen.stage_id(heur_stage) == 3 @test !ColGen.is_exact_stage(heur_stage) @test ColGen.stage_id(exact_stage) == 1 @test ColGen.is_exact_stage(exact_stage) table = [ # stage_id \| new cut \| no more col\| conv \| next stage ( 3 , false , false , false , 3 ), # other limit ( 3 , false , false , true , 3 ), # impossible in theory ( 3 , false , true , false , 2 ), ( 3 , false , true , true , 3 ), ( 3 , true , false , false , 4 ), ( 3 , true , false , true , 4 ), # impossible in theory ( 3 , true , true , false , 4 ), ( 3 , true , true , true , 4 ), # stage_id \| new cut \| no more col\| conv \| next stage ( 1 , false , false , false , 1 ), # other limit ( 1 , false , false , true , 1 ), # impossible in theory ( 1 , false , true , false , nothing ), ( 1 , false , true , true , 1 ), ( 1 , true , false , false , 4 ), ( 1 , true , false , true , 4 ), # impossible in theory ( 1 , true , true , false , 4 ), ( 1 , true , true , true , 4 ), ] for (cur_st_id, cut, no_more_col, conv, next_st_id) in table stage = cur_st_id == 3 ? heur_stage : exact_stage next_stage = ColGen.next_stage(it, stage, TestStageColGenPhaseOutput(cut, no_more_col, conv)) if isnothing(next_st_id) @test isnothing(next_stage) else @test ColGen.stage_id(next_stage) == next_st_id end end end register!(unit_tests, "colgen_stage", test_colgen_next_stage)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	1886	function hash_table_1() x = 'A' y = 'B' z = 'C' col1 = 1 sol1 = [(x, 1.0), (z, 2.0)] col2 = 2 sol2 = [(x, 2.0), (y, -1.0)] col3 = 3 sol3 = [(y, -1.0), (z, 3.0)] col4 = 4 sol4 = [(x, 1.0)] col5 = 5 sol5 = [(z, -2.0), (y, 1.0)] ht = ClB.HashTable{Char,Int}() ClB.savesolid!(ht, col1, sol1) ClB.savesolid!(ht, col2, sol2) ClB.savesolid!(ht, col3, sol3) ClB.savesolid!(ht, col4, sol4) ClB.savesolid!(ht, col5, sol5) @test ClB.getsolids(ht, sol1) == [col1] @test ClB.getsolids(ht, sol2) == [col2] @test ClB.getsolids(ht, sol3) == [col3, col5] @test ClB.getsolids(ht, sol4) == [col4] @test ClB.getsolids(ht, sol5) == [col3, col5] end register!(unit_tests, "hashtable", hash_table_1) # Same test as "hash table 1" but we use VarIds from Coluna. function hash_table_2() x = ClMP.VarId(ClMP.OriginalVar, 1, 1) y = ClMP.VarId(ClMP.OriginalVar, 2, 1) z = ClMP.VarId(ClMP.OriginalVar, 3, 1) col1 = ClMP.VarId(ClMP.MasterCol, 4, 2) sol1 = [(x, 1.0), (z, 2.0)] col2 = ClMP.VarId(ClMP.MasterCol, 5, 2) sol2 = [(x, 2.0), (y, -1.0)] col3 = ClMP.VarId(ClMP.MasterCol, 6, 2) sol3 = [(y, -1.0), (z, 3.0)] col4 = ClMP.VarId(ClMP.MasterCol, 7, 2) sol4 = [(x, 1.0)] col5 = ClMP.VarId(ClMP.MasterCol, 8, 2) sol5 = [(z, -2.0), (y, 1.0)] ht = ClB.HashTable{ClMP.VarId, ClMP.VarId}() ClB.savesolid!(ht, col1, sol1) ClB.savesolid!(ht, col2, sol2) ClB.savesolid!(ht, col3, sol3) ClB.savesolid!(ht, col4, sol4) ClB.savesolid!(ht, col5, sol5) @test ClB.getsolids(ht, sol1) == [col1] @test ClB.getsolids(ht, sol2) == [col2] @test ClB.getsolids(ht, sol3) == [col3, col5] @test ClB.getsolids(ht, sol4) == [col4] @test ClB.getsolids(ht, sol5) == [col3, col5] end register!(unit_tests, "hashtable", hash_table_2)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	594	ClB.@nestedenum begin VarConstrDuty A <= VarConstrDuty A1 <= A A2 <= A A3 <= A B <= VarConstrDuty B1 <= B B2 <= B B3 <= B B3A <= B3 B3B <= B3 B3C <= B3 C <= VarConstrDuty D <= VarConstrDuty D1 <= D D1A <= D1 D1B <= D1 D2 <= D E <= VarConstrDuty end function nested_enum() @test <=(A1, A) @test A1 <= A @test !<=(A, B) @test !(A <= B) @test <=(B3A, B) @test B3A <= B end register!(unit_tests, "nestedenum", nested_enum)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	11413	struct FakeModel <: ClB.AbstractModel id::Int end FakeModel() = FakeModel(1) const Solution = ClB.Solution{FakeModel,Int,Float64} function constructor() # Make sure that Coluna initializes bounds to infinity. # Check that initial value of the bound is correct. primal = true min = true dual = false max = false pb = ClB.Bound(primal, min) @test pb == Inf @test ClB.getvalue(pb) == Inf pb = ClB.Bound(primal, max) @test pb == -Inf @test ClB.getvalue(pb) == -Inf db = ClB.Bound(dual, min) @test db == -Inf @test ClB.getvalue(db) == -Inf db = ClB.Bound(dual, max) @test db == Inf @test ClB.getvalue(db) == Inf pb = ClB.Bound(primal, min, 100) @test pb == 100 @test ClB.getvalue(pb) == 100 db = ClB.Bound(dual, min, -π) @test db == -π @test ClB.getvalue(db) == -π end register!(unit_tests, "bounds", constructor) function isbetter() primal = true min = true dual = false max = false # In minimization, pb with value 10 is better than pb with value 15 pb1 = ClB.Bound(primal, min, 10.0) pb2 = ClB.Bound(primal, min, 15.0) @test ClB.isbetter(pb1, pb2) == !ClB.isbetter(pb2, pb1) == true # In maximization, pb with value 15 is better than pb with value 10 pb1 = ClB.Bound(primal, max, 10.0) pb2 = ClB.Bound(primal, max, 15.0) @test ClB.isbetter(pb2, pb1) == !ClB.isbetter(pb1, pb2) == true # In minimization, db with value 15 is better than db with value 10 db1 = ClB.Bound(dual, min, 15.0) db2 = ClB.Bound(dual, min, 10.0) @test ClB.isbetter(db1, db2) == !ClB.isbetter(db2, db1) == true # In maximization, db with value 10 is better than db with value 15 db1 = ClB.Bound(dual, max, 15.0) db2 = ClB.Bound(dual, max, 10.0) @test ClB.isbetter(db2, db1) == !ClB.isbetter(db1, db2) == true # Cannot compare a primal & a dual bound db1 = ClB.Bound(dual, max, -10.0) @test_throws AssertionError ClB.isbetter(db1, pb1) # Cannot compare a bound from maximization & a bound from minimization db2 = ClB.Bound(dual, min, 10.0) @test_throws AssertionError ClB.isbetter(db1, db2) end register!(unit_tests, "bounds", isbetter) function diff() primal = true min = true dual = false max = false # Compute distance between primal bound and dual bound # In minimization, if pb = 10 & db = 5, distance is 5 pb = ClB.Bound(min, primal, 10) db = ClB.Bound(min, dual, 5) @test ClB.diff(pb, db) == ClB.diff(db, pb) == 5 # In maximisation if pb = 10 & db = 5, distance is -5 pb = ClB.Bound(max, primal, 10) db = ClB.Bound(max, dual, 5) @test ClB.diff(pb, db) == ClB.diff(db, pb) == -5 # Cannot compute the distance between two primal bounds pb1 = ClB.Bound(max, primal, 10) pb2 = ClB.Bound(max, primal, 15) @test_throws AssertionError ClB.diff(pb1, pb2) # Cannot compute the distance between two dual bounds db1 = ClB.Bound(max, dual, 5) db2 = ClB.Bound(max, dual, 50) @test_throws AssertionError ClB.diff(db1, db2) # Cannot compute the distance between two bounds from different sense pb = ClB.Bound(max, primal, 10) db = ClB.Bound(min, dual, 5) @test_throws AssertionError ClB.diff(pb, db) end register!(unit_tests, "bounds", diff) function gap() primal = true min = true dual = false max = false # In minimisation, gap = (pb - db)/db pb = ClB.Bound(min, primal, 10.0) db = ClB.Bound(min, dual, 5.0) @test ClB.gap(pb, db) == ClB.gap(db, pb) == (10.0-5.0)/5.0 # In maximisation, gap = (db - pb)/pb pb = ClB.Bound(max, primal, 5.0) db = ClB.Bound(max, dual, 10.0) @test ClB.gap(pb, db) == ClB.gap(db, pb) == (10.0-5.0)/5.0 pb = ClB.Bound(min, primal, 10.0) db = ClB.Bound(min, dual, -5.0) @test ClB.gap(pb, db) == ClB.gap(db, pb) == (10.0+5.0)/5.0 # Cannot compute the gap between 2 primal bounds pb1 = ClB.Bound(max, primal, 10) pb2 = ClB.Bound(max, primal, 15) @test_throws AssertionError ClB.gap(pb1, pb2) # Cannot compute the gap between 2 dual bounds db1 = ClB.Bound(max, dual, 5) db2 = ClB.Bound(max, dual, 50) @test_throws AssertionError ClB.gap(db1, db2) # Cannot compute the gap between 2 bounds with different sense pb = ClB.Bound(max, primal, 10) db = ClB.Bound(min, dual, 5) @test_throws AssertionError ClB.gap(pb, db) end register!(unit_tests, "bounds", gap) function printbounds() primal = true min = true dual = false max = false # In minimisation sense pb1 = ClB.Bound(min, primal, 100) db1 = ClB.Bound(min, dual, -100) io = IOBuffer() ClB.printbounds(db1, pb1, io) @test String(take!(io)) == "[ -100.0000 , 100.0000 ]" # In maximisation sense pb2 = ClB.Bound(max, primal, -100) db2 = ClB.Bound(max, dual, 100) io = IOBuffer() ClB.printbounds(db2, pb2, io) @test String(take!(io)) == "[ -100.0000 , 100.0000 ]" end register!(unit_tests, "bounds", printbounds) function show_test() primal = true max = false pb = ClB.Bound(max, primal, 4) io = IOBuffer() show(io, pb) @test String(take!(io)) == "4.0" end register!(unit_tests, "bounds", show_test) function promotions_and_conversion() primal = true max = false pb = ClB.Bound(max, primal, 4.0) @test eltype(promote(pb, 1)) == Real @test eltype(promote(pb, 2.0)) == typeof(2.0) @test eltype(promote(pb, π)) == Float64 @test eltype(promote(pb, 1, 2.0, π)) == Float64 @test promote_rule(eltype(pb), Integer) == Integer @test promote_rule(eltype(pb), Float64) == Float64 @test promote_rule(eltype(pb), Irrational) == Irrational @test typeof(pb + 1) == Float64 # check that promotion works @test convert(Float64, pb) == pb.value @test convert(Integer, pb) == pb.value @test convert(Irrational, pb) == pb.value end register!(unit_tests, "bounds", promotions_and_conversion) function convert_MOI_Coluna_termination_statuses() statuses_bijection = [ (MOI.OPTIMIZE_NOT_CALLED, ClB.OPTIMIZE_NOT_CALLED), (MOI.OPTIMAL, ClB.OPTIMAL), (MOI.INFEASIBLE, ClB.INFEASIBLE), (MOI.DUAL_INFEASIBLE, ClB.UNBOUNDED), (MOI.INFEASIBLE_OR_UNBOUNDED, ClB.UNBOUNDED), (MOI.TIME_LIMIT, ClB.TIME_LIMIT), (MOI.NODE_LIMIT, ClB.NODE_LIMIT), (MOI.OTHER_LIMIT, ClB.OTHER_LIMIT), ] statuses_surjection = [ (MOI.ALMOST_OPTIMAL, ClB.UNCOVERED_TERMINATION_STATUS), (MOI.SLOW_PROGRESS, ClB.UNCOVERED_TERMINATION_STATUS), (MOI.MEMORY_LIMIT, ClB.UNCOVERED_TERMINATION_STATUS), (MOI.ALMOST_OPTIMAL, ClB.UNCOVERED_TERMINATION_STATUS) ] for (moi_status, coluna_status) in statuses_bijection @test ClB.convert_status(moi_status) == coluna_status @test ClB.convert_status(coluna_status) == moi_status end for (moi_status, coluna_status) in statuses_surjection @test ClB.convert_status(moi_status) == coluna_status @test ClB.convert_status(coluna_status) == MOI.OTHER_LIMIT end end register!(unit_tests, "convert_MOI_Coluna", convert_MOI_Coluna_termination_statuses) function convert_MOI_Coluna_result_statuses() @test ClB.convert_status(MOI.NO_SOLUTION) == ClB.UNKNOWN_SOLUTION_STATUS @test ClB.convert_status(MOI.FEASIBLE_POINT) == ClB.FEASIBLE_SOL @test ClB.convert_status(MOI.INFEASIBLE_POINT) == ClB.INFEASIBLE_SOL @test ClB.convert_status(MOI.NEARLY_FEASIBLE_POINT) == ClB.UNCOVERED_SOLUTION_STATUS end register!(unit_tests, "convert_MOI_Coluna", convert_MOI_Coluna_result_statuses) function convert_MOI_Coluna_termination_statuses() @test ClB.convert_status(ClB.FEASIBLE_SOL) == MOI.FEASIBLE_POINT @test ClB.convert_status(ClB.INFEASIBLE_SOL) == MOI.INFEASIBLE_POINT @test ClB.convert_status(ClB.UNCOVERED_SOLUTION_STATUS) == MOI.OTHER_RESULT_STATUS end register!(unit_tests, "convert_MOI_Coluna", convert_MOI_Coluna_termination_statuses) function solution_factory(nbdecisions) decisions = Set{Int}() i = 0 while i < nbdecisions v = rand(rng, 1:100) if v ∉ decisions push!(decisions, v) i += 1 end end dict = Dict{Int, Float64}() soldecisions = Vector{Int}() solvals = Vector{Float64}() for d in decisions val = rand(rng, 0:0.0001:1000) dict[d] = val push!(soldecisions, d) push!(solvals, val) end return dict, soldecisions, solvals end function test_solution_iterations(solution::ClB.Solution, dict::Dict) prev_decision = nothing for (decision, value) in solution if prev_decision !== nothing @test prev_decision < decision end @test solution[decision] == dict[decision] solution[decision] += 1 @test solution[decision] == dict[decision] + 1 end return end function solution_constructor_iterate_print() model = FakeModel() dict_sol, soldecs, solvals = solution_factory(100) primal_sol = Solution(model, soldecs, solvals, 12.3, ClB.FEASIBLE_SOL) test_solution_iterations(primal_sol, dict_sol) @test ClB.getvalue(primal_sol) == 12.3 @test ClB.getstatus(primal_sol) == ClB.FEASIBLE_SOL dict_sol = Dict(1 => 2.0, 2 => 3.0, 3 => 4.0) primal_sol = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 0.0, ClB.FEASIBLE_SOL) @test length(primal_sol) == typemax(Coluna.MAX_NB_ELEMS) @test nnz(primal_sol) == 3 @test primal_sol[1] == 2.0 primal_sol[1] = 5.0 # change the value @test primal_sol[1] == 5.0 io = IOBuffer() show(io, primal_sol) @test String(take!(io)) == "Solution\n\| 1 = 5.0\n\| 2 = 3.0\n\| 3 = 4.0\n└ value = 0.00 \n" end register!(unit_tests, "solution", solution_constructor_iterate_print) function solution_isequal() model = FakeModel() model2 = FakeModel(2) dict_sol = Dict(1 => 2.0, 2 => 5.0, 3 => 8.0, 9 => 15.0) dict_sol2 = Dict(1 => 2.0, 2 => 5.0, 3 => 7.0, 9 => 15.0) # key 3 has different value dict_sol3 = Dict(1 => 2.0, 2 => 5.0, 3 => 8.0, 9 => 15.0, 10 => 11.0) # new key 10 dict_sol4 = Dict(1 => 2.0, 2 => 5.0, 3 => 8.0) # missing key 9 sol1 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.FEASIBLE_SOL) sol2 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.FEASIBLE_SOL) sol3 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 15.0, ClB.FEASIBLE_SOL) sol4 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.INFEASIBLE_SOL) sol5 = Solution(model2, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.FEASIBLE_SOL) sol6 = Solution(model, collect(keys(dict_sol2)), collect(values(dict_sol2)), 12.0, ClB.FEASIBLE_SOL) sol7 = Solution(model, collect(keys(dict_sol3)), collect(values(dict_sol3)), 12.0, ClB.FEASIBLE_SOL) sol8 = Solution(model, collect(keys(dict_sol4)), collect(values(dict_sol4)), 12.0, ClB.FEASIBLE_SOL) @test sol1 == sol2 @test sol1 != sol3 # different cost @test sol1 != sol4 # different solution status @test sol1 != sol5 # different model @test sol1 != sol6 # different solution @test sol1 != sol7 @test sol1 != sol8 end register!(unit_tests, "solution", solution_isequal)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	1744	# Storage units keeps records of a given data structure. const NB_VARS_CS1 = 6 struct ModelCs1 <: ClB.AbstractModel char_values::Vector{Char} tracked_char_pos::Vector{Int} end function Base.show(io::IO, model::ModelCs1) # TODO remove print(io, model.char_values) end struct CharStorageUnitCs1 <: ClB.AbstractRecordUnit end ClB.storage_unit(::Type{CharStorageUnitCs1}, _) = CharStorageUnitCs1() struct CharRecordCs1 <: ClB.AbstractRecord id::Int char_values::Dict{Int, Char} end ClB.get_id(r::CharRecordCs1) = r.id ClB.record_type(::Type{CharStorageUnitCs1}) = CharRecordCs1 ClB.storage_unit_type(::Type{CharRecordCs1}) = CharStorageUnitCs1 function ClB.record(::Type{CharRecordCs1}, id::Int, model::ModelCs1, ::CharStorageUnitCs1) entries_it = Iterators.filter( t -> t[1] ∈ model.tracked_char_pos, Iterators.map(t -> (t[1] => t[2]), Iterators.enumerate(model.char_values)) ) return CharRecordCs1(id, Dict{Int,Char}(collect(entries_it))) end function ClB.restore_from_record!( model::ModelCs1, ::CharStorageUnitCs1, record::CharRecordCs1 ) for (pos, char) in record.char_values model.char_values[pos] = char end return end function storage() model = ModelCs1(fill('A', NB_VARS_CS1), [3,4,5]) storage = ClB.Storage(model) r1 = ClB.create_record(storage, CharStorageUnitCs1) # create_record -> save_current_state a = ClB.restore_from_record!(storage, r1) model.char_values[3] = 'B' r2 = ClB.create_record(storage, CharStorageUnitCs1) ClB.restore_from_record!(storage, r1) @test model.char_values[3] == 'A' ClB.restore_from_record!(storage, r2) @test model.char_values[3] == 'B' end register!(unit_tests, "storage", storage)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	8343	function benders_decomposition() """ original min x1 + 4x2 + 2y1 + 3y2 s.t. x1 + x2 >= 0 - x1 + 3x2 - y1 + 2y2 >= 2 x1 + 3x2 + y1 + y2 >= 3 y1 + y2 >= 0 continuous original y1, y2 integer original x1, x2 bounds x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) origform = Coluna.MathProg.create_formulation!( env, Coluna.MathProg.Original() ) # Variables vars = Dict{String, Coluna.MathProg.VarId}() variables_infos = [ ("x1", 1.0, Coluna.MathProg.Integ), ("x2", 4.0, Coluna.MathProg.Integ), ("y1", 2.0, Coluna.MathProg.Continuous), ("y2", 3.0, Coluna.MathProg.Continuous) ] for (name, cost, kind) in variables_infos vars[name] = Coluna.MathProg.getid(Coluna.MathProg.setvar!( origform, name, Coluna.MathProg.OriginalVar; cost = cost, lb = 0.0, kind = kind )) end # Constraints constrs = Dict{String, Coluna.MathProg.ConstrId}() constraints_infos = [ ("c1", 2.0, Coluna.MathProg.Greater, Dict(vars["x1"] => -1.0, vars["x2"] => 4.0, vars["y1"] => 2.0, vars["y2"] => 3.0)), ("c2", 3.0, Coluna.MathProg.Greater, Dict(vars["x1"] => 1.0, vars["x2"] => 3.0, vars["y1"] => 1.0, vars["y2"] => 1.0)), ("c3", 0.0, Coluna.MathProg.Greater, Dict(vars["x1"] => 1.0, vars["x2"] => 1.0)), ("c4", 0.0, Coluna.MathProg.Greater, Dict(vars["y1"] => 1.0, vars["y2"] => 1.0)) ] for (name, rhs, sense, members) in constraints_infos constrs[name] = Coluna.MathProg.getid(Coluna.MathProg.setconstr!( origform, name, Coluna.MathProg.OriginalConstr; rhs = rhs, sense = sense, members = members )) end # Decomposition tree m = JuMP.Model() BlockDecomposition.@axis(axis, [1]) tree = BlockDecomposition.Tree(m, BlockDecomposition.Benders, axis) mast_ann = tree.root.master sp_ann = BlockDecomposition.Annotation(tree, BlockDecomposition.BendersSepSp, BlockDecomposition.Benders, []) BlockDecomposition.create_leaf!(BlockDecomposition.getroot(tree), axis[1], sp_ann) # Benders annotations ann = Coluna.Annotations() ann.tree = tree Coluna.store!(ann, mast_ann, Coluna.MathProg.getvar(origform, vars["x1"])) Coluna.store!(ann, mast_ann, Coluna.MathProg.getvar(origform, vars["x2"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getvar(origform, vars["y1"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getvar(origform, vars["y2"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getconstr(origform, constrs["c1"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getconstr(origform, constrs["c2"])) Coluna.store!(ann, mast_ann, Coluna.MathProg.getconstr(origform, constrs["c3"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getconstr(origform, constrs["c4"])) problem = Coluna.MathProg.Problem(env) Coluna.MathProg.set_original_formulation!(problem, origform) Coluna.reformulate!(problem, ann, env) reform = Coluna.MathProg.get_reformulation(problem) # Test first stage variables & constraints # Coluna.MathProg.MinSense + 1.0 x1 + 4.0 x2 + 1.0 η[4] # c3 : + 1.0 x1 + 1.0 x2 >= 0.0 (MasterPureConstrConstraintu3 \| true) # 0.0 <= x1 <= Inf (Continuous \| MasterPureVar \| true) # 0.0 <= x2 <= Inf (Continuous \| MasterPureVar \| true) # 0.0 <= η[4] <= Inf (Continuous \| MasterBendSecondStageCostVar \| true) master = Coluna.MathProg.getmaster(reform) fs_vars = Dict(getname(master, varid) => var for (varid, var) in Coluna.MathProg.getvars(master)) fs_constrs = Dict(getname(master, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(master)) @test length(fs_vars) == 3 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_vars["x1"])) <= Coluna.MathProg.MasterPureVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_vars["x2"])) <= Coluna.MathProg.MasterPureVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_vars["η[4]"])) <= Coluna.MathProg.MasterBendSecondStageCostVar @test Coluna.MathProg.getcurlb(master, fs_vars["x1"]) == 0.0 @test Coluna.MathProg.getcurlb(master, fs_vars["x2"]) == 0.0 @test Coluna.MathProg.getcurlb(master, fs_vars["η[4]"]) == -Inf @test Coluna.MathProg.getcurub(master, fs_vars["x1"]) == Inf @test Coluna.MathProg.getcurub(master, fs_vars["x2"]) == Inf @test Coluna.MathProg.getcurub(master, fs_vars["η[4]"]) == Inf @test Coluna.MathProg.getcurcost(master, fs_vars["x1"]) == 1.0 @test Coluna.MathProg.getcurcost(master, fs_vars["x2"]) == 4.0 @test Coluna.MathProg.getcurcost(master, fs_vars["η[4]"]) == 1.0 @test length(fs_constrs) == 1 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_constrs["c3"])) <= Coluna.MathProg.MasterPureConstr @test Coluna.MathProg.getcurrhs(master, fs_constrs["c3"]) == 0.0 # Test second stage variables & Constraints # Coluna.MathProg.MinSense + 2.0 y1 + 3.0 y2 + 1.0 μ⁺[x1] + 1.0 μ⁻[x1] + 4.0 μ⁺[x2] + 4.0 μ⁻[x2] # c1 : - 1.0 x1 + 4.0 x2 + 2.0 y1 + 3.0 y2 >= 2.0 (BendSpTechnologicalConstrConstraintu1 \| true) # c2 : + 1.0 x1 + 3.0 x2 + 1.0 y1 + 1.0 y2 >= 3.0 (BendSpTechnologicalConstrConstraintu2 \| true) # c4 : + 1.0 y1 + 1.0 y2 >= 0.0 (BendSpPureConstrConstraintu4 \| true) # 0.0 <= y1 <= Inf (Continuous \| BendSpSepVar \| true) # 0.0 <= y2 <= Inf (Continuous \| BendSpSepVar \| true) # 0.0 <= x1 <= Inf (Continuous \| BendFirstStageRepVar \| true) # 0.0 <= x2 <= Inf (Continuous \| BendFirstStageRepVar \| true) subprob = first(values(Coluna.MathProg.get_benders_sep_sps(reform))) ss_vars = Dict(getname(subprob, varid) => var for (varid, var) in Coluna.MathProg.getvars(subprob)) ss_constrs = Dict(getname(subprob, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(subprob)) @test length(ss_vars) == 7 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["y1"])) <= Coluna.MathProg.BendSpSepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["y2"])) <= Coluna.MathProg.BendSpSepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["x1"])) <= Coluna.MathProg.BendSpFirstStageRepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["x2"])) <= Coluna.MathProg.BendSpFirstStageRepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["local_art_of_c1"])) <= Coluna.MathProg.BendSpSecondStageArtVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["local_art_of_c2"])) <= Coluna.MathProg.BendSpSecondStageArtVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["local_art_of_c4"])) <= Coluna.MathProg.BendSpSecondStageArtVar @test Coluna.MathProg.getcurlb(subprob, ss_vars["y1"]) == 0.0 @test Coluna.MathProg.getcurlb(subprob, ss_vars["y2"]) == 0.0 @test Coluna.MathProg.getcurlb(subprob, ss_vars["x1"]) == 0.0 @test Coluna.MathProg.getcurlb(subprob, ss_vars["x2"]) == 0.0 @test Coluna.MathProg.getcurub(subprob, ss_vars["y1"]) == Inf @test Coluna.MathProg.getcurub(subprob, ss_vars["y2"]) == Inf @test Coluna.MathProg.getcurub(subprob, ss_vars["x1"]) == Inf @test Coluna.MathProg.getcurub(subprob, ss_vars["x2"]) == Inf @test Coluna.MathProg.getcurcost(subprob, ss_vars["y1"]) == 2.0 @test Coluna.MathProg.getcurcost(subprob, ss_vars["y2"]) == 3.0 @test Coluna.MathProg.getcurcost(subprob, ss_vars["x1"]) == 1.0 @test Coluna.MathProg.getcurcost(subprob, ss_vars["x2"]) == 4.0 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_constrs["c1"])) <= Coluna.MathProg.BendSpTechnologicalConstr @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_constrs["c2"])) <= Coluna.MathProg.BendSpTechnologicalConstr @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_constrs["c4"])) <= Coluna.MathProg.BendSpPureConstr @test Coluna.MathProg.getcurrhs(subprob, ss_constrs["c1"]) == 2.0 @test Coluna.MathProg.getcurrhs(subprob, ss_constrs["c2"]) == 3.0 @test Coluna.MathProg.getcurrhs(subprob, ss_constrs["c4"]) == 0.0 @test length(ss_constrs) == 3 return end register!(unit_tests, "benders_decomposition", benders_decomposition)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	2703	function primal_bound_constructor() env = Coluna.Env{ClMP.VarId}(Coluna.Params()) min_form = ClMP.create_formulation!( env, ClMP.Original(); obj_sense = ClMP.MinSense ) max_form = ClMP.create_formulation!( env, ClMP.Original(); obj_sense = ClMP.MaxSense ) pb1 = ClMP.PrimalBound(min_form) @test pb1 == Inf pb2 = ClMP.PrimalBound(max_form) @test pb2 == -Inf pb3 = ClMP.PrimalBound(min_form, 10) @test pb3 == 10 @test_throws AssertionError ClMP.PrimalBound(max_form, pb3) end register!(unit_tests, "bounds", primal_bound_constructor) function dual_bound_constructor() env = Coluna.Env{ClMP.VarId}(Coluna.Params()) min_form = ClMP.create_formulation!( env, ClMP.Original(); obj_sense = ClMP.MinSense ) max_form = ClMP.create_formulation!( env, ClMP.Original(); obj_sense = ClMP.MaxSense ) db1 = ClMP.DualBound(min_form) @test db1 == -Inf db2 = ClMP.DualBound(max_form) @test db2 == Inf db3 = ClMP.DualBound(min_form, 150) @test db3 == 150 @test_throws AssertionError ClMP.DualBound(max_form, db3) end register!(unit_tests, "bounds", dual_bound_constructor) function obj_values_constructor() env = Coluna.Env{ClMP.VarId}(Coluna.Params()) min_form = ClMP.create_formulation!( env, ClMP.Original(); obj_sense = ClMP.MinSense ) obj = ClMP.ObjValues( min_form; ip_primal_bound = 15.0, ip_dual_bound = 12.0, lp_primal_bound = 66, lp_dual_bound = π ) @test obj.ip_primal_bound == 15.0 @test obj.ip_dual_bound == 12.0 @test obj.lp_primal_bound == 66 @test obj.lp_dual_bound == float(π) # precision... # Gap methods are already tested in containers/solsandbounds.jl @test ClMP._update_ip_primal_bound!(obj, ClMP.PrimalBound(min_form, 16.0)) == false @test ClMP._update_ip_primal_bound!(obj, ClMP.PrimalBound(min_form, 14.0)) == true @test ClMP._update_lp_primal_bound!(obj, ClMP.PrimalBound(min_form, 67.0)) == false @test ClMP._update_lp_primal_bound!(obj, ClMP.PrimalBound(min_form, 65.0)) == true @test ClMP._update_ip_dual_bound!(obj, ClMP.DualBound(min_form, 11.0)) == false @test ClMP._update_ip_dual_bound!(obj, ClMP.DualBound(min_form, 13.0)) == true @test ClMP._update_lp_dual_bound!(obj, ClMP.DualBound(min_form, 3.0)) == false @test ClMP._update_lp_dual_bound!(obj, ClMP.DualBound(min_form, 3.2)) == true @test obj.ip_primal_bound == 14.0 @test obj.ip_dual_bound == 13.0 @test obj.lp_primal_bound == 65 end register!(unit_tests, "bounds", obj_values_constructor)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	10787	function model_factory_for_buffer() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) push!(form.optimizers, ClMP.MoiOptimizer(MOI._instantiate_and_check(GLPK.Optimizer))) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost=2.0, lb=-1.0, ub=1.0, kind=ClMP.Integ, inc_val=4.0 ) constr = ClMP.setconstr!( form, "constr1", ClMP.MasterBranchOnOrigVarConstr, rhs=-13.0, members = Dict(ClMP.getid(var) => 2.0) ) CL.closefillmode!(ClMP.getcoefmatrix(form)) return form, var, constr end function _test_buffer(current::ClMP.FormulationBuffer, expected::ClMP.FormulationBuffer) @test isequal(current.changed_obj_sense, expected.changed_obj_sense) @test isequal(current.changed_cost, expected.changed_cost) @test isequal(current.changed_bound, expected.changed_bound) @test isequal(current.changed_var_kind, expected.changed_var_kind) @test isequal(current.changed_rhs, expected.changed_rhs) @test isequal(current.var_buffer, expected.var_buffer) @test isequal(current.constr_buffer, expected.constr_buffer) @test isequal(current.reset_coeffs, expected.reset_coeffs) return end _empty_buffer() = ClMP.FormulationBuffer{ClMP.VarId,ClMP.Variable,ClMP.ConstrId,ClMP.Constraint}() function model_factory_buffer_initial_state() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.changed_obj_sense = false # minimization by default expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.added = Set([varid]) expected_buffer.constr_buffer = ClMP.VarConstrBuffer{ClMP.ConstrId, ClMP.Constraint}() expected_buffer.constr_buffer.added = Set([constrid]) _test_buffer(form.buffer, expected_buffer) end register!(unit_tests, "buffer", model_factory_buffer_initial_state) function setcurcost!_and_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurcost!(form, var, 3.0) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # should not throw any error @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurcost!_and_deactivate_variable) function setcurkind!_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurkind!(form, var, ClMP.Integ) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # should not throw any error @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurkind!_deactivate_variable) function setcurlb!_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurlb!(form, var, 0.0) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurlb!_deactivate_variable) function setcurub!_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurub!(form, var, 0.0) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurub!_deactivate_variable) function setcurrhs!_deactivate_constraint() form, var, constr = model_factory_for_buffer() constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.constr_buffer = ClMP.VarConstrBuffer{ClMP.ConstrId, ClMP.Constraint}() expected_buffer.constr_buffer.removed = Set([constrid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurrhs!(form, constr, 0.0) ClMP.deactivate!(form, constr) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, constr) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurrhs!_deactivate_constraint) function set_matrix_coeff() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.reset_coeffs = Dict((constrid => varid) => 5.0) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] = 5.0 _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] == 5.0 _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", set_matrix_coeff) function add_variable_and_set_matrix_coeff() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) var2 = ClMP.setvar!( form, "var2", ClMP.OriginalVar, cost=3.0, lb=-2.0, ub=2.0, kind=ClMP.Integ, inc_val=4.0 ) ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var2)] = 8.0 expected_buffer = _empty_buffer() # change of the matrix is not buffered because it is a new variable and Coluna has # to create the whole column in the subsolver. expected_buffer.var_buffer.added = Set([ClMP.getid(var2)]) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", add_variable_and_set_matrix_coeff) function set_matrix_coeff_and_deactivate_var_and_constr() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) expected_buffer.constr_buffer = ClMP.VarConstrBuffer{ClMP.ConstrId, ClMP.Constraint}() expected_buffer.constr_buffer.removed = Set([constrid]) # matrix coefficient change is kept because it's too expensive to propagate # variable or column deletion in the matrix coeff buffer expected_buffer.reset_coeffs = Dict((constrid => varid) => 3.0) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] = 3.0 ClMP.deactivate!(form, var) ClMP.deactivate!(form, constr) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, var) == false @test ClMP.iscuractive(form, constr) == false @test ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] == 3.0 _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", set_matrix_coeff_and_deactivate_var_and_constr) function change_objective_sense() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.changed_obj_sense = true ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.set_objective_sense!(form, false) # maximization _test_buffer(form.buffer, expected_buffer) @test ClMP.getobjsense(form) == ClMP.MaxSense ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) _test_buffer(form.buffer, _empty_buffer()) end register!(unit_tests, "buffer", change_objective_sense) function set_peren_lb_and_ub() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.changed_bound = Set{ClMP.VarId}([ClMP.getid(var)]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setperenlb!(form, var, 0.0) ClMP.setperenub!(form, var, 1.0) _test_buffer(form.buffer, expected_buffer) end register!(unit_tests, "buffer", set_peren_lb_and_ub) function remove_variable() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.var_buffer.removed = Set([ClMP.getid(var)]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurcost!(form, var, 2.0) # make sure we delete buffered changes delete!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # make sure exception thrown end register!(unit_tests, "buffer", remove_variable) function remove_constraint() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.constr_buffer.removed = Set([ClMP.getid(constr)]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurrhs!(form, constr, 3.0) # make sure we delete buffered changes delete!(form, constr) #_test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # make sure exception thrown end register!(unit_tests, "buffer", remove_constraint)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	1765	function getters_and_setters() form = ClMP.create_formulation!(Coluna.Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) constr = ClMP.setconstr!(form, "fake_constr", ClMP.MasterBranchOnOrigVarConstr, rhs = -13.0, kind = ClMP.Facultative, sense = ClMP.Less, inc_val = -12.0, is_active = false, is_explicit = false ) cid = ClMP.getid(constr) # rhs @test ClMP.getcurrhs(form, cid) == -13.0 ClMP.setcurrhs!(form, cid, 10.0) @test ClMP.getperenrhs(form, cid) == -13.0 @test ClMP.getcurrhs(form, cid) == 10.0 ClMP.setperenrhs!(form, cid, 45.0) @test ClMP.getperenrhs(form, cid) == 45.0 @test ClMP.getcurrhs(form, cid) == 45.0 ClMP.setcurrhs!(form, cid, 12.0) # change cur before reset! # sense @test ClMP.getcursense(form, cid) == ClMP.Less ClMP.setcursense!(form, cid, ClMP.Greater) @test ClMP.getperensense(form, cid) == ClMP.Less @test ClMP.getcursense(form, cid) == ClMP.Greater ClMP.setperensense!(form, cid, ClMP.Equal) @test ClMP.getperensense(form, cid) == ClMP.Equal @test ClMP.getcursense(form, cid) == ClMP.Equal ClMP.reset!(form, cid) @test ClMP.getcurrhs(form, cid) == 45.0 @test ClMP.getperenrhs(form, cid) == 45.0 end register!(unit_tests, "constraints", getters_and_setters) function records() c_rec = ClMP.MoiConstrRecord( ; index = ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-15) ) @test ClMP.getmoiindex(c_rec) == ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-15) ClMP.setmoiindex!(c_rec, ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-20)) @test ClMP.getmoiindex(c_rec) == ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-20) end register!(unit_tests, "constraints", records)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	14221	function dw_decomposition() """ min x1 + x2 + y1 + y2 + s1 + s2 st. x1 + x2 + y1 + y2 >= 1 2x1 + 3x2 <= s1 3y1 + 2y2 <= s2 2 >= x1 >= 1 3 >= x2 >= 2 -Inf >= s1 >= -Inf 2 >= y1 >= 1 3 >= y2 >= 2 -Inf >= -Inf """ env = Coluna.Env{Coluna.MathProg.VarId}( Coluna.Params( global_art_var_cost=1000.0, local_art_var_cost=100.0 ) ) origform = Coluna.MathProg.create_formulation!( env, Coluna.MathProg.Original() ) # Variables vars = Dict{String,Coluna.MathProg.VarId}() variables_infos = [ ("x1", 1.0, Integ, 1.0, 2.0), ("x2", 1.0, Integ, 2.0, 3.0), ("s1", 1.0, Continuous, -Inf, Inf), ("y1", 1.0, Integ, 1.0, 2.0), ("y2", 1.0, Integ, 2.0, 3.0), ("s2", 1.0, Continuous, -1.0, Inf) ] for (name, cost, kind, lb, ub) in variables_infos vars[name] = Coluna.MathProg.getid( Coluna.MathProg.setvar!( origform, name, Coluna.MathProg.OriginalVar; cost=cost, lb=lb, ub=ub, kind=kind ) ) end # Constraints constrs = Dict{String,Coluna.MathProg.ConstrId}() constraints_infos = [ ("c1", 1.0, Coluna.MathProg.Greater, Dict(vars["x1"] => 1.0, vars["x2"] => 1.0, vars["y1"] => 1.0, vars["y2"] => 1.0, vars["s1"] => 1.0, vars["s2"] => 1.0)), ("c2", 0.0, Coluna.MathProg.Less, Dict(vars["x1"] => 2.0, vars["x2"] => 3.0, vars["s1"] => -1.0)), ("c3", 0.0, Coluna.MathProg.Less, Dict(vars["y1"] => 3.0, vars["y2"] => 2.0, vars["s2"] => -1.0)) ] for (name, rhs, sense, members) in constraints_infos constrs[name] = Coluna.MathProg.getid( Coluna.MathProg.setconstr!( origform, name, Coluna.MathProg.OriginalConstr; rhs=rhs, sense=sense, members=members ) ) end # Decomposition tree m = JuMP.Model() BlockDecomposition.@axis(axis, [1, 2]) tree = BlockDecomposition.Tree(m, BlockDecomposition.DantzigWolfe, axis) mast_ann = tree.root.master sp_ann1 = BlockDecomposition.Annotation(tree, BlockDecomposition.DwPricingSp, BlockDecomposition.DantzigWolfe, []) BlockDecomposition.create_leaf!(BlockDecomposition.getroot(tree), axis[1], sp_ann1) sp_ann2 = BlockDecomposition.Annotation(tree, BlockDecomposition.DwPricingSp, BlockDecomposition.DantzigWolfe, []) BlockDecomposition.create_leaf!(BlockDecomposition.getroot(tree), axis[2], sp_ann2) # Dantzig-Wolfe annotations ann = Coluna.Annotations() ann.tree = tree Coluna.store!(ann, mast_ann, Coluna.MathProg.getconstr(origform, constrs["c1"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getconstr(origform, constrs["c2"])) Coluna.store!(ann, sp_ann2, Coluna.MathProg.getconstr(origform, constrs["c3"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getvar(origform, vars["x1"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getvar(origform, vars["x2"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getvar(origform, vars["s1"])) Coluna.store!(ann, sp_ann2, Coluna.MathProg.getvar(origform, vars["y1"])) Coluna.store!(ann, sp_ann2, Coluna.MathProg.getvar(origform, vars["y2"])) Coluna.store!(ann, sp_ann2, Coluna.MathProg.getvar(origform, vars["s2"])) problem = Coluna.MathProg.Problem(env) Coluna.MathProg.set_original_formulation!(problem, origform) Coluna.reformulate!(problem, ann, env) reform = Coluna.MathProg.get_reformulation(problem) # Test master master = Coluna.MathProg.getmaster(reform) master_vars = Dict(getname(master, varid) => var for (varid, var) in Coluna.MathProg.getvars(master)) master_constrs = Dict(getname(master, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(master)) @test length(master_vars) == 15 @test getcurub(master, master_vars["x1"]) == 2.0 @test getcurub(master, master_vars["x2"]) == 3.0 @test getcurub(master, master_vars["y1"]) == 2.0 @test getcurub(master, master_vars["y2"]) == 3.0 @test getcurub(master, master_vars["s1"]) == Inf @test getcurub(master, master_vars["s2"]) == Inf @test getcurlb(master, master_vars["x1"]) == 1.0 @test getcurlb(master, master_vars["x2"]) == 2.0 @test getcurlb(master, master_vars["y1"]) == 1.0 @test getcurlb(master, master_vars["y2"]) == 2.0 @test getcurlb(master, master_vars["s1"]) == -Inf @test getcurlb(master, master_vars["s2"]) == -1.0 sp1 = first(values(Coluna.MathProg.get_dw_pricing_sps(reform))) sp1_vars = Dict(getname(sp1, varid) => var for (varid, var) in Coluna.MathProg.getvars(sp1)) sp1_constrs = Dict(getname(sp1, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(sp1)) @test Coluna.MathProg.getcurlb(sp1, sp1_vars["x1"]) == 1.0 @test Coluna.MathProg.getcurub(sp1, sp1_vars["x1"]) == 2.0 @test Coluna.MathProg.getcurlb(sp1, sp1_vars["x2"]) == 2.0 @test Coluna.MathProg.getcurub(sp1, sp1_vars["x2"]) == 3.0 end register!(unit_tests, "dw_decomposition", dw_decomposition) function dw_decomposition_with_identical_subproblems() """ min x1 + x2 + y1 + y2 + x3 + y3 st. x1 + x2 + y1 + y2 >= 1 2x1 + 3x2 <= x3 2y1 + 3y2 <= y3 // same subproblem 1 <= x1 <= 2 2 <= x2 <= 3 """ env = Coluna.Env{Coluna.MathProg.VarId}( Coluna.Params( global_art_var_cost=1000.0, local_art_var_cost=100.0 ) ) origform = Coluna.MathProg.create_formulation!( env, Coluna.MathProg.Original() ) # Variables vars = Dict{String,Coluna.MathProg.VarId}() variables_infos = [ ("x1", 1.0, Integ, 1.0, 2.0), ("x2", 1.0, Integ, 2.0, 3.0), ("x3", 1.0, Continuous, -Inf, Inf), ] for (name, cost, kind, lb, ub) in variables_infos vars[name] = Coluna.MathProg.getid( Coluna.MathProg.setvar!( origform, name, Coluna.MathProg.OriginalVar; cost=cost, lb=lb, ub=ub, kind=kind ) ) end # Constraints constrs = Dict{String,Coluna.MathProg.ConstrId}() constraints_infos = [ ("c1", 1.0, Coluna.MathProg.Greater, Dict(vars["x1"] => 1.0, vars["x2"] => 1.0)), ("c2", 5.0, Coluna.MathProg.Less, Dict(vars["x1"] => 2.0, vars["x2"] => 3.0, vars["x3"] => -1.0)), ] for (name, rhs, sense, members) in constraints_infos constrs[name] = Coluna.MathProg.getid( Coluna.MathProg.setconstr!( origform, name, Coluna.MathProg.OriginalConstr; rhs=rhs, sense=sense, members=members ) ) end # Decomposition tree m = JuMP.Model() BlockDecomposition.@axis(axis, [1, 2]) tree = BlockDecomposition.Tree(m, BlockDecomposition.DantzigWolfe, axis) mast_ann = tree.root.master sp_ann1 = BlockDecomposition.Annotation(tree, BlockDecomposition.DwPricingSp, BlockDecomposition.DantzigWolfe, []) BlockDecomposition.setlowermultiplicity!(sp_ann1, 0) BlockDecomposition.setuppermultiplicity!(sp_ann1, 2) BlockDecomposition.create_leaf!(BlockDecomposition.getroot(tree), axis[1], sp_ann1) # Dantzig-Wolfe annotations ann = Coluna.Annotations() ann.tree = tree Coluna.store!(ann, mast_ann, Coluna.MathProg.getconstr(origform, constrs["c1"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getconstr(origform, constrs["c2"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getvar(origform, vars["x1"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getvar(origform, vars["x2"])) Coluna.store!(ann, sp_ann1, Coluna.MathProg.getvar(origform, vars["x3"])) problem = Coluna.MathProg.Problem(env) Coluna.MathProg.set_original_formulation!(problem, origform) Coluna.reformulate!(problem, ann, env) reform = Coluna.MathProg.get_reformulation(problem) # Test master master = Coluna.MathProg.getmaster(reform) master_vars = Dict(getname(master, varid) => var for (varid, var) in Coluna.MathProg.getvars(master)) master_constrs = Dict(getname(master, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(master)) @test length(master_vars) == 9 @test Coluna.MathProg.getcurub(master, master_vars["x1"]) == 2.0 * 2 @test Coluna.MathProg.getcurub(master, master_vars["x2"]) == 3.0 * 2 @test Coluna.MathProg.getcurub(master, master_vars["x3"]) == Inf @test Coluna.MathProg.getcurlb(master, master_vars["x1"]) == 1.0 * 0 @test Coluna.MathProg.getcurlb(master, master_vars["x2"]) == 2.0 * 0 @test Coluna.MathProg.getcurlb(master, master_vars["x3"]) == -Inf @test Coluna.MathProg.getcurrhs(master, master_constrs["c1"]) == 1.0 @test Coluna.MathProg.getcurrhs(master, master_constrs["sp_ub_4"]) == 2.0 @test Coluna.MathProg.getcurrhs(master, master_constrs["sp_lb_4"]) == 0.0 sp1 = first(values(Coluna.MathProg.get_dw_pricing_sps(reform))) sp1_vars = Dict(getname(sp1, varid) => var for (varid, var) in Coluna.MathProg.getvars(sp1)) sp1_constrs = Dict(getname(sp1, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(sp1)) @test length(sp1_vars) == 4 @test Coluna.MathProg.getcurlb(sp1, sp1_vars["x1"]) == 1.0 @test Coluna.MathProg.getcurub(sp1, sp1_vars["x1"]) == 2.0 @test Coluna.MathProg.getcurlb(sp1, sp1_vars["x2"]) == 2.0 @test Coluna.MathProg.getcurub(sp1, sp1_vars["x2"]) == 3.0 end register!(unit_tests, "dw_decomposition", dw_decomposition_with_identical_subproblems) function dw_decomposition_repr() """ min e1 s.t. e1 >= 4 sp1 : 1 <= e1 <= 2 with lm = 0, lm= 2 sp2 : 1 <= e1 <= 2 with lm = 1, lm= 3 """ env = Coluna.Env{Coluna.MathProg.VarId}( Coluna.Params( global_art_var_cost=1000.0, local_art_var_cost=100.0 ) ) origform = Coluna.MathProg.create_formulation!( env, Coluna.MathProg.Original() ) # Variables vars = Dict{String,Coluna.MathProg.VarId}() e1 = Coluna.MathProg.getid( Coluna.MathProg.setvar!( origform, "e1", Coluna.MathProg.OriginalVar; cost=1.0, lb=1.0, ub=2.0, kind=Integ ) ) # Constraints constrs = Dict{String,Coluna.MathProg.ConstrId}() c1 = Coluna.MathProg.getid( Coluna.MathProg.setconstr!( origform, "c1", Coluna.MathProg.OriginalConstr; rhs=4.0, sense=Coluna.MathProg.Greater, members=Dict(e1 => 1.0) ) ) # Decomposition tree m = JuMP.Model() BlockDecomposition.@axis(axis, [1, 2]) tree = BlockDecomposition.Tree(m, BlockDecomposition.DantzigWolfe, axis) mast_ann = tree.root.master sp_ann1 = BlockDecomposition.Annotation(tree, BlockDecomposition.DwPricingSp, BlockDecomposition.DantzigWolfe, []) BlockDecomposition.setlowermultiplicity!(sp_ann1, 0) BlockDecomposition.setuppermultiplicity!(sp_ann1, 2) BlockDecomposition.create_leaf!(BlockDecomposition.getroot(tree), axis[1], sp_ann1) sp_ann2 = BlockDecomposition.Annotation(tree, BlockDecomposition.DwPricingSp, BlockDecomposition.DantzigWolfe, []) BlockDecomposition.setlowermultiplicity!(sp_ann2, 1) BlockDecomposition.setuppermultiplicity!(sp_ann2, 3) BlockDecomposition.create_leaf!(BlockDecomposition.getroot(tree), axis[2], sp_ann2) # Dantzig-Wolfe annotations ann = Coluna.Annotations() ann.tree = tree Coluna.store!(ann, mast_ann, Coluna.MathProg.getconstr(origform, c1)) Coluna.store_repr!(ann, [sp_ann1, sp_ann2], Coluna.MathProg.getvar(origform, e1)) problem = Coluna.MathProg.Problem(env) Coluna.MathProg.set_original_formulation!(problem, origform) Coluna.reformulate!(problem, ann, env) reform = Coluna.MathProg.get_reformulation(problem) _io = IOBuffer() print(IOContext(_io, :user_only => true), reform) @test String(take!(_io)) == """ --- Reformulation --- Formulation DwMaster id = 3 MinSense c1 : + 1.0 e1 >= 4.0 (MasterMixedConstr \| true) 1.0 <= e1 <= 10.0 (Integ \| MasterRepPricingVar \| false) Formulation DwSp id = 5 Multiplicities: lower = 0, upper = 2 MinSense + 1.0 e1 1.0 <= e1 <= 2.0 (Integ \| DwSpPricingVar \| true) Formulation DwSp id = 4 Multiplicities: lower = 1, upper = 3 MinSense + 1.0 e1 1.0 <= e1 <= 2.0 (Integ \| DwSpPricingVar \| true) --------------------- """ # Test master master = Coluna.MathProg.getmaster(reform) master_vars = Dict(getname(master, varid) => var for (varid, var) in Coluna.MathProg.getvars(master)) master_constrs = Dict(getname(master, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(master)) @test Coluna.MathProg.getcurlb(master, master_vars["e1"]) == 1.0 * (0 + 1) @test Coluna.MathProg.getcurub(master, master_vars["e1"]) == 2.0 * (2 + 3) @test Coluna.MathProg.getcurrhs(master, master_constrs["c1"]) == 4.0 # Test subproblem 1 sp1 = first(values(Coluna.MathProg.get_dw_pricing_sps(reform))) sp1_vars = Dict(getname(sp1, varid) => var for (varid, var) in Coluna.MathProg.getvars(sp1)) sp1_constrs = Dict(getname(sp1, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(sp1)) @test length(sp1_vars) == 2 @test Coluna.MathProg.getcurlb(sp1, sp1_vars["e1"]) == 1.0 @test Coluna.MathProg.getcurub(sp1, sp1_vars["e1"]) == 2.0 # Test subproblem 2 sp2 = collect(values(Coluna.MathProg.get_dw_pricing_sps(reform)))[2] sp2_vars = Dict(getname(sp2, varid) => var for (varid, var) in Coluna.MathProg.getvars(sp2)) sp2_constrs = Dict(getname(sp2, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(sp2)) @test length(sp2_vars) == 2 @test Coluna.MathProg.getcurlb(sp1, sp2_vars["e1"]) == 1.0 @test Coluna.MathProg.getcurub(sp1, sp2_vars["e1"]) == 2.0 end register!(unit_tests, "dw_decomposition", dw_decomposition_repr)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	2507	vid(uid) = ClMP.VarId(ClMP.OriginalVar, uid, 1) # Dantzig-wolfe solution pool struct DummyFormulation <: ClMP.AbstractFormulation end # optimizers struct DummyOptimizer <: ClMP.AbstractOptimizer end function dantzig_wolfe_pool() pool_sols = dynamicsparse(ClMP.VarId, ClMP.VarId, Float64; fill_mode = false) pool_ht = ClB.HashTable{ClMP.VarId,ClMP.VarId}() form = DummyFormulation() sol1_id = vid(1) sol1_ids = [vid(4), vid(5), vid(8)] sol1_vals = [1.0, 2.0, 5.0] sol1_repr = ClMP.PrimalSolution(form, sol1_ids, sol1_vals, 2.0, ClMP.FEASIBLE_SOL) sol2_id = vid(2) sol2_ids = [vid(4), vid(7), vid(9)] sol2_vals = [2.0, 2.0, 3.0] sol2_repr = ClMP.PrimalSolution(form, sol2_ids, sol2_vals, 4.0, ClMP.FEASIBLE_SOL) sol3_ids = [vid(4), vid(7), vid(9)] sol3_vals = [1.0, 2.0, 3.0] sol3_repr = ClMP.PrimalSolution(form, sol3_ids, sol3_vals, 5.0, ClMP.FEASIBLE_SOL) addrow!(pool_sols, sol1_id, sol1_ids, sol1_vals) ClB.savesolid!(pool_ht, sol1_id, sol1_repr) addrow!(pool_sols, sol2_id, sol2_ids, sol2_vals) ClB.savesolid!(pool_ht, sol2_id, sol2_repr) a = ClMP._get_same_sol_in_pool(pool_sols, pool_ht, sol1_repr) @test a == sol1_id a = ClMP._get_same_sol_in_pool(pool_sols, pool_ht, sol2_repr) @test a == sol2_id a = ClMP._get_same_sol_in_pool(pool_sols, pool_ht, sol3_repr) @test a === nothing end register!(unit_tests, "formulations", dantzig_wolfe_pool) function optimizers() env = CL.Env{ClMP.VarId}(CL.Params()) form = ClMP.create_formulation!(env, ClMP.DwMaster()) @test ClMP.getoptimizer(form, 1) isa ClMP.NoOptimizer ClMP.push_optimizer!(form, () -> DummyOptimizer()) @test ClMP.getoptimizer(form, 1) isa DummyOptimizer @test ClMP.getoptimizer(form, 2) isa ClMP.NoOptimizer end register!(unit_tests, "formulations", optimizers) # TODO : move this test outside unit tests. # function max_nb_form_unit() # coluna = optimizer_with_attributes( # Coluna.Optimizer, # "params" => Coluna.Params( # solver = Coluna.Algorithm.TreeSearchAlgorithm() # default BCP # ), # "default_optimizer" => GLPK.Optimizer # GLPK for the master & the subproblems # ) # @axis(M, 1:typemax(Int16)+1) # model = BlockModel(coluna) # @variable(model, x[m in M], Bin) # @dantzig_wolfe_decomposition(model, decomposition, M) # @test_throws ErrorException("Maximum number of formulations reached.") optimize!(model) # return # end	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	1588	DynamicSparseArrays.semaphore_key(::Type{Char}) = ' ' Base.zero(::Type{Char}) = ' ' DynamicSparseArrays.semaphore_key(::Type{Int}) = 0 function coefmatrix_factory() rows = ['a', 'a', 'b', 'b', 'd', 'f'] cols = [1, 2, 3, 1, 7, 1] vals = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] buffer = ClMP.FormulationBuffer{Int,Nothing,Char,Nothing}() matrix = ClMP.CoefficientMatrix{Char,Int,Float64}(buffer) for (i,j,v) in Iterators.zip(rows, cols, vals) matrix[i,j] = v end return rows, cols, vals, matrix end function close_fill_mode() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) for (i,j,v) in Iterators.zip(rows, cols, vals) @test matrix[i,j] == v end end register!(unit_tests, "formulations", close_fill_mode) function view_col() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) for (row, val) in @view matrix[:, 1] @test val == matrix[row,1] end end register!(unit_tests, "formulations", view_col) function view_row() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) for (col, val) in @view matrix['a', :] @test val == matrix['a', col] end end register!(unit_tests, "formulations", view_row) function transpose_test() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) transposed_matrix = transpose(matrix) for (i,j,v) in Iterators.zip(rows, cols, vals) @test transposed_matrix[j,i] == matrix[i,j] == v end end register!(unit_tests, "formulations", transpose_test)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	8643	function test_mapping_operator_1() G = Vector{Float64}[ [0, 0, 1, 1, 0, 0, 1], [1, 0, 0, 1, 1, 0, 1], [1, 1, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1] ] v = Float64[3, 2, 0, 0] result = Coluna.MathProg._mapping(G, v; col_len=7)#, 6) @test result == [ [1, 0, 0, 1, 1, 0, 1], [1, 0, 0, 1, 1, 0, 1], [0, 0, 1, 1, 0, 0, 1], [0, 0, 1, 1, 0, 0, 1], [0, 0, 1, 1, 0, 0, 1], ] @test Coluna.MathProg._rolls_are_integer(result) return end register!(unit_tests, "projection", test_mapping_operator_1) function test_mapping_operator_2() # Example from the paper: # Branching in Branch-and-Price: a Generic Scheme # François Vanderbeck # A = [ # 1 0 1 0; # 0 1 0 1; # 1 1 0 2; # ] # a = [5 5 10] G = Vector{Float64}[ [1, 1, 1, 0], [1, 1, 0, 1], [1, 1, 0, 0], [1, 0, 1, 1], [1, 0, 1, 0], [1, 0, 0, 1], [1, 0, 0, 0], [0, 1, 1, 1], [0, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0, 0, 1, 1], [0, 0, 1, 0], [0, 0, 0, 1], ] v = Float64[0, 1/2, 1, 1/2, 0, 0, 1, 1, 0, 0, 1/2, 0, 1/2, 0, 0] result = Coluna.MathProg._mapping(G, v; col_len=4) @test result == [ [1.0, 1.0, 0.0, 0.5], [1.0, 0.5, 0.5, 0.5], [1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0], [0.0, 0.5, 0.5, 0.0] ] @test Coluna.MathProg._rolls_are_integer(result) == false return end register!(unit_tests, "projection", test_mapping_operator_2) # function test_mapping_operator_3() # G = Vector{Float64}[ # #x_12, x_13, x_14, x_15, x_23, x_24, x_25, x_34, x_35, x_45 # [1, 0, 1, 0, 0, 1, 0, 0, 0, 0], # [1, 0, 0, 1, 1, 0, 0, 0, 1, 0], # [0, 1, 1, 0, 0, 0, 0, 1, 0, 0], # [0, 0, 0, 2, 0, 0, 0, 0, 0, 0], # [1, 0, 1, 0, 1, 0, 0, 1, 0, 0], # [0, 1, 0, 1, 0, 0, 0, 0, 1, 0] # ] # v = Float64[2/3, 1/3, 1/3, 2/3, 1/3, 1/3] # result = Coluna.MathProg._mapping(G, v, 10) # @show result # end # register!(unit_tests, "projection", test_mapping_operator_3; f= true) function identical_subproblems_vrp() # We consider a vrp problem (with fake subproblem) where routes are: # - MC1 : 1 -> 2 -> 3 # - MC2 : 2 -> 3 -> 4 # - MC4 : 3 -> 4 -> 1 # - MC3 : 4 -> 1 -> 2 # At most, three vehicles are available to visit all customers. # We can visit a customer multiple times. # Fractional solution is 1/2 for all columns form = """ master min x_12 + x_13 + x_14 + x_23 + x_24 + x_34 + s_1 + 1.4 MC1 + 1.4 MC2 + 1.4 MC3 + 1.4 MC4 + 0.0 PricingSetupVar_sp_5 s.t. x_12 + x_13 + x_14 + MC1 + MC3 + MC4 >= 1.0 x_12 + x_23 + x_24 + MC1 + MC2 + MC4 >= 1.0 x_13 + x_23 + x_34 + MC1 + MC2 + MC3 >= 1.0 x_14 + x_24 + x_34 + MC2 + MC3 + MC4 >= 1.0 PricingSetupVar_sp_5 >= 0.0 {MasterConvexityConstr} PricingSetupVar_sp_5 <= 3.0 {MasterConvexityConstr} dw_sp min x_12 + x_13 + x_14 + x_23 + x_24 + x_34 + s_1 + 0.0 PricingSetupVar_sp_5 s.t. x_12 + x_13 + x_14 + x_23 + x_24 + x_34 >= 0 continuous columns MC1, MC2, MC3, MC4 representatives s_1 integer pricing_setup PricingSetupVar_sp_5 binary representatives x_12, x_13, x_14, x_23, x_24, x_34 bounds 0.0 <= x_12 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_34 <= 1.0 -Inf <= s_1 <= Inf MC1 >= 0 MC2 >= 0 MC3 >= 0 MC4 >= 0 1.0 <= PricingSetupVar_sp_5 <= 1.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function projection_from_dw_reform_to_master_1() env, master, sps, reform = identical_subproblems_vrp() mastervarids = Dict(CL.getname(master, var) => varid for (varid, var) in CL.getvars(master)) # Register column in the pool spform = first(sps) pool = ClMP.get_primal_sol_pool(spform) var_ids = map(n -> ClMP.getid(ClMP.getvar(spform, mastervarids[n])), ["x_12", "x_13", "x_14", "x_23", "x_24", "x_34", "s_1"]) # VarId[Variableu2, Variableu1, Variableu3, Variableu4, Variableu5, Variableu6] for (name, vals) in Iterators.zip( ["MC1", "MC2", "MC3", "MC4"], [ [1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.4], [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.4], [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.4] ] ) col_id = ClMP.VarId(mastervarids[name]; duty=DwSpPrimalSol) ClMP.push_in_pool!(pool, ClMP.PrimalSolution(spform, var_ids, vals, 1.0, ClMP.FEASIBLE_SOL), col_id, 1.0) end # Create primal solution where each route is used 1/2 time. # This solution is integer feasible. solution = Coluna.MathProg.PrimalSolution( master, map(n -> ClMP.VarId(mastervarids[n]; origin_form_uid=4), ["MC1", "MC2", "MC3", "MC4"]), [1 / 2, 1 / 2, 1 / 2, 1 / 2], 2.0, ClB.FEASIBLE_SOL ) # Test integration columns, values = Coluna.MathProg._extract_data_for_mapping(solution) rolls = Coluna.MathProg._mapping_by_subproblem(columns, values) Coluna.MathProg._remove_continuous_vars_from_rolls!(rolls, reform) # Expected: # \| 1/2 of [1.0, 0.0, 1.0, 0.0, 0.0, 0.0] # \| 1/2 of [1.0, 0.0, 0.0, 1.0, 0.0, 0.0] # -----> [1.0, 0.0, 0.5, 0.5, 0.0, 0.0] # \| 1/2 of [0.0, 0.0, 1.0, 0.0, 0.0, 1.0] # \| 1/2 of [0.0, 0.0, 0.0, 1.0, 0.0, 1.0] # -----> [0.0, 0.0, 0.5, 0.5, 0.0, 1.0] @test rolls == Dict(4 => [ Dict(mastervarids["x_14"] => 0.5, mastervarids["x_23"] => 0.5, mastervarids["x_34"] => 1.0) Dict(mastervarids["x_12"] => 1.0, mastervarids["x_14"] => 0.5, mastervarids["x_23"] => 0.5) ]) proj = Coluna.MathProg.proj_cols_on_rep(solution) @test proj[mastervarids["x_12"]] == 1.0 @test proj[mastervarids["x_14"]] == 1.0 @test proj[mastervarids["x_23"]] == 1.0 @test proj[mastervarids["x_34"]] == 1.0 @test Coluna.MathProg.proj_cols_is_integer(solution) == false end register!(unit_tests, "projection", projection_from_dw_reform_to_master_1) function projection_from_dw_reform_to_master_2() env, master, sps, reform = identical_subproblems_vrp() mastervarids = Dict(CL.getname(master, var) => varid for (varid, var) in CL.getvars(master)) # Register column in the pool spform = first(sps) pool = ClMP.get_primal_sol_pool(spform) var_ids = map(n -> ClMP.getid(ClMP.getvar(spform, mastervarids[n])), ["x_12", "x_13", "x_14", "x_23", "x_24", "x_34", "s_1"]) # VarId[Variableu2, Variableu1, Variableu3, Variableu4, Variableu5, Variableu6] for (name, vals) in Iterators.zip( ["MC1", "MC2"], [ [0.9999999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7], [0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999, 0.7], ] ) col_id = ClMP.VarId(mastervarids[name]; duty=DwSpPrimalSol) ClMP.push_in_pool!(pool, ClMP.PrimalSolution(spform, var_ids, vals, 1.0, ClMP.FEASIBLE_SOL), col_id, 1.0) end # Create primal solution where each route is used 1/2 time. # This solution is integer feasible. solution = Coluna.MathProg.PrimalSolution( master, map(n -> ClMP.VarId(mastervarids[n]; origin_form_uid=4), ["MC1", "MC2"]), [1.0, 1.0], 2.0, ClB.FEASIBLE_SOL ) # Test integration columns, values = Coluna.MathProg._extract_data_for_mapping(solution) rolls = Coluna.MathProg._mapping_by_subproblem(columns, values) Coluna.MathProg._remove_continuous_vars_from_rolls!(rolls, reform) # Expected: # \| 1 of [0.9999999, 0.0, 0.0, 0.0, 0.0, 0.0] # \| 1 of [0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999] @test rolls == Dict(4 => [ Dict(mastervarids["x_34"] => 0.9999999), Dict(mastervarids["x_12"] => 0.9999999) ]) proj = Coluna.MathProg.proj_cols_on_rep(solution) @test proj[mastervarids["x_12"]] == 0.9999999 @test proj[mastervarids["x_34"]] == 0.9999999 @test Coluna.MathProg.proj_cols_is_integer(solution) == true end register!(unit_tests, "projection", projection_from_dw_reform_to_master_2)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	7578	function isless_min_sense() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!(form, "var1", ClMP.OriginalVar) constr = ClMP.setconstr!(form, "constr1", ClMP.OriginalConstr) primalsol1 = ClMP.PrimalSolution(form, [ClMP.getid(var)], [1.0], 1.0, ClB.UNKNOWN_FEASIBILITY) primalsol2 = ClMP.PrimalSolution(form, [ClMP.getid(var)], [0.0], 0.0, ClB.UNKNOWN_FEASIBILITY) @test isless(primalsol1, primalsol2) # primalsol1 is worse than primalsol2 for min sense dualsol1 = ClMP.DualSolution(form, [ClMP.getid(constr)], [1.0], ClMP.VarId[], Float64[], ClMP.ActiveBound[], 1.0, ClB.UNKNOWN_FEASIBILITY) dualsol2 = ClMP.DualSolution(form, [ClMP.getid(constr)], [0.0], ClMP.VarId[], Float64[], ClMP.ActiveBound[], 0.0, ClB.UNKNOWN_FEASIBILITY) @test isless(dualsol2, dualsol1) # dualsol2 is worse than dualsol1 for min sense end register!(unit_tests, "solutions", isless_min_sense) function isless_max_sense() # MaxSense form = ClMP.create_formulation!( Env{ClMP.VarId}(Coluna.Params()), ClMP.Original(), obj_sense = Coluna.MathProg.MaxSense ) var = ClMP.setvar!(form, "var1", ClMP.OriginalVar) constr = ClMP.setconstr!(form, "constr1", ClMP.OriginalConstr) primalsol1 = ClMP.PrimalSolution(form, [ClMP.getid(var)], [1.0], 1.0, ClB.UNKNOWN_FEASIBILITY) primalsol2 = ClMP.PrimalSolution(form, [ClMP.getid(var)], [0.0], 0.0, ClB.UNKNOWN_FEASIBILITY) @test isless(primalsol2, primalsol1) # primalsol2 is worse than primalsol1 for max sense dualsol1 = ClMP.DualSolution(form, [ClMP.getid(constr)], [1.0], ClMP.VarId[], Float64[], ClMP.ActiveBound[], 1.0, ClB.UNKNOWN_FEASIBILITY) dualsol2 = ClMP.DualSolution(form, [ClMP.getid(constr)], [0.0], ClMP.VarId[], Float64[], ClMP.ActiveBound[], 0.0, ClB.UNKNOWN_FEASIBILITY) @test isless(dualsol1, dualsol2) # dualsol1 is worse than dualsol2 for max sense end register!(unit_tests, "solutions", isless_max_sense) function isequal_test() form = ClMP.create_formulation!( Env{ClMP.VarId}(Coluna.Params()), ClMP.Original(), obj_sense = Coluna.MathProg.MaxSense ) var1 = ClMP.setvar!(form, "var1", ClMP.OriginalVar) var2 = ClMP.setvar!(form, "var2", ClMP.OriginalVar) primalsol1 = ClMP.PrimalSolution(form, [ClMP.getid(var1), ClMP.getid(var2)], [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL) primalsol2 = ClMP.PrimalSolution(form, [ClMP.getid(var1), ClMP.getid(var2)], [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL) @test primalsol1 == primalsol2 constr1 = ClMP.setconstr!(form, "constr1", ClMP.OriginalConstr) constr2 = ClMP.setconstr!(form, "constr2", ClMP.OriginalConstr) dualsol1 = ClMP.DualSolution( form, [ClMP.getid(constr2), ClMP.getid(constr1)], [-6.0, 1.0], ClMP.VarId[ClMP.getid(var1)], Float64[2.0], ClMP.ActiveBound[ClMP.LOWER], 1.0, ClB.FEASIBLE_SOL ) dualsol2 = ClMP.DualSolution( form, [ClMP.getid(constr2), ClMP.getid(constr1)], [-6.0, 1.0], ClMP.VarId[ClMP.getid(var1)], Float64[2.0], ClMP.ActiveBound[ClMP.LOWER], 1.0, ClB.FEASIBLE_SOL ) @test dualsol1 == dualsol2 end register!(unit_tests, "solutions", isequal_test) function lin_alg_1_vec_basic_op() form = ClMP.create_formulation!( Env{ClMP.VarId}(Coluna.Params()), ClMP.Original(), obj_sense = Coluna.MathProg.MaxSense ) var1 = ClMP.setvar!(form, "var1", ClMP.OriginalVar; cost = 1.0) var2 = ClMP.setvar!(form, "var2", ClMP.OriginalVar; cost = 0.5) var3 = ClMP.setvar!(form, "var3", ClMP.OriginalVar; cost = -0.25) nzinds1 = [ClMP.getid(var1), ClMP.getid(var2)] nzvals1 = [1.0, 2.0] nzinds2 = [ClMP.getid(var1), ClMP.getid(var2), ClMP.getid(var3)] nzvals2 = [1.0, 2.0, 4.0] primalsol1 = ClMP.PrimalSolution(form, nzinds1, nzvals1, 2.0, ClB.FEASIBLE_SOL) primalsol2 = ClMP.PrimalSolution(form, nzinds2, nzvals2, 1.0, ClB.FEASIBLE_SOL) a = 2 * primalsol1 b = primalsol1 + primalsol2 c = primalsol1 - primalsol2 @test ClB.getvalue(a) == 2 * ClB.getvalue(primalsol1) @test ClB.getvalue(b) == ClB.getvalue(primalsol1) + ClB.getvalue(primalsol2) @test ClB.getvalue(c) == ClB.getvalue(primalsol1) - ClB.getvalue(primalsol2) @test ClB.getstatus(a) == ClB.UNKNOWN_SOLUTION_STATUS @test ClB.getstatus(b) == ClB.UNKNOWN_SOLUTION_STATUS @test ClB.getstatus(c) == ClB.UNKNOWN_SOLUTION_STATUS @test findnz(a.solution.sol) == (nzinds1, 2nzvals1) @test findnz(b.solution.sol) == (nzinds2, [nzvals1..., 0.0] + nzvals2) @test findnz(c.solution.sol) == ([ClMP.getid(var3)], [-4.0]) end register!(unit_tests, "solutions", lin_alg_1_vec_basic_op) function lin_alg_2_transpose() form = ClMP.create_formulation!( Env{ClMP.VarId}(Coluna.Params()), ClMP.Original(), obj_sense = Coluna.MathProg.MaxSense ) var1 = ClMP.setvar!(form, "var1", ClMP.OriginalVar; cost = 1.0) var2 = ClMP.setvar!(form, "var2", ClMP.OriginalVar; cost = 0.5) var3 = ClMP.setvar!(form, "var3", ClMP.OriginalVar; cost = -0.25) nzinds1 = [ClMP.getid(var1), ClMP.getid(var2)] nzvals1 = [12.0, 4.0] vec1 = sparsevec(ClMP.getuid.(nzinds1), nzvals1, 4) primalsol1 = ClMP.PrimalSolution(form, nzinds1, nzvals1, 14.0, ClB.FEASIBLE_SOL) nzinds2 = [ClMP.getid(var1), ClMP.getid(var2), ClMP.getid(var3)] nzvals2 = [1.0, 2.0, 4.0] vec2 = sparsevec(ClMP.getuid.(nzinds2), nzvals2, 4) primalsol2 = ClMP.PrimalSolution(form, nzinds2, nzvals2, 1.0, ClB.FEASIBLE_SOL) a = transpose(vec1) vec2 b = transpose(primalsol1) * primalsol2 @test a == b end register!(unit_tests, "solutions", lin_alg_2_transpose) function lin_alg_3_spMv() form = ClMP.create_formulation!( Env{ClMP.VarId}(Coluna.Params()), ClMP.Original(), obj_sense = Coluna.MathProg.MaxSense ) var1 = ClMP.setvar!(form, "var1", ClMP.OriginalVar; cost = 1.0) var2 = ClMP.setvar!(form, "var2", ClMP.OriginalVar; cost = 0.5) var3 = ClMP.setvar!(form, "var3", ClMP.OriginalVar; cost = -0.25) var4 = ClMP.setvar!(form, "var4", ClMP.OriginalVar; cost = 1.0) var5 = ClMP.setvar!(form, "var5", ClMP.OriginalVar; cost = 1.0) nzrows = ClMP.getid.([var1, var1, var2, var4, var4, var5]) # 5 rows nzcols = ClMP.getid.([var2, var3, var4, var1, var3, var1]) # 4 cols nzvals = [1.0, 2.5, 1.0, 4.0, 5.0, 1.2] int_nzrows = ClMP.getuid.(nzrows) int_nzcols = ClMP.getuid.(nzcols) dyn_mat = DynamicSparseArrays.dynamicsparse(nzrows, nzcols, nzvals) mat = SparseArrays.sparse(int_nzrows, int_nzcols, nzvals, 5, 4) nzinds_sol1 = ClMP.getid.([var1, var3])::Vector{ClMP.VarId} nzvals_sol1 = [2.0, 4.0] sol_len4 = ClMP.PrimalSolution(form, nzinds_sol1, nzvals_sol1, 2.0, ClB.FEASIBLE_SOL) vec_len4 = sparsevec(nzinds_sol1, nzvals_sol1, 4) int_vec_len4 = sparsevec(ClMP.getuid.(nzinds_sol1), nzvals_sol1, 4) nzinds_sol2 = ClMP.getid.([var2, var4])::Vector{ClMP.VarId} nzvals_sol2 = [2.5, 4.5] sol_len5 = ClMP.PrimalSolution(form, nzinds_sol2, nzvals_sol2, 2.0, ClB.FEASIBLE_SOL) vec_len5 = sparsevec(nzinds_sol2, nzvals_sol2, 5) int_vec_len5 = sparsevec(ClMP.getuid.(nzinds_sol2), nzvals_sol2, 5) a = mat * int_vec_len4 b = dyn_mat * sol_len4 c = dyn_mat * vec_len4 @test a == b == c e = transpose(mat) * int_vec_len5 f = transpose(dyn_mat) * sol_len5 g = transpose(dyn_mat) * vec_len5 @test e == f == g end register!(unit_tests, "solutions", lin_alg_3_spMv)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	1716	function convert_MOI_Coluna() @test ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}() == MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo}(-1) @test ClMP.MoiConstrIndex() == MOI.ConstraintIndex{MOI.ScalarAffineFunction{Float64},MOI.LessThan{Float64}}(-1) @test ClMP.MoiVarIndex() == MOI.VariableIndex(-1) @test ClMP.MoiVarKind() == MOI.ConstraintIndex{MOI.VariableIndex,MOI.Integer}(-1) @test ClMP.convert_moi_sense_to_coluna(MOI.LessThan{Float64}(0.0)) == ClMP.Less @test ClMP.convert_moi_sense_to_coluna(MOI.GreaterThan{Float64}(0.0)) == ClMP.Greater @test ClMP.convert_moi_sense_to_coluna(MOI.EqualTo{Float64}(0.0)) == ClMP.Equal @test ClMP.convert_moi_rhs_to_coluna(MOI.LessThan{Float64}(-12.3)) == -12.3 @test ClMP.convert_moi_rhs_to_coluna(MOI.GreaterThan{Float64}(-12.3)) == -12.3 @test ClMP.convert_moi_rhs_to_coluna(MOI.EqualTo{Float64}(-12.3)) == -12.3 @test ClMP.convert_moi_bounds_to_coluna(MOI.LessThan{Float64}(3.0)) == (-Inf, 3.0) @test ClMP.convert_moi_bounds_to_coluna(MOI.GreaterThan{Float64}(4.0)) == (4.0, Inf) @test ClMP.convert_moi_bounds_to_coluna(MOI.EqualTo{Float64}(5.0)) == (5.0, 5.0) @test ClMP.convert_moi_bounds_to_coluna(MOI.Interval{Float64}(1.0, 2.0)) == (1.0, 2.0) @test ClMP.convert_moi_kind_to_coluna(MOI.ZeroOne()) == ClMP.Binary @test ClMP.convert_moi_kind_to_coluna(MOI.Integer()) == ClMP.Integ @test ClMP.convert_coluna_sense_to_moi(ClMP.Less) == MOI.LessThan{Float64} @test ClMP.convert_coluna_sense_to_moi(ClMP.Greater) == MOI.GreaterThan{Float64} @test ClMP.convert_coluna_sense_to_moi(ClMP.Equal) == MOI.EqualTo{Float64} return end register!(unit_tests, "types", convert_MOI_Coluna)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	8768	function getters_and_setters() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -1.0, ub = 1.0, kind = ClMP.Integ, inc_val = 4.0 ) varid = ClMP.getid(var) @test ClMP.getperencost(form, varid) == 2.0 @test ClMP.getperenlb(form, varid) == -1.0 @test ClMP.getperenub(form, varid) == 1.0 @test ClMP.getperensense(form, varid) == ClMP.Free @test ClMP.getperenkind(form, varid) == ClMP.Integ @test ClMP.getperenincval(form, varid) == 4.0 @test ClMP.getcurcost(form, varid) == 2.0 @test ClMP.getcurlb(form, varid) == -1.0 @test ClMP.getcurub(form, varid) == 1.0 @test ClMP.getcursense(form, varid) == ClMP.Free @test ClMP.getcurkind(form, varid) == ClMP.Integ @test ClMP.getcurincval(form, varid) == 4.0 ClMP.setcurcost!(form, varid, 3.0) ClMP.setcurlb!(form, varid, -2.0) ClMP.setcurub!(form, varid, 2.0) ClMP.setcurkind!(form, varid, ClMP.Continuous) ClMP.setcurincval!(form, varid, 3.0) @test ClMP.getcurcost(form, varid) == 3.0 @test ClMP.getcurlb(form, varid) == -2.0 @test ClMP.getcurub(form, varid) == 2.0 @test ClMP.getcursense(form, varid) == ClMP.Free @test ClMP.getcurkind(form, varid) == ClMP.Continuous @test ClMP.getcurincval(form, varid) == 3.0 ClMP.reset!(form, varid) @test ClMP.getcurcost(form, varid) == 2.0 @test ClMP.getcurlb(form, varid) == -1.0 @test ClMP.getcurub(form, varid) == 1.0 @test ClMP.getcursense(form, varid) == ClMP.Free @test ClMP.getcurkind(form, varid) == ClMP.Integ @test ClMP.getcurincval(form, varid) == 4.0 end register!(unit_tests, "variables", getters_and_setters) function bounds_of_binary_variable_1() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, kind = ClMP.Binary ) @test ClMP.getperenlb(form, var) == 0.0 @test ClMP.getperenub(form, var) == 1.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 ClMP.setperenlb!(form, var, -1.0) ClMP.setperenub!(form, var, 2.0) @test ClMP.getperenlb(form, var) == 0.0 @test ClMP.getperenub(form, var) == 1.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 ClMP.setcurlb!(form, var, -1.1) ClMP.setcurub!(form, var, 2.1) @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 ClMP.setperenlb!(form, var, 0.1) ClMP.setperenub!(form, var, 0.9) @test ClMP.getperenlb(form, var) == 0.1 @test ClMP.getperenub(form, var) == 0.9 @test ClMP.getcurlb(form, var) == 0.1 @test ClMP.getcurub(form, var) == 0.9 ClMP.setcurlb!(form, var, 0.2) ClMP.setcurub!(form, var, 0.8) @test ClMP.getperenlb(form, var) == 0.1 @test ClMP.getperenub(form, var) == 0.9 @test ClMP.getcurlb(form, var) == 0.2 @test ClMP.getcurub(form, var) == 0.8 end register!(unit_tests, "variables", bounds_of_binary_variable_1) function bounds_of_binary_variable_2() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, kind = ClMP.Continuous, lb = -10.0, ub = 10.0 ) @test ClMP.getperenlb(form, var) == -10.0 @test ClMP.getperenub(form, var) == 10.0 @test ClMP.getcurlb(form, var) == -10.0 @test ClMP.getcurub(form, var) == 10.0 ClMP.setperenkind!(form, var, ClMP.Binary) @test ClMP.getperenlb(form, var) == 0.0 @test ClMP.getperenub(form, var) == 1.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 end register!(unit_tests, "variables", bounds_of_binary_variable_2) function bounds_of_binary_variable_3() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, kind = ClMP.Continuous, lb = -10.0, ub = 10.0 ) @test ClMP.getperenlb(form, var) == -10.0 @test ClMP.getperenub(form, var) == 10.0 @test ClMP.getcurlb(form, var) == -10.0 @test ClMP.getcurub(form, var) == 10.0 ClMP.setcurkind!(form, var, ClMP.Binary) @test ClMP.getperenlb(form, var) == -10.0 @test ClMP.getperenub(form, var) == 10.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 end register!(unit_tests, "variables", bounds_of_binary_variable_3) function record() v_rec = ClMP.MoiVarRecord(; index = ClMP.MoiVarIndex(-15)) @test ClMP.getmoiindex(v_rec) == ClMP.MoiVarIndex(-15) @test ClMP.getlowerbound(v_rec) == ClMP.MoiVarLowerBound(-1) @test ClMP.getupperbound(v_rec) == ClMP.MoiVarUpperBound(-1) ClMP.setmoiindex!(v_rec, ClMP.MoiVarIndex(-20)) ClMP.setlowerbound!(v_rec, ClMP.MoiVarLowerBound(10)) ClMP.setupperbound!(v_rec, ClMP.MoiVarUpperBound(20)) @test ClMP.getmoiindex(v_rec) == ClMP.MoiVarIndex(-20) @test ClMP.getlowerbound(v_rec) == ClMP.MoiVarLowerBound(10) @test ClMP.getupperbound(v_rec) == ClMP.MoiVarUpperBound(20) end register!(unit_tests, "variables", record) function add_in_partial_sol_variable_1() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -3.0, ub = 3.0, kind = ClMP.Integ, inc_val = 4.0 ) DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) varid = ClMP.getid(var) @test ClMP.iscuractive(form, var) @test ClMP.isexplicit(form, var) @test !ClMP.in_partial_sol(form, var) @test ClMP.get_value_in_partial_sol(form, var) == 0 ClMP.add_to_partial_solution!(form, var, -1.0, true) @test ClMP.getcurub(form, var) == 0 @test ClMP.getcurlb(form, var) == -2 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.in_partial_sol(form, var) @test ClMP.get_value_in_partial_sol(form, var) == -1 @test ClMP.iscuractive(form, var) end register!(unit_tests, "variables", add_in_partial_sol_variable_1) function add_in_partial_sol_variable_2() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -3.0, ub = 3.0, kind = ClMP.Integ, inc_val = 4.0 ) DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) varid = ClMP.getid(var) ClMP.deactivate!(form, varid) @test !ClMP.iscuractive(form, varid) ClMP.add_to_partial_solution!(form, var, -1.0, true) # try an unactive variable -> should work. @test ClMP.getcurub(form, var) == 0 @test ClMP.getcurlb(form, var) == -2 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == -1 end register!(unit_tests, "variables", add_in_partial_sol_variable_2) function add_in_partial_sol_variable_3() # sequential fix form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -3.0, ub = 3.0, kind = ClMP.Integ, inc_val = 4.0 ) DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) varid = ClMP.getid(var) ClMP.add_to_partial_solution!(form, var, 0.0, true) @test ClMP.getcurub(form, var) == 3 @test ClMP.getcurlb(form, var) == -3 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == 0 @test ClMP.iscuractive(form, var) ClMP.add_to_partial_solution!(form, var, 1.0, true) @test ClMP.getcurub(form, var) == 2 @test ClMP.getcurlb(form, var) == 0 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == 1 @test ClMP.iscuractive(form, var) ClMP.add_to_partial_solution!(form, var, -1.0, true) @test ClMP.getcurub(form, var) == 3 @test ClMP.getcurlb(form, var) == -3 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == 0 @test ClMP.iscuractive(form, var) ClMP.add_to_partial_solution!(form, var, -1.0, true) @test ClMP.getcurub(form, var) == 0 @test ClMP.getcurlb(form, var) == -2 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == -1 @test ClMP.iscuractive(form, var) end register!(unit_tests, "variables", add_in_partial_sol_variable_3)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	1338	function id_equality() # vid1 & vid2 have same uid 1. vid3 has uid 2. vid1 = ClMP.VarId(ClMP.OriginalVar, 1, 1) vid2 = ClMP.VarId(ClMP.DwSpPricingVar, 1, 2) vid3 = ClMP.VarId(ClMP.OriginalVar, 2, 1) @test vid1 == vid2 @test vid1 != vid3 dict = Dict{ClMP.VarId, Float64}() dict[vid1] = 1.0 dict[vid3] = 2.0 @test dict[vid1] == 1.0 @test dict[vid2] == 1.0 @test dict[vid3] == 2.0 dict[vid2] = 3.0 @test dict[vid1] == 3.0 @test dict[vid2] == 3.0 @test dict[vid3] == 2.0 @test haskey(dict, vid1) @test haskey(dict, vid2) @test haskey(dict, vid3) end register!(unit_tests, "vcids", id_equality) function math_operations() vid1 = ClMP.VarId(ClMP.OriginalVar, 1, 1) @test vid1 + 1 == 2 @test vid1 - 1 == 0 @test vid1 < 2 @test vid1 <= 2 @test 2 >= vid1 @test 2 > vid1 @test vid1 == 1 @test isequal(vid1, 1) vid2 = ClMP.VarId(ClMP.OriginalVar, 2, 1) @test vid1 < vid2 @test vid1 <= vid2 @test vid2 >= vid1 @test vid2 > vid1 vid3 = ClMP.VarId(ClMP.OriginalVar, 2, 2) @test vid2 <= vid3 @test vid2 >= vid3 @test vid2 == vid3 @test isequal(vid2, vid3) @test vid1 + vid2 == 3 @test vid1 - vid2 == -1 @test vid1 * vid2 == 2 end register!(unit_tests, "vcids", math_operations)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	413	Coluna.MustImplement.@mustimplement "API" mi_f1() = nothing Coluna.MustImplement.@mustimplement "API" mi_f2(a, b) = nothing mi_f2(a::Int, b::Int) = a+b function must_implement() @test_throws Coluna.MustImplement.IncompleteInterfaceError mi_f1() @test_throws Coluna.MustImplement.IncompleteInterfaceError mi_f2("a", "b") @test mi_f2(1,2) == 3 end register!(unit_tests, "MustImplement", must_implement)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	3462	function parser_strip_indentation() s = "1.1x_1" @test Parser._strip_identation(s) == "1.1x_1" s = " 1.1x_1" @test Parser._strip_identation(s) == "1.1x_1" s = " 1.1x_1" @test Parser._strip_identation(s) == "1.1x_1" end register!(unit_tests, "parser", parser_strip_indentation) function parser_strip_line() s = "1.1x_1,2.2y_1,3.3z_1" @test Parser._strip_line(s) == "1.1x_1,2.2y_1,3.3z_1" s = " 1.1x_1, 2.2y_1, 3.3z_1 " @test Parser._strip_line(s) == "1.1x_1,2.2y_1,3.3z_1" s = " 1.1x_1 , 2.2y_1 , 3.3z_1 " @test Parser._strip_line(s) == "1.1x_1,2.2y_1,3.3z_1" end register!(unit_tests, "parser", parser_strip_line) function parser_get_vars_list() s = "x_1,y_1,z_1" @test Parser._get_vars_list(s) == ["x_1", "y_1", "z_1"] s = " x_1, y_1, z_1 " @test Parser._get_vars_list(s) == ["x_1", "y_1", "z_1"] s = " x_1 , y_1 , z_1 " @test Parser._get_vars_list(s) == ["x_1", "y_1", "z_1"] end register!(unit_tests, "parser", parser_get_vars_list) function parser_read_expression() s = "x_1 + y_1 - z_1" @test Parser._read_expression(s).vars == Dict("y_1" => 1.0, "x_1" => 1.0, "z_1" => -1.0) s = "- x_1 + 2.5y_1 - 3z_1" @test Parser._read_expression(s).vars == Dict("y_1" => 2.5, "x_1" => -1.0, "z_1" => -3.0) s = "2x_1 + 6y_1 - 1.1z_1" @test Parser._read_expression(s).vars == Dict("y_1" => 6.0, "x_1" => 2.0, "z_1" => -1.1) end register!(unit_tests, "parser", parser_read_expression) function parser_read_constraint() s = "==" @test Parser._read_constraint(s) === nothing s = "x_1 + y_1" @test Parser._read_constraint(s) === nothing s = "x_1 + y_1 <=" @test Parser._read_constraint(s) === nothing s = "x_1 + y_1 >= z_1" @test Parser._read_constraint(s) === nothing s = "x_1 + 1.2y_1 == 5" c = Parser._read_constraint(s) @test c.lhs.vars == Dict("y_1" => 1.2, "x_1" => 1.0) @test c.sense == ClMP.Equal @test c.rhs == 5.0 s = "-4x_1 + 1.2y_1 <= 5" c = Parser._read_constraint(s) @test c.lhs.vars == Dict("y_1" => 1.2, "x_1" => -4.0) @test c.sense == ClMP.Less @test c.rhs == 5.0 s = "-4.25*x_1 + 2y_1 >= 5" c = Parser._read_constraint(s) @test c.lhs.vars == Dict("y_1" => 2.0, "x_1" => -4.25) @test c.sense == ClMP.Greater @test c.rhs == 5.0 end register!(unit_tests, "parser", parser_read_constraint) function parser_read_bounds() less_r = Regex("(($(Parser.coeff_re))<=)?([\\w,]+)(<=($(Parser.coeff_re)))?") greater_r = Regex("(($(Parser.coeff_re))>=)?([\\w,]+)(>=($(Parser.coeff_re)))?") s = "" @test Parser._read_bounds(s, less_r) == ([], "", "") s = "y == 10" @test Parser._read_bounds(s, less_r) == (["y"], "", "") @test Parser._read_bounds(s, greater_r) == (["y"], "", "") s = "20 <= x" @test Parser._read_bounds(s, less_r) == (["x"], "20", "") s = "20 <= x <= 21.5" @test Parser._read_bounds(s, less_r) == (["x"], "20", "21.5") s = "20 <= x1, x2 <= 21.5" @test Parser._read_bounds(s, less_r) == (["x1", "x2"], "20", "21.5") s = "21.5 >= x" @test Parser._read_bounds(s, greater_r) == (["x"], "21.5", "") s = "21.5 >= x >= 20" @test Parser._read_bounds(s, greater_r) == (["x"], "21.5", "20") s = "21.5 >= x1, x2 >= 20" @test Parser._read_bounds(s, greater_r) == (["x1", "x2"], "21.5", "20") end register!(unit_tests, "parser", parser_read_bounds)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	41187	function presolve_toy_gap_with_penalties() form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 2.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 global_bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 0.1 <= y_1 <= 1.1 0.2 <= y_2 <= 1.2 0.3 <= y_3 <= 1.3 0.4 <= y_4 <= 1.4 0.5 <= y_5 <= 1.5 0.6 <= y_6 <= 1.6 0.7 <= y_7 <= 1.7 """ env, master, sps, _, reform = reformfromstring(form) return reform, master, sps end function build_dw_presolve_reformulation() reform, master, sps = presolve_toy_gap_with_penalties() presolve_reform = Coluna.Algorithm.create_presolve_reform(reform) presolve_original_master = presolve_reform.representative_master mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_original_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["y_2"], 0.2, 1.2), (mast_var_ids["y_3"], 0.3, 1.3), (mast_var_ids["y_4"], 0.4, 1.4), (mast_var_ids["y_5"], 0.5, 1.5), (mast_var_ids["y_6"], 0.6, 1.6), (mast_var_ids["y_7"], 0.7, 1.7), (mast_var_ids["x_11"], 0.0, 1.0), (mast_var_ids["x_12"], 0.0, 1.0), (mast_var_ids["x_13"], 0.0, 1.0), (mast_var_ids["x_14"], 0.0, 1.0), (mast_var_ids["x_15"], 0.0, 1.0), (mast_var_ids["x_16"], 0.0, 1.0), (mast_var_ids["x_17"], 0.0, 1.0), (mast_var_ids["x_21"], 0.0, 1.0), (mast_var_ids["x_22"], 0.0, 1.0), (mast_var_ids["x_23"], 0.0, 1.0), (mast_var_ids["x_24"], 0.0, 1.0), (mast_var_ids["x_25"], 0.0, 1.0), (mast_var_ids["x_26"], 0.0, 1.0), (mast_var_ids["x_27"], 0.0, 1.0) ] @test presolve_original_master.form.lower_multiplicity == 1 @test presolve_original_master.form.upper_multiplicity == 1 for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_original_master.form.lbs[varuid] == lb @test presolve_original_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_original_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_original_master.form.rhs[construid] == rhs @test presolve_original_master.form.sense[construid] == sense end presolve_restricted_master = presolve_reform.restricted_master @test presolve_restricted_master.form.lower_multiplicity == 1 @test presolve_restricted_master.form.upper_multiplicity == 1 mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_restricted_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["MC_38"], 0.0, Inf), (mast_var_ids["MC_39"], 0.0, Inf), (mast_var_ids["MC_40"], 0.0, Inf), (mast_var_ids["MC_41"], 0.0, Inf), (mast_var_ids["MC_42"], 0.0, Inf), (mast_var_ids["MC_43"], 0.0, Inf), (mast_var_ids["MC_44"], 0.0, Inf), (mast_var_ids["MC_45"], 0.0, Inf), (mast_var_ids["local_art_of_cov_5"], 0.0, Inf), (mast_var_ids["local_art_of_cov_4"], 0.0, Inf), (mast_var_ids["local_art_of_cov_6"], 0.0, Inf), (mast_var_ids["local_art_of_cov_7"], 0.0, Inf), (mast_var_ids["local_art_of_cov_2"], 0.0, Inf), (mast_var_ids["local_art_of_cov_3"], 0.0, Inf), (mast_var_ids["local_art_of_cov_1"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_4"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_4"], 0.0, Inf), (mast_var_ids["global_pos_art_var"], 0.0, Inf), (mast_var_ids["global_neg_art_var"], 0.0, Inf), (mast_var_ids["local_art_of_limit_pen"], 0.0, Inf), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_restricted_master.form.lbs[varuid] == lb @test presolve_restricted_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_restricted_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), (mast_constr_ids["c9"], 0.0, ClMP.Greater), (mast_constr_ids["c10"], 1.0, ClMP.Less), (mast_constr_ids["c11"], 0.0, ClMP.Greater), (mast_constr_ids["c12"], 1.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_restricted_master.form.rhs[construid] == rhs @test presolve_restricted_master.form.sense[construid] == sense end # Test coefficient matrix restricted_coef_matrix = [ (mast_constr_ids["c1"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c1"], mast_var_ids["local_art_of_cov_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c2"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c2"], mast_var_ids["local_art_of_cov_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c3"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c3"], mast_var_ids["local_art_of_cov_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c4"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c4"], mast_var_ids["local_art_of_cov_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c5"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c5"], mast_var_ids["local_art_of_cov_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c6"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c6"], mast_var_ids["local_art_of_cov_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c7"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c7"], mast_var_ids["local_art_of_cov_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c8"], mast_var_ids["local_art_of_limit_pen"], -1.0), (mast_constr_ids["c8"], mast_var_ids["global_neg_art_var"], -1.0), (mast_constr_ids["c9"], mast_var_ids["local_art_of_sp_lb_5"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c10"], mast_var_ids["local_art_of_sp_ub_5"], -1.0), (mast_constr_ids["c10"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c11"], mast_var_ids["local_art_of_sp_lb_4"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c12"], mast_var_ids["local_art_of_sp_ub_4"], -1.0), (mast_constr_ids["c12"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_45"], 1.0), ] for (c, v, val) in restricted_coef_matrix @test presolve_restricted_master.form.col_major_coef_matrix[c, v] == val end dw_sp = ClMP.get_dw_pricing_sps(reform)[5] presolve_dw_sp = presolve_reform.dw_sps[5] @test presolve_dw_sp.form.lower_multiplicity == 0 @test presolve_dw_sp.form.upper_multiplicity == 1 sp_var_ids = Dict{String, Int}(ClMP.getname(dw_sp, var) => k for (k,var) in enumerate(presolve_dw_sp.col_to_var)) var_ids_lbs_ubs = [ (sp_var_ids["x_11"], 0.0, 1.0), (sp_var_ids["x_12"], 0.0, 1.0), (sp_var_ids["x_13"], 0.0, 1.0), (sp_var_ids["x_14"], 0.0, 1.0), (sp_var_ids["x_15"], 0.0, 1.0), (sp_var_ids["x_16"], 0.0, 1.0), (sp_var_ids["x_17"], 0.0, 1.0), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_dw_sp.form.lbs[varuid] == lb @test presolve_dw_sp.form.ubs[varuid] == ub end sp_constr_ids = Dict{String, Int}(ClMP.getname(dw_sp, constr) => k for (k, constr) in enumerate(presolve_dw_sp.row_to_constr)) constr_ids = [ sp_constr_ids["sp_c2"], ] constr_rhs = [ 5.0, ] constr_sense = [ ClMP.Less, ] for (k, construid) in enumerate(constr_ids) @test presolve_dw_sp.form.rhs[construid] == constr_rhs[k] @test presolve_dw_sp.form.sense[construid] == constr_sense[k] end # Test coefficient matrix @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_11"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_12"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_13"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_14"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_15"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_16"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_17"]] == 1.0 end register!(unit_tests, "presolve_reformulation", build_dw_presolve_reformulation) function presolve_toy_gap_with_penalties2() form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 2.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 3.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 <= 5.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 2.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 integer representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.1 <= x_11 <= 1.0 0.2 <= x_12 <= 1.0 0.3 <= x_13 <= 1.0 0.4 <= x_14 <= 1.0 0.5 <= x_15 <= 1.0 0.6 <= x_16 <= 1.0 0.7 <= x_17 <= 1.0 0.8 <= x_21 <= 1.0 0.9 <= x_22 <= 1.0 1.0 <= x_23 <= 2.0 1.1 <= x_24 <= 2.0 1.2 <= x_25 <= 2.0 1.3 <= x_26 <= 2.0 1.4 <= x_27 <= 2.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 0.1 <= y_1 <= 1.1 0.2 <= y_2 <= 1.2 0.3 <= y_3 <= 1.3 0.4 <= y_4 <= 1.4 0.5 <= y_5 <= 1.5 0.6 <= y_6 <= 1.6 0.7 <= y_7 <= 1.7 """ env, master, sps, _, reform = reformfromstring(form) return reform, master, sps end function build_dw_presolve_reformulation2() reform, master, sps = presolve_toy_gap_with_penalties2() presolve_reform = Coluna.Algorithm.create_presolve_reform(reform) presolve_original_master = presolve_reform.representative_master mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_original_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["y_2"], 0.2, 1.2), (mast_var_ids["y_3"], 0.3, 1.3), (mast_var_ids["y_4"], 0.4, 1.4), (mast_var_ids["y_5"], 0.5, 1.5), (mast_var_ids["y_6"], 0.6, 1.6), (mast_var_ids["y_7"], 0.7, 1.7), ] @test presolve_original_master.form.lower_multiplicity == 1 @test presolve_original_master.form.upper_multiplicity == 1 for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_original_master.form.lbs[varuid] == lb @test presolve_original_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_original_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_original_master.form.rhs[construid] == rhs @test presolve_original_master.form.sense[construid] == sense end presolve_restricted_master = presolve_reform.restricted_master @test presolve_restricted_master.form.lower_multiplicity == 1 @test presolve_restricted_master.form.upper_multiplicity == 1 mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_restricted_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["MC_38"], 0.0, Inf), (mast_var_ids["MC_39"], 0.0, Inf), (mast_var_ids["MC_40"], 0.0, Inf), (mast_var_ids["MC_41"], 0.0, Inf), (mast_var_ids["MC_42"], 0.0, Inf), (mast_var_ids["MC_43"], 0.0, Inf), (mast_var_ids["MC_44"], 0.0, Inf), (mast_var_ids["MC_45"], 0.0, Inf), (mast_var_ids["local_art_of_cov_5"], 0.0, Inf), (mast_var_ids["local_art_of_cov_4"], 0.0, Inf), (mast_var_ids["local_art_of_cov_6"], 0.0, Inf), (mast_var_ids["local_art_of_cov_7"], 0.0, Inf), (mast_var_ids["local_art_of_cov_2"], 0.0, Inf), (mast_var_ids["local_art_of_cov_3"], 0.0, Inf), (mast_var_ids["local_art_of_cov_1"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_4"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_4"], 0.0, Inf), (mast_var_ids["global_pos_art_var"], 0.0, Inf), (mast_var_ids["global_neg_art_var"], 0.0, Inf), (mast_var_ids["local_art_of_limit_pen"], 0.0, Inf), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_restricted_master.form.lbs[varuid] == lb @test presolve_restricted_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_restricted_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), (mast_constr_ids["c9"], 3.0, ClMP.Greater), (mast_constr_ids["c10"], 5.0, ClMP.Less), (mast_constr_ids["c11"], 0.0, ClMP.Greater), (mast_constr_ids["c12"], 2.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_restricted_master.form.rhs[construid] == rhs @test presolve_restricted_master.form.sense[construid] == sense end # Test coefficient matrix restricted_coef_matrix = [ (mast_constr_ids["c1"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c1"], mast_var_ids["local_art_of_cov_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c2"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c2"], mast_var_ids["local_art_of_cov_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c3"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c3"], mast_var_ids["local_art_of_cov_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c4"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c4"], mast_var_ids["local_art_of_cov_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c5"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c5"], mast_var_ids["local_art_of_cov_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c6"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c6"], mast_var_ids["local_art_of_cov_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c7"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c7"], mast_var_ids["local_art_of_cov_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c8"], mast_var_ids["local_art_of_limit_pen"], -1.0), (mast_constr_ids["c8"], mast_var_ids["global_neg_art_var"], -1.0), (mast_constr_ids["c9"], mast_var_ids["local_art_of_sp_lb_5"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c10"], mast_var_ids["local_art_of_sp_ub_5"], -1.0), (mast_constr_ids["c10"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c11"], mast_var_ids["local_art_of_sp_lb_4"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c12"], mast_var_ids["local_art_of_sp_ub_4"], -1.0), (mast_constr_ids["c12"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_45"], 1.0), ] for (c, v, val) in restricted_coef_matrix @test presolve_restricted_master.form.col_major_coef_matrix[c, v] == val end dw_sp = ClMP.get_dw_pricing_sps(reform)[5] presolve_dw_sp = presolve_reform.dw_sps[5] @test presolve_dw_sp.form.lower_multiplicity == 3 @test presolve_dw_sp.form.upper_multiplicity == 5 sp_var_ids = Dict{String, Int}(ClMP.getname(dw_sp, var) => k for (k,var) in enumerate(presolve_dw_sp.col_to_var)) var_ids_lbs_ubs = [ (sp_var_ids["x_11"], 0.1, 1.0), (sp_var_ids["x_12"], 0.2, 1.0), (sp_var_ids["x_13"], 0.3, 1.0), (sp_var_ids["x_14"], 0.4, 1.0), (sp_var_ids["x_15"], 0.5, 1.0), (sp_var_ids["x_16"], 0.6, 1.0), (sp_var_ids["x_17"], 0.7, 1.0), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_dw_sp.form.lbs[varuid] == lb @test presolve_dw_sp.form.ubs[varuid] == ub end sp_constr_ids = Dict{String, Int}(ClMP.getname(dw_sp, constr) => k for (k, constr) in enumerate(presolve_dw_sp.row_to_constr)) constr_ids = [ sp_constr_ids["sp_c2"], ] constr_rhs = [ 5.0, ] constr_sense = [ ClMP.Less, ] for (k, construid) in enumerate(constr_ids) @test presolve_dw_sp.form.rhs[construid] == constr_rhs[k] @test presolve_dw_sp.form.sense[construid] == constr_sense[k] end # Test coefficient matrix @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_11"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_12"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_13"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_14"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_15"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_16"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_17"]] == 1.0 end register!(unit_tests, "presolve_reformulation", build_dw_presolve_reformulation2) function presolve_reformulation_with_var_not_in_coeff_matrix() form = """ master min 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_4 s.t. 0.0 x_11 + 1.0 x_12 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 >= 1.0 1.0 x_13 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_14 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 >= 1.0 1.0 x_15 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 >= 1.0 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_11 + 1.0 x_12 + 1.0 x_13 + 3.0 x_14 + 1.0 x_15 + 5.0 x_16 + 4.0 x_17 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4 binary representatives x_11, x_12, x_13, x_14, x_15, x_16, x_17 global_bounds 0.0 <= x_11 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_17 <= 1.0 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_17 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 """ env, master, sps, _, reform = reformfromstring(form) return reform, master, sps end function build_dw_presolve_reformulation_with_var_not_in_coeff_matrix() # We create a reformulation several subproblem variables does not appear in the master problem. # x11 does not appear in the coefficient matrix reform, master, sps = presolve_reformulation_with_var_not_in_coeff_matrix() presolve_reform = Coluna.Algorithm.create_presolve_reform(reform) presolve_original_master = presolve_reform.representative_master mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_original_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["x_11"], 0, 1), (mast_var_ids["x_12"], 0, 1), (mast_var_ids["x_13"], 0, 1), (mast_var_ids["x_14"], 0, 1), (mast_var_ids["x_15"], 0, 1), (mast_var_ids["x_16"], 0, 1), (mast_var_ids["x_17"], 0, 1) ] @test presolve_original_master.form.lower_multiplicity == 1 @test presolve_original_master.form.upper_multiplicity == 1 for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_original_master.form.lbs[varuid] == lb @test presolve_original_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_original_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_original_master.form.rhs[construid] == rhs @test presolve_original_master.form.sense[construid] == sense end restricted_coef_matrix = [ (mast_constr_ids["c1"], mast_var_ids["x_12"], 1.0), (mast_constr_ids["c2"], mast_var_ids["x_13"], 1.0), (mast_constr_ids["c3"], mast_var_ids["x_14"], 1.0), (mast_constr_ids["c4"], mast_var_ids["x_15"], 1.0), (mast_constr_ids["c5"], mast_var_ids["x_16"], 1.0), (mast_constr_ids["c6"], mast_var_ids["x_17"], 1.0), (mast_constr_ids["c1"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c2"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c3"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c4"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c5"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c6"], mast_var_ids["x_11"], 0.0), ] for (c, v, val) in restricted_coef_matrix @test presolve_original_master.form.col_major_coef_matrix[c, v] == val end end register!(unit_tests, "presolve_reformulation", build_dw_presolve_reformulation_with_var_not_in_coeff_matrix)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	3402	function test_non_proper_column1() # min x1 + x2 + 1MC1 + 2MC2 + 4MC3 # s.t. x1 + x2 + MC1 + MC2 + MC3 >= 2 # 0 <= x1 <= 1 # 0 <= x2 <= 2 # with # MC1 = [x1 = 1] # MC2 = [x2 = 2] # MC3 = [x1 = 2, x2 = 2] # non-proper! env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) master_form, master_name_to_var, master_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwMaster(), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 1.0, nothing, nothing), ("x2", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 2.0, nothing, nothing), ("MC1", Coluna.MathProg.MasterCol, 1.0, 0.0, 1.0, nothing, 2), ("MC2", Coluna.MathProg.MasterCol, 2.0, 0.0, 1.0, nothing, 2), ("MC3", Coluna.MathProg.MasterCol, 4.0, 0.0, 1.0, nothing, 2), ], [ # name, duty, rhs, sense , id ("c1", Coluna.MathProg.MasterMixedConstr, 2.0, ClMP.Greater, nothing), ] ) coeffs = [ # var, constr, coeff ("c1", "x1", 1.0), ("c1", "x2", 1.0), ("c1", "MC1", 1.0), ("c1", "MC2", 1.0), ("c1", "MC3", 1.0), ] master_form_coef_matrix = Coluna.MathProg.getcoefmatrix(master_form) for (constr_name, var_name, coef) in coeffs constr = master_name_to_constr[constr_name] var = master_name_to_var[var_name] master_form_coef_matrix[ClMP.getid(constr), ClMP.getid(var)] = coef end DynamicSparseArrays.closefillmode!(master_form_coef_matrix) sp_form, sp_name_to_var, sp_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwSp( nothing, nothing, nothing, ClMP.Continuous, 1.0, Coluna.MathProg.Pool() ), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 1.0, Coluna.Algorithm.getid(master_name_to_var["x1"]), nothing), ("x2", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 2.0, Coluna.Algorithm.getid(master_name_to_var["x2"]), nothing) ], [ # name, duty, rhs, sense, id ("c2", Coluna.MathProg.DwSpPureConstr, 2.0, ClMP.Less, nothing) ], ) var_ids = [Coluna.MathProg.getid(sp_name_to_var["x1"]), Coluna.MathProg.getid(sp_name_to_var["x2"])] pool = Coluna.MathProg.get_primal_sol_pool(sp_form) for (name, vals) in Iterators.zip( ["MC1", "MC2", "MC3"], [ #x1, x2, Float64[1.0, 0.0], Float64[0.0, 1.0], Float64[2.0, 2.0] ] ) col_id = Coluna.MathProg.VarId(Coluna.MathProg.getid(master_name_to_var[name]); duty = Coluna.MathProg.DwSpPrimalSol) Coluna.MathProg.push_in_pool!( pool, Coluna.MathProg.PrimalSolution(sp_form, var_ids, vals, 1.0, Coluna.MathProg.FEASIBLE_SOL), col_id, 1.0 ) end @test Coluna.Algorithm._column_is_proper(Coluna.getid(master_name_to_var["MC1"]), sp_form) === true @test Coluna.Algorithm._column_is_proper(Coluna.getid(master_name_to_var["MC2"]), sp_form) === true @test Coluna.Algorithm._column_is_proper(Coluna.getid(master_name_to_var["MC3"]), sp_form) === false return end register!(unit_tests, "columns", test_non_proper_column1)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	31715	function test_lb_precision() z = 1.19999999999999999 a = 1.2 + 1e-5 b = 1.2 + 1e-6 c = 0.30000000000000004 # floating point error d = 0.30000000000000008 # floating point error e = 0.29999999999999998 # floating point error f = 0.29999999999999994 # floating point error @test Coluna.Algorithm._lb_prec(z) == 1.2 @test Coluna.Algorithm._lb_prec(a) == a @test Coluna.Algorithm._lb_prec(b) == 1.2 @test Coluna.Algorithm._lb_prec(c) == 0.3 @test Coluna.Algorithm._lb_prec(d) == 0.3 @test Coluna.Algorithm._lb_prec(e) == 0.3 @test Coluna.Algorithm._lb_prec(f) == 0.3 end register!(unit_tests, "presolve_helper", test_lb_precision) function test_ub_precision() z = 1.20000000000000001 a = 1.2 - 1e-5 b = 1.2 - 1e-6 c = 0.30000000000000004 # floating point error d = 0.30000000000000008 # floating point error e = 0.29999999999999998 # floating point error f = 0.29999999999999994 # floating point error @test Coluna.Algorithm._ub_prec(z) == 1.2 @test Coluna.Algorithm._ub_prec(a) == a @test Coluna.Algorithm._ub_prec(b) == 1.2 @test Coluna.Algorithm._ub_prec(c) == 0.3 @test Coluna.Algorithm._ub_prec(d) == 0.3 @test Coluna.Algorithm._ub_prec(e) == 0.3 @test Coluna.Algorithm._ub_prec(f) == 0.3 end register!(unit_tests, "presolve_helper", test_ub_precision) function test_presolve_builder1() coef_matrix = sparse([ 0 0 -1 1 0 1 2.5 # <= 4 0 0 1 -1 0 -1 -2.5 # >= -4 1 0 0 0 1 0 0 # == 1 0 1 2 -4 0 0 0 # >= 2 0 1 2 -4 0 0 0 # <= 1 1 -2 3 5.5 0 1 1 # == 6 ]) rhs = [4, -4, 1, 2, 1, 6] sense = [Less, Greater, Equal, Greater, Less, Equal] lbs = [1, 0, 2, 1, -1, -Inf, 0] ubs = [10, Inf, 3, 2, 1, 0, 1] partial_sol = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1.0, 1.0) @test form.nb_vars == 7 @test form.nb_constrs == 6 @test all(form.col_major_coef_matrix .== coef_matrix) @test all(form.rhs .== rhs) @test all(form.sense .== sense) @test all(form.lbs .== lbs) @test all(form.ubs .== ubs) return end register!(unit_tests, "presolve_helper", test_presolve_builder1; x = true) # Test rows deactivation. function test_presolve_builder2() coef_matrix = sparse([ 0 0 -1 1 0 1 2.5 # <= 4 0 0 1 -1 0 -1 -2.5 # >= -4 1 0 0 0 1 0 0 # == 1 0 1 2 -4 0 0 0 # >= 2 0 1 2 -4 0 0 0 # <= 1 1 -2 3 5.5 0 1 1 # == 6 ]) rhs = [4, -4, 1, 2, 1, 6] sense = [Less, Greater, Equal, Greater, Less, Equal] lbs = [1, 0, 2, 1, -1, -Inf, 0] ubs = [10, Inf, 3, 2, 1, 0, 1] partial_sol = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) # Deactivate some rows. rows_to_deactivate = [1, 3, 6] tightened_bounds = Dict{Int, Tuple{Float64, Bool, Float64, Bool}}() form2, _, _ = Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1.0, 1.0 ) @test form2.nb_vars == 7 @test form2.nb_constrs == 3 @test all(form2.col_major_coef_matrix .== coef_matrix[[2, 4, 5], :]) @test all(form2.rhs .== rhs[[2, 4, 5]] - [12 - 1, 22 - 4, 22 - 4]) @test all(form2.sense .== sense[[2, 4, 5]]) @test all(form2.lbs .== [0, 0, 0, 0, -1, -Inf, 0]) @test all(form2.ubs .== [9, Inf, 1, 1, 1, 0, 1]) @test all(form2.partial_solution .== [1, 0, 2, 1, 0, 0, 0]) end register!(unit_tests, "presolve_helper", test_presolve_builder2; x = true) # Test vars fixing. function test_presolve_builder3() coef_matrix = sparse([ 0 0 -1 1 0 1 2.5 # <= 4 0 0 1 -1 0 -1 -2.5 # >= -4 1 0 0 0 1 0 0 # == 1 0 1 2 -4 0 0 0 # >= 2 0 1 2 -4 0 0 0 # <= 1 1 -2 3 5.5 0 1 1 # == 6 ]) rhs = [4, -4, 1, 2, 1, 6] sense = [Less, Greater, Equal, Greater, Less, Equal] lbs = [10, 2, 1, 1, -1, 0, -1] ubs = [10, 3, 1, 2, 1, 0, -1] partial_sol = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) # Deactivate some rows. rows_to_deactivate = Int[] tightened_bounds = Dict{Int,Tuple{Float64, Bool, Float64, Bool}}() # Fixed variables: # -1 - 2.5 # <= 4 -> 7.5 # 1 + 2.5 # >= -4 -> -7.5 # 10 # == 1 -> -9 # 2 # >= 2 -> 0 # 2 # <= 1 -> -1 # 10+ 3 -1 # == 6 -> -6 # Lower bound reduction: # x2 = 2, x4 = 1 # <= 7.5 - 1 -> 6.5 # >= -7.5 + 1 -> -6.5 # == -9 -> -9 # >= 0 - 2 + 4 -> 2 # <= -1 - 2 + 4 -> 1 # == -6 +22 - 5.5 -> -7.5 form2, _, _ = Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1.0, 1.0 ) @test form2.nb_vars == 3 @test form2.nb_constrs == 6 @test all(form2.col_major_coef_matrix .== coef_matrix[:, [2, 4, 5]]) @test all(form2.rhs .== [6.5, -6.5, -9, 2, 1, -7.5]) @test all(form2.sense .== sense) @test all(form2.lbs .== [0, 0, -1]) # Vars 2, 4 & 5 @test all(form2.ubs .== [1, 1, 1]) # Vars 2, 4, & 5 @test all(form2.partial_solution .== [2, 1, 0]) end register!(unit_tests, "presolve_helper", test_presolve_builder3; x = true) # Test bound tightening. function test_presolve_builder4() coef_matrix = sparse([ 0 0 -1 1 0 1 2.5 # <= 4 0 0 1 -1 0 -1 -2.5 # >= -4 1 0 0 0 1 0 0 # == 1 0 1 2 -4 0 0 0 # >= 2 0 1 2 -4 0 0 0 # <= 1 1 -2 3 5.5 0 1 1 # == 6 ]) rhs = [4, -4, 1, 2, 1, 6] sense = [Less, Greater, Equal, Greater, Less, Equal] lbs = [1, 0, 2, 1, -1, -Inf, 0] ubs = [10, Inf, 3, 2, 1, 0, 1] partial_sol = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1.0, 1.0) rows_to_deactivate = Int[] tightened_bounds = Dict{Int,Tuple{Float64, Bool, Float64, Bool}}( 1 => (1, false, 2, true), 2 => (0, true, 1, true), 3 => (-1, false, 3, false), 6 => (0.5, true, 0.5, true) # the flag forces the update! ) form2, _, _= Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1.0, 1.0 ) @test form2.nb_vars == 6 @test form2.nb_constrs == 6 @test all(form2.col_major_coef_matrix .== coef_matrix[:, [1, 2, 3, 4, 5, 7]]) @test all(form2.rhs .== [4.5, -4.5, 0.0, 2.0, 1.0, -7.0]) @test all(form2.sense .== sense) @test all(form2.lbs .== [0, 0, 0, 0, -1, 0]) @test all(form2.ubs .== [1, 1, 1, 1, 1, 1]) @test all(form2.partial_solution .== [1, 0, 2, 1, 0, 0]) end register!(unit_tests, "presolve_helper", test_presolve_builder4; x = true) function test_presolve_builder5() # 2x1 + 3x2 - 2x3 >= 2 # 3x1 - 4x2 + x3 >= 5 coef_matrix = sparse([ 2 3 -2 3 -4 1 ]) rhs = [2, 5] sense = [Greater, Greater] lbs = [0, 0, -3] ubs = [Inf, Inf, 3] partial_sol = [1, 1, 0] form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) rows_to_deactivate = Int[] tightened_bounds = Dict{Int,Tuple{Float64, Bool, Float64, Bool}}( 1 => (1, true, Inf, false), 2 => (1, true, Inf, false), 3 => (1, true, 2, true) ) form2, _, _= Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1.0, 1.0 ) @test form2.nb_vars == 3 @test form2.nb_constrs == 2 @test all(form2.col_major_coef_matrix .== coef_matrix) @test all(form2.rhs .== ([2, 5] - [2 + 3 - 2, 3 - 4 + 1])) @test all(form2.sense .== sense) @test all(form2.lbs .== [0, 0, 0]) @test all(form2.ubs .== [Inf, Inf, 1]) @test all(form2.partial_solution .== [2, 2, 1]) end register!(unit_tests, "presolve_helper", test_presolve_builder5; x = true) function row_activity() coef_matrix = sparse([ 0 0 -1 1 0 1 2.5 # <= 4 0 0 1 -1 0 -1 -2.5 # >= -4 1 0 0 0 1 0 0 # == 1 0 1 2 -4 0 0 0 # >= 2 0 1 2 -4 0 0 0 # <= 1 1 -2 3 5.5 0 1 1 # == 6 ]) rhs = [4, -4, 1, 2, 1, 6] sense = [Less, Greater, Equal, Greater, Less, Equal] lbs = [1, 0, 2, 1, -1, -Inf, 0] ubs = [10, Inf, 3, 2, 1, 0, 1] partial_sol = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) @test Coluna.Algorithm.row_min_activity(form, 1) == 0 + 0 - 1 * ubs[3] + 1 * lbs[4] + 0 + 1 * lbs[6] + 2.5 * lbs[7] @test Coluna.Algorithm.row_max_activity(form, 1) == 0 + 0 - 1 * lbs[3] + 1 * ubs[4] + 0 + 1 * ubs[6] + 2.5 * ubs[7] @test Coluna.Algorithm.row_min_activity(form, 2) == 0 + 0 + 1 * lbs[3] - 1 * ubs[4] + 0 - 1 * ubs[6] - 2.5 * ubs[7] @test Coluna.Algorithm.row_max_activity(form, 2) == 0 + 0 + 1 * ubs[3] - 1 * lbs[4] + 0 - 1 * lbs[6] - 2.5 * lbs[7] @test Coluna.Algorithm.row_min_activity(form, 3) == transpose(coef_matrix[3,:]) * [lbs[1], 0, 0, 0, lbs[5], 0, 0] # ok @test Coluna.Algorithm.row_max_activity(form, 3) == transpose(coef_matrix[3,:]) * [ubs[1], 0, 0, 0, ubs[5], 0, 0] # ok @test Coluna.Algorithm.row_min_activity(form, 4) == transpose(coef_matrix[4,:]) * [0, lbs[2], lbs[3], ubs[4], 0, 0, 0] # ok @test Coluna.Algorithm.row_max_activity(form, 4) == transpose(coef_matrix[4,:]) * [0, ubs[2], ubs[3], lbs[4], 0, 0, 0] # ok @test Coluna.Algorithm.row_min_activity(form, 5) == transpose(coef_matrix[5,:]) * [0, lbs[2], lbs[3], ubs[4], 0, 0, 0] # ok @test Coluna.Algorithm.row_max_activity(form, 5) == transpose(coef_matrix[5,:]) * [0, ubs[2], ubs[3], lbs[4], 0, 0, 0] # ok @test Coluna.Algorithm.row_min_activity(form, 6) == transpose(coef_matrix[6,:]) * [lbs[1], ubs[2], lbs[3], lbs[4], 0, lbs[6], lbs[7]] # ok @test Coluna.Algorithm.row_max_activity(form, 6) == transpose(coef_matrix[6,:]) * [ubs[1], lbs[2], ubs[3], ubs[4], 0, ubs[6], ubs[7]] # ok end register!(unit_tests, "presolve_helper", row_activity) function row_slack() coef_matrix = sparse([ 0 0 -1 1 0 1 2.5 # <= 4 0 0 1 -1 0 -1 -2.5 # >= -4 1 0 0 0 1 0 0 # == 1 0 1 2 -4 0 0 0 # >= 2 0 1 2 -4 0 0 0 # <= 1 1 -2 3 5.5 0 1 1 # == 6 ]) rhs = [4, -4, 1, 2, 1, 6] sense = [Less, Greater, Equal, Greater, Less, Equal] lbs = [1, 0, 2, 1, -1, -Inf, 0] ubs = [10, Inf, 3, 2, 1, 0, 1] partial_sol = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) @test Coluna.Algorithm.row_min_slack(form, 1) == rhs[1] - Coluna.Algorithm.row_max_activity(form, 1) # ok @test Coluna.Algorithm.row_max_slack(form, 1) == rhs[1] - Coluna.Algorithm.row_min_activity(form, 1) # ok @test Coluna.Algorithm.row_min_slack(form, 2) == rhs[2] - Coluna.Algorithm.row_max_activity(form, 2) # ok @test Coluna.Algorithm.row_max_slack(form, 2) == rhs[2] - Coluna.Algorithm.row_min_activity(form, 2) # ok @test Coluna.Algorithm.row_min_slack(form, 3) == rhs[3] - Coluna.Algorithm.row_max_activity(form, 3) # ok @test Coluna.Algorithm.row_max_slack(form, 3) == rhs[3] - Coluna.Algorithm.row_min_activity(form, 3) # ok @test Coluna.Algorithm.row_min_slack(form, 4) == rhs[4] - Coluna.Algorithm.row_max_activity(form, 4) # ok @test Coluna.Algorithm.row_max_slack(form, 4) == rhs[4] - Coluna.Algorithm.row_min_activity(form, 4) # ok @test Coluna.Algorithm.row_min_slack(form, 5) == rhs[5] - Coluna.Algorithm.row_max_activity(form, 5) # ok @test Coluna.Algorithm.row_max_slack(form, 5) == rhs[5] - Coluna.Algorithm.row_min_activity(form, 5) # ok @test Coluna.Algorithm.row_min_slack(form, 6) == rhs[6] - Coluna.Algorithm.row_max_activity(form, 6) # ok @test Coluna.Algorithm.row_max_slack(form, 6) == rhs[6] - Coluna.Algorithm.row_min_activity(form, 6) # ok end register!(unit_tests, "presolve_helper", row_slack) function test_inner_unbounded_row() @test Coluna.Algorithm._unbounded_row(Less, Inf) @test Coluna.Algorithm._unbounded_row(Greater, -Inf) @test !Coluna.Algorithm._unbounded_row(Less, -Inf) @test !Coluna.Algorithm._unbounded_row(Greater, Inf) @test !Coluna.Algorithm._unbounded_row(Equal, Inf) @test !Coluna.Algorithm._unbounded_row(Equal, -Inf) @test !Coluna.Algorithm._unbounded_row(Less, 15) @test !Coluna.Algorithm._unbounded_row(Greater, 15) end register!(unit_tests, "presolve_helper", test_inner_unbounded_row) function test_inner_row_bounded_by_var_bounds_1() # x + y + z >= 1 coeffs = [1, 1, 1] lbs = [1, 1, 1] ubs = [10, 10, 10] rhs = 1 sense = Greater min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test !Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) # x + y + z >= 4 rhs = 4 min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test !Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test !Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) # x + y + z >= 31 rhs = 31 min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test !Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) end register!(unit_tests, "presolve_helper", test_inner_row_bounded_by_var_bounds_1) function test_inner_row_bounded_by_var_bounds_2() # x + y + z <= 9 coeffs = [1, 1, 1] lbs = [1, 1, 1] ubs = [3, 3, 3] rhs = 9 sense = Less min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test !Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) # x + y + z <= 4 rhs = 4 min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test !Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test !Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) # x + y + z <= -1 rhs = -1 min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test !Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) end register!(unit_tests, "presolve_helper", test_inner_row_bounded_by_var_bounds_2) function test_inner_row_bounded_by_var_bounds_3() # x + y + z == 3 coeffs = [1, 1, 1] lbs = [1, 1, 1] ubs = [1, 1, 1] rhs = 3 sense = Equal min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test !Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) # x + y + z == 4 rhs = 4 min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test !Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) # x + y + z == 2 lbs = [0, 0, 0] ubs = [1, 1, 1] rhs = 2 min_slack = rhs - transpose(coeffs) * ubs max_slack = rhs - transpose(coeffs) * lbs @test !Coluna.Algorithm._row_bounded_by_var_bounds(sense, min_slack, max_slack, 1e-6) @test !Coluna.Algorithm._infeasible_row(sense, min_slack, max_slack, 1e-6) end register!(unit_tests, "presolve_helper", test_inner_row_bounded_by_var_bounds_3) function test_var_bounds_from_row1() # x + 2y + 3z >= 10 # -x - 2y - 3z <= -10 # 0 <= x <= 10 # 0 <= y <= 2 # 0 <= z <= 1 # therefore x >= 10 - 22 - 31 >= 3 # x >= rhs - max_act(y,z) coef_matrix = sparse([1 2 3; -1 -2 -3;]) rhs = [10, -10] sense = [Greater; Less] lbs = [0, 0, 0] ubs = [10, 2, 1] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, 1.0) @test lb == 3 ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, 1.0) @test isinf(ub) && ub > 0 min_slack = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[2], min_slack, max_slack, -1.0) @test lb == 3 ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack, max_slack, -1.0) @test isinf(ub) && ub > 0 end register!(unit_tests, "presolve_helper", test_var_bounds_from_row1) function test_var_bounds_from_row2() # -3x + y + 2z <= 2 # 3x - y - 2z >= -2 # 0 <= x <= 10 # 0 <= y <= 1 # 0 <= z <= 1 # therefore -3x <= 2 - y - 2z # 3x >= -2 + y + 2z # 3x >= -2 + 0 + 20 # x >= -2/3 coef_matrix = sparse([-3 1 2; 3 -1 -2]) rhs = [2, -2] sense = [Less, Greater] lbs = [0, 0, 0] ubs = [10, 1, 1] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, -3) @test lb == -2/3 ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, -3) @test isinf(ub) && ub > 0 min_slack = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[2], min_slack, max_slack, 3) @test lb == -2/3 ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack, max_slack, 3) @test isinf(ub) && ub > 0 end register!(unit_tests, "presolve_helper", test_var_bounds_from_row2) function test_var_bounds_from_row3() # 2x + 3y - 4z <= 9 # -2x - 3y + 4z >= -9 # 0 <= x <= 10 # 4 <= y <= 8 # 0 <= z <= 1 # therefore 2x <= 9 - 3y + 4z # 2x <= 9 - 12 + 4 # 2x <= 1 coef_matrix = sparse([2 3 -4; -2 -3 4;]) lbs = [0, 4, 0] ubs = [10, 8, 1] rhs = [9, -9] sense = [Less, Greater] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, 2) @test isinf(lb) && lb < 0 ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, 2) @test ub == 1/2 min_slack = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[2], min_slack, max_slack, -2) @test isinf(lb) && lb < 0 ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack, max_slack, -2) @test ub == 1/2 end register!(unit_tests, "presolve_helper", test_var_bounds_from_row3) function test_var_bounds_from_row4() # -2x + 2y + 3z >= 10 # 2x - 2y - 3z <= 10 # -10 <= x <= 0 # 1 <= y <= 2 # 1 <= z <= 2 # therefore -2x >= 10 - 2y - 3z # 2x <= -10 + 2y + 3z # 2x <= -10 + 22 + 32 # x <= 0 coef_matrix = sparse([-2 2 3; 2 -2 -3]) lbs = [-10, 1, 1] ubs = [0, 2, 2] rhs = [10, -10] sense = [Greater, Less] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, -2) @test lb == -Inf ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, -2) @test ub == 0 min_slack = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[2], min_slack, max_slack, 2) @test lb == -Inf ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack, max_slack, 2) @test ub == 0 end register!(unit_tests, "presolve_helper", test_var_bounds_from_row4) function test_var_bounds_from_row5() # 2x + 3y + 4z = 5 # -5 <= x <= 3 # 0 <= y <= 1 # 0 <= z <= 1 # Sense1: # 2x + 3y + 4z >= 5 # 2x >= 5 - 3y - 4z # 2x >= -2 # Sense 2: # 2x + 3y + 4z <= 5 # 2x <= 5 - 3y - 4z # 2x <= 5 coef_matrix = sparse([2 3 4;]) lbs = [-5, 0, 0] ubs = [3, 1, 1] rhs = [5] sense = [Equal] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, 2) @test lb == -1 ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, 2) @test ub == 5/2 end register!(unit_tests, "presolve_helper", test_var_bounds_from_row5) function test_var_bounds_from_row6() # x1 + x2 >= 1 (row 1) # y1 + y2 >= 1 (row 2) # 0 <= x1 <= 0.5 # x2 >= 0 # 0 <= y1 <= 0.3 # y2 >= 0 coef_matrix = sparse([ 1 1 0 0; 0 0 1 1 ]) lbs = [0, 0, 0, 0] ubs = [0.5, Inf, 0.3, Inf] sense = [Greater, Greater] rhs = [1, 1] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack1 = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 2) max_slack1 = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 2) lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack1, max_slack1, 1) @test lb == 0.5 ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack1, max_slack1, 1) @test ub == Inf min_slack2 = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 4) max_slack2 = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 4) lb = Coluna.Algorithm._var_lb_from_row(sense[2], min_slack2, max_slack2, 1) @test lb == 0.7 ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack2, max_slack2, 1) @test ub == Inf end register!(unit_tests, "presolve_helper", test_var_bounds_from_row6) function test_var_bounds_from_row7() # -2x + y + z >= 150 # -x + y + z <= 600 # x == 10 # y >= 0 # z >= 0 coef_matrix = sparse([-2 1 1; -1 1 1]) lbs = [10, 0, 0] ubs = [10, Inf, Inf] rhs = [150, 600] sense = [Greater, Less] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) # -2x + y + z >= 150 # -2x >= 150 - y - z # 2x <= -150 + y + z # x <= Inf lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, -2) @test isinf(lb) && lb < 0 ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, -2) @test isinf(ub) && ub > 0 min_slack = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 1) # -x + y + z <= 600 # -x <= 600 - y - z # x >= -600 + y + z # x >= -600 lb = Coluna.Algorithm._var_lb_from_row(sense[2], min_slack, max_slack, -1) @test lb == -600 ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack, max_slack, -1) @test isinf(ub) && ub > 0 end register!(unit_tests, "presolve_helper", test_var_bounds_from_row7) function test_var_bounds_from_row8() # this was producing a bug # 2x + y + z <= 1 # x == 2 # y >= 0 # z >= 0 coef_matrix = sparse([2 1 1;]) lbs = [2, 0, 0] ubs = [2, Inf, Inf] rhs = [1] sense = [Less] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) # 2x <= 1 - y - z # x <= 1/2 lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, 2) @test lb == -Inf ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, 2) @test ub == 1/2 end register!(unit_tests, "presolve_helper", test_var_bounds_from_row8) function test_var_bounds_from_row9() # x + y + a >= 1 # x + y <= 1 # x + y >= 0 # x >= 0 # y >= 1 # a >= 0 coef_matrix = sparse([1 1 1; 1 1 0; 1 1 0]) lbs = [0, 1, 0] ubs = [Inf, Inf, Inf] rhs = [1, 1, 0] sense = [Greater, Less, Greater] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 1) # x <= 1 - y # x <= 0 ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack, max_slack, 1) @test ub == 0 min_slack = Coluna.Algorithm.row_min_slack(form, 2, col -> col == 2) max_slack = Coluna.Algorithm.row_max_slack(form, 2, col -> col == 2) # y <= 1 - x # y <= 0 ub = Coluna.Algorithm._var_ub_from_row(sense[2], min_slack, max_slack, 1) @test ub == 1 result = Coluna.Algorithm.bounds_tightening(form) @test result[1] === (0.0, false, 0.0, true) @test result[2] === (1.0, false, 1.0, true) end register!(unit_tests, "presolve_helper", test_var_bounds_from_row9) function test_var_bounds_from_row10() # y2 # + 1.0 x + 1.0 y1 - 1.0 y2 + 1.0 z1 - 1.0 z2 == 0.0 # 0.0 <= x <= Inf (Integ \| MasterRepPricingVar \| false) # 0.0 <= y1 <= Inf (Continuous \| MasterArtVar \| true) # 0.0 <= y2 <= Inf (Continuous \| MasterArtVar \| true) # 0.0 <= z1 <= Inf (Continuous \| MasterArtVar \| true) # 0.0 <= z2 <= Inf (Continuous \| MasterArtVar \| true) coef_matrix = sparse([1 1 -1 1 -1]) lbs = [0, 0, 0, 0, 0] ubs = [Inf, Inf, Inf, Inf, Inf] rhs = [0] sense = [Equal] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 3) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 3) ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, 1) @test ub == Inf lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, 1) @test lb == -Inf end register!(unit_tests, "presolve_helper", test_var_bounds_from_row10) function test_var_bounds_from_row11() # - w - x + y + z = 0 # # w = y + z -x # lb: -x -> # donc coef_matrix = sparse([-1 -1 1 1]) lbs = [0, 0, 0, 0] ubs = [Inf, Inf, Inf, Inf] rhs = [0] sense = [Equal] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 0, 0) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, -1) @test ub == Inf lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, -1) @test lb == -Inf end register!(unit_tests, "presolve_helper", test_var_bounds_from_row11) function test_var_bounds_from_row12() # 0x + y + z <= 5 # 0 <= x <= 2 # 1 <= y <= 3 # 0 <= z <= 6 coef_matrix = sparse([0 1 1;]) lbs = [0, 1, 0] ubs = [2, 3, 6] rhs = [5] sense = [Less] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_solution, 1, 1) min_slack = Coluna.Algorithm.row_min_slack(form, 1, col -> col == 1) max_slack = Coluna.Algorithm.row_max_slack(form, 1, col -> col == 1) ub = Coluna.Algorithm._var_ub_from_row(sense[1], min_slack, max_slack, 0) @test ub == Inf lb = Coluna.Algorithm._var_lb_from_row(sense[1], min_slack, max_slack, 0) @test lb == -Inf end register!(unit_tests, "presolve_helper", test_var_bounds_from_row12) function test_uninvolved_vars1() # 0x + y + z <= 5 # 0 <= x <= 2 # 1 <= y <= 3 # 0 <= z <= 6 coef_matrix = sparse([0 1 1;]) lbs = [0, 1, 0] ubs = [2, 3, 6] rhs = [5] sense = [Less] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, ubs, lbs, partial_solution, 1, 1) cols = Coluna.Algorithm.find_uninvolved_vars(form.col_major_coef_matrix) @test cols == [1] end register!(unit_tests, "presolve_helper", test_uninvolved_vars1) function test_uninvolved_vars2() # x + y + z <= 5 # 0 <= x <= 2 # 1 <= y <= 3 # 0 <= z <= 6 coef_matrix = sparse([1 1 1;]) lbs = [0, 1, 0] ubs = [2, 3, 6] rhs = [5] sense = [Less] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, ubs, lbs, partial_solution, 1, 1) cols = Coluna.Algorithm.find_uninvolved_vars(form.col_major_coef_matrix) @test cols == [] end register!(unit_tests, "presolve_helper", test_uninvolved_vars2) function test_uninvolved_vars3() # w, x, y, z # x >= 2 # x + y >= 5 coef_matrix = sparse([0 1 0 0; 0 1 1 0]) lbs = [0, 0, 0, 0] ubs = [Inf, Inf, Inf, Inf] rhs = [2, 5] sense = [Greater, Greater] partial_solution = zeros(Float64, length(lbs)) form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, ubs, lbs, partial_solution, 1, 1) cols = Coluna.Algorithm.find_uninvolved_vars(form.col_major_coef_matrix) @test cols == [1, 4] end register!(unit_tests, "presolve_helper", test_uninvolved_vars3)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	5604	function test_partial_solution1() # 2x + 3y <= 4 # 0 <= x <= 5 # 0 <= y <= 6 # with partial sol: x1 = 1, x2 = 2 coef_matrix = sparse([2 3;]) rhs = [4.0] sense = [Less] lbs = [0.0, 0.0] ubs = [5.0, 6.0] partial_sol = [1.0, 2.0] form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) @test form.nb_vars == 2 @test form.nb_constrs == 1 @test all(form.col_major_coef_matrix .== coef_matrix) @test all(form.rhs .== rhs) @test all(form.sense .== sense) @test all(form.lbs .== lbs) @test all(form.ubs .== ubs) @test all(form.partial_solution .== partial_sol) rows_to_deactivate = Int[] tightened_bounds = Dict{Int, Tuple{Float64, Bool, Float64, Bool}}( 1 => (1, true, Inf, false), 2 => (2, true, Inf, false) ) form2, _, _, _ = Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1, 1; store_unpropagated_partial_sol = false ) @test form2.nb_vars == 2 @test form2.nb_constrs == 1 @test all(form2.col_major_coef_matrix .== coef_matrix) @test form2.rhs == [-4] @test form2.sense == [Less] @test form2.lbs == [0.0, 0.0] @test form2.ubs == [4.0, 4.0] @test form2.partial_solution == [2.0, 4.0] end register!(unit_tests, "presolve_partial_sol", test_partial_solution1; x = true) function test_partial_solution2() # 2x + 3y <= 5 # x >= 0 # y >= 0 # with partial sol: x1 = 2, x2 = 1 coef_matrix = sparse([2 3;]) rhs = [5.0] sense = [Less] lbs = [0.0, 0.0] ubs = [Inf, Inf] partial_sol = [2.0, 1.0] form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) @test form.nb_vars == 2 @test form.nb_constrs == 1 @test all(form.col_major_coef_matrix .== coef_matrix) @test all(form.rhs .== rhs) @test all(form.sense .== sense) @test all(form.lbs .== lbs) @test all(form.ubs .== ubs) @test all(form.partial_solution .== partial_sol) rows_to_deactivate = Int[] tightened_bounds = Dict{Int, Tuple{Float64, Bool, Float64, Bool}}( 1 => (1, true, Inf, false), 2 => (2, true, Inf, false) ) form2, _, _, _ = Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1, 1; store_unpropagated_partial_sol = false ) @test form2.nb_vars == 2 @test form2.nb_constrs == 1 @test all(form2.col_major_coef_matrix .== coef_matrix) @test form2.rhs == [-3] @test form2.sense == [Less] @test all(form2.lbs .== [0.0, 0.0]) @test all(form2.ubs .== [Inf, Inf]) @test all(form2.partial_solution .== [3.0, 3.0]) end register!(unit_tests, "presolve_partial_sol", test_partial_solution2; x = true) function test_partial_solution3() # 2x + 3y <= 5 # x + y >= 2 # -5 <= x <= 5 # -10 <= y <= 10 coef_matrix = sparse([2 3; 1 1;]) rhs = [5.0, 2.0] sense = [Less, Greater] lbs = [-5.0, -10.0] ubs = [5.0, 10.0] partial_sol = [0.0, 0.0] form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) @test form.nb_vars == 2 @test form.nb_constrs == 2 @test all(form.col_major_coef_matrix .== coef_matrix) @test all(form.rhs .== rhs) @test all(form.sense .== sense) @test all(form.lbs .== lbs) @test all(form.ubs .== ubs) @test all(form.partial_solution .== partial_sol) rows_to_deactivate = Int[] tightened_bounds = Dict{Int, Tuple{Float64, Bool, Float64, Bool}}( 1 => (-5, false, -3, true), 2 => (-10, false, 8, true) ) form2, _, _, _ = Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1, 1; store_unpropagated_partial_sol = false ) @test form2.nb_vars == 2 @test form2.nb_constrs == 2 @test all(form2.col_major_coef_matrix .== coef_matrix) @test all(form2.rhs .== [11, 5]) @test all(form2.lbs .== [-2.0, -10.0]) @test all(form2.ubs .== [-0.0, 8.0]) @test all(form2.partial_solution .== [-3.0, 0.0]) end register!(unit_tests, "presolve_partial_sol", test_partial_solution3; x = true) function test_partial_solution4() # 2x + 3y <= 5 # x + y >= 2 # x <= 0 # y <= 0 coef_matrix = sparse([2 3; 1 1;]) rhs = [5.0, 2.0] sense = [Less, Greater] lbs = [-Inf, -Inf] ubs = [0.0, 0.0] partial_sol = [0.0, 0.0] form = Coluna.Algorithm.PresolveFormRepr(coef_matrix, rhs, sense, lbs, ubs, partial_sol, 1, 1) @test form.nb_vars == 2 @test form.nb_constrs == 2 @test all(form.col_major_coef_matrix .== coef_matrix) @test all(form.rhs .== rhs) @test all(form.sense .== sense) @test all(form.lbs .== lbs) @test all(form.ubs .== ubs) @test all(form.partial_solution .== partial_sol) rows_to_deactivate = Int[] tightened_bounds = Dict{Int, Tuple{Float64, Bool, Float64, Bool}}( 1 => (-Inf, false, -1.0, true), 2 => (-Inf, false, -1.0, true) ) form2, _, _, _ = Coluna.Algorithm.PresolveFormRepr( form, rows_to_deactivate, tightened_bounds, 1, 1; store_unpropagated_partial_sol = false ) @test form2.nb_vars == 2 @test form2.nb_constrs == 2 @test all(form2.col_major_coef_matrix .== coef_matrix) @test all(form2.rhs .== [10, 4]) @test all(form2.lbs .== [-Inf, -Inf]) @test all(form2.ubs .== [0.0, 0.0]) @test all(form2.partial_solution .== [-1.0, -1.0]) end register!(unit_tests, "presolve_partial_sol", test_partial_solution4; x = true)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	22529	function test_compute_rhs1() env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) restricted_master, name_to_vars, name_to_constrs = _mathprog_formulation!( env, Coluna.MathProg.DwMaster(), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 1.0, nothing, nothing), ("x2", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 2.0, nothing, nothing), ("MC1", Coluna.MathProg.MasterCol, 1.0, 0.0, 1.0, nothing, 2), ("MC2", Coluna.MathProg.MasterCol, 2.0, 1.0, 1.0, nothing, 2), ("MC3", Coluna.MathProg.MasterCol, 4.0, 2.0, 4.0, nothing, 2), ("y1", Coluna.MathProg.MasterPureVar, 5.0, 1.5, Inf, nothing, nothing), ("y2", Coluna.MathProg.MasterPureVar, 0.0, 2.5, Inf, nothing, nothing), ], [ # name, duty, rhs, sense , id ("c1", Coluna.MathProg.MasterMixedConstr, 2.0, ClMP.Greater, nothing), ("c2", Coluna.MathProg.MasterConvexityConstr, 0.0, ClMP.Greater, nothing), ("c3", Coluna.MathProg.MasterConvexityConstr, 2.0, ClMP.Less, nothing) ] ) master_repr_presolve_form = _presolve_formulation( ["MC1", "MC2", "MC3", "y1", "y2"], ["c1", "c2", "c3"], [1 1 3 2 2; 1 1 1 0 0; 1 1 1 0 0], restricted_master, name_to_vars, name_to_constrs, ) partial_sol = [0.0, 0.0, 1.0, 1.0, 0.0] rhs_result = Coluna.Algorithm.compute_rhs( master_repr_presolve_form, partial_sol ) @test rhs_result == [ 2.0 - 3 * 1.0 - 2 * 1.0, # MC3 & y1 0.0 - 1.0, # MC3 2.0 - 1.0, # MC3 ] end register!(unit_tests, "presolve_algorithm", test_compute_rhs1) function test_partial_sol_on_repr() env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) master, master_name_to_var, master_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwMaster(), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 1.0, nothing, nothing), ("x2", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 2.0, nothing, nothing), ("MC1", Coluna.MathProg.MasterCol, 1.0, 0.0, 1.0, nothing, 2), ("MC2", Coluna.MathProg.MasterCol, 2.0, 1.0, 1.0, nothing, 2), ("MC3", Coluna.MathProg.MasterCol, 4.0, 2.0, 4.0, nothing, 2), ("y1", Coluna.MathProg.MasterPureVar, 5.0, 1.5, Inf, nothing, nothing), ("y2", Coluna.MathProg.MasterPureVar, 0.0, 2.5, Inf, nothing, nothing), ("pricing_setup", Coluna.MathProg.MasterRepPricingSetupVar, 0.0, 1.0, 1.0, nothing, nothing) ], [ # name, duty, rhs, sense , id ("c1", Coluna.MathProg.MasterMixedConstr, 4.0, ClMP.Greater, nothing), ("c2", Coluna.MathProg.MasterConvexityConstr, 0.0, ClMP.Greater, nothing), ("c3", Coluna.MathProg.MasterConvexityConstr, 4.0, ClMP.Less, nothing) ] ) spform, sp_name_to_var, sp_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwSp( Coluna.MathProg.getid(master_name_to_var["pricing_setup"]), Coluna.MathProg.getid(master_name_to_constr["c2"]), Coluna.MathProg.getid(master_name_to_constr["c3"]), Coluna.MathProg.Integ, ), [ ("x1", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 1.0, Coluna.Algorithm.getid(master_name_to_var["x1"]), Coluna.MathProg.getuid(master)), ("x2", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 1.0, Coluna.Algorithm.getid(master_name_to_var["x2"]), Coluna.MathProg.getuid(master)) ], [] ) var_ids = [Coluna.MathProg.getid(sp_name_to_var["x1"]), Coluna.MathProg.getid(sp_name_to_var["x2"])] pool = Coluna.MathProg.get_primal_sol_pool(spform) for (name, vals) in Iterators.zip( ["MC1", "MC2", "MC3"], [ # x1, x2 Float64[1.0, 2.0], Float64[1.0, 1.0], Float64[0.0, 1.0] ] ) col_id = Coluna.MathProg.VarId( Coluna.MathProg.getid(master_name_to_var[name]), origin_form_uid = Coluna.MathProg.getuid(spform), duty = Coluna.MathProg.DwSpPrimalSol ) Coluna.MathProg.push_in_pool!( pool, Coluna.MathProg.PrimalSolution(spform, var_ids, vals, 1.0, Coluna.MathProg.FEASIBLE_SOL), col_id, 1.0 ) end dw_pricing_sps = Dict( Coluna.MathProg.getuid(spform) => spform ) presolve_master_repr = _presolve_formulation( ["x1", "x2", "y1", "y2"], ["c1"], [1 1 2 1], master, master_name_to_var, master_name_to_constr, ) presolve_master_restr = _presolve_formulation( ["MC1", "MC2", "MC3", "y1", "y2"], ["c1", "c2", "c3"], [1 1 3 2 1; 1 1 1 0 0; 1 1 1 0 0], master, master_name_to_var, master_name_to_constr, ) local_restr_partial_sol = [0.0, 1.0, 2.0, 1.0, 0.0] # MC2 = 1, MC3 = 2, y1 = 1. partial_sol_on_repr, _ = Coluna.Algorithm.partial_sol_on_repr( dw_pricing_sps, presolve_master_repr, presolve_master_restr, local_restr_partial_sol ) @test partial_sol_on_repr == [ 1.0, # 1.0 MC2 with x1 = 1.0 & 2.0 MC3 with x1 = 0.0 3.0, # 1.0 MC2 with x2 = 1.0 & 2.0 MC3 with x2 = 1.0 1.0, # y1 0.0 # y2 ] end register!(unit_tests, "presolve_algorithm", test_partial_sol_on_repr) function test_update_subproblem_multiplicities() env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) # original master: # min x1 + x2 + 5y1 + 0y2 + MC1 + 2MC2 + 4MC3 # c1: x1 + x2 + 3MC1 + 2MC2 + 1MC3 + 2y1 + 2y2 >= 4 # c2: MC1 + MC2 + MC3 >= 0 # c3: MC1 + MC2 + MC3 <= 4 # 0 <= x1 <= 1 # 0 <= x2 <= 2 # 0 <= MC1 <= 1 # 1 <= MC2 <= 1 # 2 <= MC3 <= 4 # y1 >= 1.5 # y2 >= 2.5 master, master_name_to_var, master_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwMaster(), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 1.0, nothing, nothing), ("x2", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 2.0, nothing, nothing), ("MC1", Coluna.MathProg.MasterCol, 1.0, 0.0, 1.0, nothing, 2), ("MC2", Coluna.MathProg.MasterCol, 2.0, 1.0, 1.0, nothing, 2), ("MC3", Coluna.MathProg.MasterCol, 4.0, 2.0, 4.0, nothing, 2), ("y1", Coluna.MathProg.MasterPureVar, 5.0, 1.5, Inf, nothing, nothing), ("y2", Coluna.MathProg.MasterPureVar, 0.0, 2.5, Inf, nothing, nothing), ("pricing_setup", Coluna.MathProg.MasterRepPricingSetupVar, 0.0, 1.0, 1.0, nothing, nothing) ], [ # name, duty, rhs, sense , id ("c1", Coluna.MathProg.MasterMixedConstr, 4.0, ClMP.Greater, nothing), ("c2", Coluna.MathProg.MasterConvexityConstr, 0.0, ClMP.Greater, nothing), ("c3", Coluna.MathProg.MasterConvexityConstr, 4.0, ClMP.Less, nothing), ] ) spform, sp_name_to_var, sp_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwSp( Coluna.MathProg.getid(master_name_to_var["pricing_setup"]), Coluna.MathProg.getid(master_name_to_constr["c2"]), Coluna.MathProg.getid(master_name_to_constr["c3"]), Coluna.MathProg.Integ, ), [ ("x1", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 1.0, Coluna.Algorithm.getid(master_name_to_var["x1"]), Coluna.MathProg.getuid(master)), ("x2", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 1.0, Coluna.Algorithm.getid(master_name_to_var["x2"]), Coluna.MathProg.getuid(master)) ], [ # name, duty, rhs, sense, id ("c4", Coluna.MathProg.DwSpPureConstr, 2.0, ClMP.Greater, nothing) ] ) var_ids = [Coluna.MathProg.getid(sp_name_to_var["x1"]), Coluna.MathProg.getid(sp_name_to_var["x2"])] pool = Coluna.MathProg.get_primal_sol_pool(spform) for (name, vals) in Iterators.zip( ["MC1", "MC2", "MC3"], [ # x1, x2 Float64[1.0, 2.0], Float64[1.0, 1.0], Float64[0.0, 1.0] ] ) col_id = Coluna.MathProg.VarId( Coluna.MathProg.getid(master_name_to_var[name]), origin_form_uid = Coluna.MathProg.getuid(spform), duty = Coluna.MathProg.DwSpPrimalSol ) Coluna.MathProg.push_in_pool!( pool, Coluna.MathProg.PrimalSolution(spform, var_ids, vals, 1.0, Coluna.MathProg.FEASIBLE_SOL), col_id, 1.0 ) end dw_pricing_sps = Dict( Coluna.MathProg.getuid(spform) => spform ) presolve_master_repr = _presolve_formulation( ["x1", "x2"], ["c1"], [1 1], master, master_name_to_var, master_name_to_constr, ) presolve_master_restr = _presolve_formulation( ["MC1", "MC2", "MC3", "y1", "y2"], ["c1", "c2", "c3"], [3 2 1 2 2; 1 1 1 0 0; 1 1 1 0 0], master, master_name_to_var, master_name_to_constr, ) presolve_sp = _presolve_formulation( ["x1", "x2"], ["c4"], [1 1], spform, sp_name_to_var, sp_name_to_constr; lm = 0, um = 4 ) local_restr_partial_sol = [0.0, 1.0, 2.0, 1.0, 0.0] # MC2 = 1, MC3 = 2, y1 = 1. _, nb_fixed_columns_per_sp = Coluna.Algorithm.partial_sol_on_repr( dw_pricing_sps, presolve_master_repr, presolve_master_restr, local_restr_partial_sol ) presolve_pricing_sps = Dict( Coluna.MathProg.getuid(spform) => presolve_sp ) sp_form_uid = Coluna.MathProg.getuid(spform) @test nb_fixed_columns_per_sp[sp_form_uid] == 3 # 3 columns fixed. @test presolve_pricing_sps[sp_form_uid].form.lower_multiplicity == 0 @test presolve_pricing_sps[sp_form_uid].form.upper_multiplicity == 4 Coluna.Algorithm.update_subproblem_multiplicities!(presolve_pricing_sps, nb_fixed_columns_per_sp) @test presolve_pricing_sps[sp_form_uid].form.lower_multiplicity == 0 @test presolve_pricing_sps[sp_form_uid].form.upper_multiplicity == 4 - 3 end register!(unit_tests, "presolve_algorithm", test_update_subproblem_multiplicities) function test_compute_repr_master_var_domains_and_propagate_partial_sol_into_master() env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) # original master: # min x1 + x2 + 5y1 + 0y2 + MC1 + 2MC2 + 4MC3 # c1: x1 + x2 + 3MC1 + 2MC2 + 1MC3 + 2y1 + 2y2 >= 4 # c2: MC1 + MC2 + MC3 >= 0 # c3: MC1 + MC2 + MC3 <= 4 # 0 <= x1 <= 1 # 0 <= x2 <= 2 # 0 <= MC1 <= 1 # 1 <= MC2 <= 1 # 2 <= MC3 <= 4 # y1 >= 1.5 # y2 >= 2.5 master, master_name_to_var, master_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwMaster(), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 4.0, nothing, nothing), ("x2", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 8.0, nothing, nothing), ("MC1", Coluna.MathProg.MasterCol, 1.0, 0.0, 1.0, nothing, 2), ("MC2", Coluna.MathProg.MasterCol, 2.0, 1.0, 1.0, nothing, 2), ("MC3", Coluna.MathProg.MasterCol, 4.0, 2.0, 4.0, nothing, 2), ("y1", Coluna.MathProg.MasterPureVar, 5.0, 1.5, Inf, nothing, nothing), ("y2", Coluna.MathProg.MasterPureVar, 0.0, 2.5, Inf, nothing, nothing), ("pricing_setup", Coluna.MathProg.MasterRepPricingSetupVar, 0.0, 1.0, 1.0, nothing, nothing) ], [ # name, duty, rhs, sense , id ("c1", Coluna.MathProg.MasterMixedConstr, 4.0, ClMP.Greater, nothing), ("c2", Coluna.MathProg.MasterConvexityConstr, 0.0, ClMP.Greater, nothing), ("c3", Coluna.MathProg.MasterConvexityConstr, 4.0, ClMP.Less, nothing), ] ) spform, sp_name_to_var, sp_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwSp( Coluna.MathProg.getid(master_name_to_var["pricing_setup"]), Coluna.MathProg.getid(master_name_to_constr["c2"]), Coluna.MathProg.getid(master_name_to_constr["c3"]), Coluna.MathProg.Integ, ), [ ("x1", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 1.0, Coluna.Algorithm.getid(master_name_to_var["x1"]), Coluna.MathProg.getuid(master)), ("x2", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 2.0, Coluna.Algorithm.getid(master_name_to_var["x2"]), Coluna.MathProg.getuid(master)) ], [ # name, duty, rhs, sense, id ("c4", Coluna.MathProg.DwSpPureConstr, 2.0, ClMP.Greater, nothing) ] ) var_ids = [Coluna.MathProg.getid(sp_name_to_var["x1"]), Coluna.MathProg.getid(sp_name_to_var["x2"])] pool = Coluna.MathProg.get_primal_sol_pool(spform) for (name, vals) in Iterators.zip( ["MC1", "MC2", "MC3"], [ # x1, x2 Float64[1.0, 2.0], Float64[1.0, 1.0], Float64[0.0, 1.0] ] ) col_id = Coluna.MathProg.VarId( Coluna.MathProg.getid(master_name_to_var[name]), origin_form_uid = Coluna.MathProg.getuid(spform), duty = Coluna.MathProg.DwSpPrimalSol ) Coluna.MathProg.push_in_pool!( pool, Coluna.MathProg.PrimalSolution(spform, var_ids, vals, 1.0, Coluna.MathProg.FEASIBLE_SOL), col_id, 1.0 ) end dw_pricing_sps = Dict( Coluna.MathProg.getuid(spform) => spform ) presolve_master_repr = _presolve_formulation( ["x1", "x2"], ["c1"], [1 1], master, master_name_to_var, master_name_to_constr, ) presolve_master_restr = _presolve_formulation( ["MC1", "MC2", "MC3", "y1", "y2"], ["c1", "c2", "c3"], [3 2 1 2 2; 1 1 1 0 0; 1 1 1 0 0], master, master_name_to_var, master_name_to_constr, ) presolve_sp = _presolve_formulation( ["x1", "x2"], ["c4"], [1 1], spform, sp_name_to_var, sp_name_to_constr; lm = 0, um = 4 ) presolve_dw_sps = Dict( Coluna.MathProg.getuid(spform) => presolve_sp ) local_restr_partial_sol = [0.0, 1.0, 2.0, 1.0, 0.0] # MC2 = 1, MC3 = 2, y1 = 1. _, nb_fixed_columns_per_sp = Coluna.Algorithm.partial_sol_on_repr( dw_pricing_sps, presolve_master_repr, presolve_master_restr, local_restr_partial_sol ) presolve_pricing_sps = Dict( Coluna.MathProg.getuid(spform) => presolve_sp ) Coluna.Algorithm.update_subproblem_multiplicities!(presolve_pricing_sps, nb_fixed_columns_per_sp) presolve_reform = Coluna.Algorithm.DwPresolveReform( presolve_master_repr, presolve_master_restr, presolve_pricing_sps ) var_domains = Coluna.Algorithm.compute_repr_master_var_domains( dw_pricing_sps, presolve_reform, local_restr_partial_sol ) # variable domains are computed only using new multiplicity of the subproblems. @test var_domains[1] == (0, 1) @test var_domains[2] == (0, 2) local_repr_partial_sol = [1.0, 3.0] Coluna.Algorithm.propagate_partial_sol_to_global_bounds!( presolve_master_repr, local_repr_partial_sol, var_domains ) # new global bounds from the partial solution propagation are: # -1 <= x1 <= 3 # -3 <= x2 <= 5 # which are dominated by the global bounds computed from the sp multiplicity. @test presolve_master_repr.form.lbs[1] == 0 @test presolve_master_repr.form.ubs[1] == 1 @test presolve_master_repr.form.lbs[2] == 0 @test presolve_master_repr.form.ubs[2] == 2 end register!(unit_tests, "presolve_algorithm", test_compute_repr_master_var_domains_and_propagate_partial_sol_into_master) function test_presolve_full() formstring = """ master min 1.0 x_1 + 1.0 x_2 + 1.0 x_3 + 1.0 x_4 + 1.0 x_5 + 0.0 x_6 + 0.0 PricingSetupVar_sp_6 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 2.0 x_1 + 3.0 x_2 + 1.0 x_3 + 0.0 x_4 + 1.0 x_5 + 0.0 x_6 == 5.0 0.0 x_1 + 0.0 x_2 + 0.0 x_3 + 1.0 x_4 + 0.0 x_5 + 0.0 x_6 >= 2.0 1.0 PricingSetupVar_sp_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 <= 2.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 <= 2.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_6 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_6 <= 0.0 {MasterConvexityConstr} dw_sp min x_6 + 0.0 PricingSetupVar_sp_6 s.t. 1.0 x_6 <= 1.0 dw_sp min x_1 + x_2 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_1 + 1.0 x_2 >= 1.0 solutions 1.0 x_1 {MC_1} dw_sp min x_3 + x_4 + 0.0 PricingSetupVar_sp_5 s.t. 1.0 x_3 + 1.0 x_4 >= 4.0 solutions 2.0 x_3 {MC_2} continuous pure x_5 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5, PricingSetupVar_sp_6 representatives x_1, x_2, x_3, x_4, x_6 global_bounds 0.0 <= x_1 <= 1.0 0.0 <= x_2 <= 1.0 0.0 <= x_3 <= 3.0 0.0 <= x_4 <= 3.0 0.0 <= x_5 <= 1.0 -Inf <= x_6 <= Inf bounds 0.0 <= x_1 <= 1.0 0.0 <= x_2 <= 1.0 0.0 <= x_3 <= 3.0 0.0 <= x_4 <= 3.0 0.0 <= x_5 <= 1.0 -Inf <= x_6 <= Inf 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 1.0 <= PricingSetupVar_sp_6 <= 1.0 """ env, master, sps, _, reform = reformfromstring(formstring) #print(IOContext(stdout, :user_only => true), reform) master_vars = Dict{String, Coluna.MathProg.VarId}( Coluna.MathProg.getname(master, var) => varid for (varid, var) in Coluna.MathProg.getvars(master) ) master_constrs = Dict{String, Coluna.MathProg.ConstrId}( Coluna.MathProg.getname(master, constr) => constrid for (constrid, constr) in Coluna.MathProg.getconstrs(master) ) presolve_algorithm = Coluna.Algorithm.PresolveAlgorithm(; verbose=true) input = Coluna.Algorithm.PresolveInput( Dict(master_vars["MC_1"] => 1.0, master_vars["x_5"] => 1.0) ) output = Coluna.Algorithm.run!(presolve_algorithm, env, reform, input) #print(IOContext(stdout, :user_only => true), reform) #print(Coluna.MathProg.getmaster(reform)) @test output.feasible == true @test Coluna.MathProg.getcurlb(master, master_vars["x_1"]) == 0.0 @test Coluna.MathProg.getcurub(master, master_vars["x_1"]) == 0.0 @test Coluna.MathProg.getcurlb(master, master_vars["x_2"]) == 0.0 @test Coluna.MathProg.getcurub(master, master_vars["x_2"]) == 0.0 @test Coluna.MathProg.getcurlb(master, master_vars["x_3"]) == 2.0 @test Coluna.MathProg.getcurub(master, master_vars["x_3"]) == 2.0 @test Coluna.MathProg.getcurlb(master, master_vars["x_4"]) == 2.0 @test Coluna.MathProg.getcurub(master, master_vars["x_4"]) == 3.0 @test Coluna.MathProg.getcurlb(master, master_vars["x_5"]) == 0.0 @test Coluna.MathProg.getcurub(master, master_vars["x_5"]) == 0.0 @test Coluna.MathProg.getcurrhs(master, master_constrs["c1"]) == 2.0 @test Coluna.MathProg.getcurrhs(master, master_constrs["c2"]) == 2.0 @test Coluna.MathProg.getcurrhs(master, master_constrs["c3"]) == 0.0 # l_mult of sp4 @test Coluna.MathProg.getcurrhs(master, master_constrs["c4"]) == 0.0 # u_mult of sp4 @test Coluna.MathProg.getcurrhs(master, master_constrs["c5"]) == 1.0 # l_mult of sp5 @test Coluna.MathProg.getcurrhs(master, master_constrs["c6"]) == 1.0 # u_mult of sp5 for sp in sps sp_vars = Dict{String, Coluna.MathProg.VarId}( Coluna.MathProg.getname(sp, var) => varid for (varid, var) in Coluna.MathProg.getvars(sp) ) if findfirst(name->name == "x_3",collect(keys(sp_vars))) !== nothing @test Coluna.MathProg.getcurlb(sp, sp_vars["x_3"]) == 2.0 @test Coluna.MathProg.getcurub(sp, sp_vars["x_3"]) == 2.0 @test Coluna.MathProg.getcurlb(sp, sp_vars["x_4"]) == 2.0 @test Coluna.MathProg.getcurub(sp, sp_vars["x_4"]) == 3.0 elseif findfirst(name->name == "x_1",collect(keys(sp_vars))) !== nothing @test Coluna.MathProg.getcurub(sp, sp_vars["x_1"]) == 0.0 @test Coluna.MathProg.getcurlb(sp, sp_vars["x_2"]) == 1.0 end end master_partal_sol = Coluna.MathProg.getpartialsol(master) @test master_partal_sol[master_vars["x_5"]] == 1.0 @test master_partal_sol[master_vars["MC_1"]] == 1.0 # testing "expanded" printing of a primal solution with columns primal_solution = Coluna.MathProg.PrimalSolution( master, [master_vars["MC_1"], master_vars["x_5"]], [1.0, 1.0], 1.0, Coluna.ColunaBase.FEASIBLE_SOL ) _io = IOBuffer() print(IOContext(_io, :user_only => true), primal_solution) @test String(take!(_io)) == """ Primal solution \| x_5 = 1.0 \| MC_1 = [x_1 = 1.0 ] = 1.0 └ value = 1.00 """ @test Coluna.Algorithm.column_is_proper(master_vars["MC_1"], reform) == false @test Coluna.Algorithm.column_is_proper(master_vars["MC_2"], reform) == true input = Coluna.Algorithm.PresolveInput(Dict(master_vars["MC_1"] => 0.0)) output = Coluna.Algorithm.run!(presolve_algorithm, env, reform, input) @test output.feasible == true input = Coluna.Algorithm.PresolveInput(Dict(master_vars["MC_1"] => 1.0)) output = Coluna.Algorithm.run!(presolve_algorithm, env, reform, input) @test output.feasible == false return nothing end register!(unit_tests, "presolve_reformulation", test_presolve_full)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	4026	# # Propagation between formulations of Dantzig-Wolf reformulation. # # In the following tests, we consider that the variables have the possible following duties # # Original formulation: # # variable: # # - OriginalVar # # constraint: # # - OriginalConstr # # Master: # # variable: # # - MasterRepPricingVar # # - MasterPureVar # # - MasterCol # # - MasterArtVar # # constraint: # # - MasterPureConstr # # - MasterMixedConstr # # - MasterConvexityConstr # # Pricing subproblems: # # variable: # # - DwSpPricingVar # # - DwSpSetupVar # # constraint: # # - DwSpPureConstr # ## Helpers function _presolve_propagation_vars(form, var_descriptions) vars = Tuple{String, Coluna.MathProg.Variable}[] for (name, duty, cost, lb, ub, id, origin_form_id) in var_descriptions if isnothing(id) var = if isnothing(origin_form_id) Coluna.MathProg.setvar!(form, name, duty, cost = cost, lb = lb, ub = ub) else Coluna.MathProg.setvar!( form, name, duty, cost = cost, lb = lb, ub = ub, id = Coluna.MathProg.VarId( duty, form.env.var_counter += 1, origin_form_id; ) ) end else id_of_clone = if isnothing(origin_form_id) ClMP.VarId(id; duty = duty) else ClMP.VarId(id; duty = duty, origin_form_uid = origin_form_id) end var = Coluna.MathProg.setvar!(form, name, duty; id = id_of_clone, cost = cost, lb = lb, ub = ub) end push!(vars, (name, var)) end return vars end function _presolve_propagation_constrs(form, constr_descriptions) constrs = Tuple{String, Coluna.MathProg.Constraint}[] for (name, duty, rhs, sense, id) in constr_descriptions if isnothing(id) constr = Coluna.MathProg.setconstr!(form, name, duty, rhs = rhs, sense = sense) else id_of_clone = ClMP.ConstrId(id; duty = duty) constr = Coluna.MathProg.setconstr!(form, name, duty; id = id_of_clone, rhs = rhs, sense = sense) end push!(constrs, (name, constr)) end return constrs end function _mathprog_formulation!(env, form_duty, var_descriptions, constr_descriptions) form = Coluna.MathProg.create_formulation!(env, form_duty) vars = _presolve_propagation_vars(form, var_descriptions) constrs = _presolve_propagation_constrs(form, constr_descriptions) name_to_vars = Dict(name => var for (name, var) in vars) name_to_constrs = Dict(name => constr for (name, constr) in constrs) return form, name_to_vars, name_to_constrs end function _presolve_formulation(var_names, constr_names, matrix, form, name_to_vars, name_to_constrs; lm=1, um=1) rhs = [Coluna.MathProg.getcurrhs(form, name_to_constrs[name]) for name in constr_names] sense = [Coluna.MathProg.getcursense(form, name_to_constrs[name]) for name in constr_names] lbs = [Coluna.MathProg.getcurlb(form, name_to_vars[name]) for name in var_names] ubs = [Coluna.MathProg.getcurub(form, name_to_vars[name]) for name in var_names] partial_solution = zeros(Float64, length(lbs)) form_repr = Coluna.Algorithm.PresolveFormRepr( matrix, rhs, sense, lbs, ubs, partial_solution, lm, um ) col_to_var = [name_to_vars[name] for name in var_names] row_to_constr = [name_to_constrs[name] for name in constr_names] var_to_col = Dict(ClMP.getid(name_to_vars[name]) => i for (i, name) in enumerate(var_names)) constr_to_row = Dict(ClMP.getid(name_to_constrs[name]) => i for (i, name) in enumerate(constr_names)) presolve_form = Coluna.Algorithm.PresolveFormulation( col_to_var, row_to_constr, var_to_col, constr_to_row, form_repr, Coluna.MathProg.ConstrId[], Dict{Coluna.MathProg.VarId, Float64}() ) return presolve_form end	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	code	36613	# To be able to properly test the tree search implemented in Coluna, we write a redefinition of the tree search interface with a customized search space TestBaBSearchSpace. # The goal is to simplify the tests writings by giving the possibility to "build" a specific branch and bound tree. # To do so, we use customized conquer and divide algorithms, together with node ids. To be more precise, each test corresponds to the construction of a branch and bound tree where the nodes are built thanks to a deterministic divide algorithm where we specify the nodes ids, and a deterministic conquer which matches each node id to the optimization state we want for the given node. # Flags are added to the conquer and divide algorithms in order to be able to check which nodes have been explored during the execution. The expected behaviour of each test is specified at its beginning. # WARNING: the re-implementation is generic, but the tests have been implemented keeping in mind a DFS exploration strategy. The expected results (and therefore the tests) are no longer valid if we change the explore strategy. mutable struct TestBaBSearchSpace <: Coluna.Algorithm.AbstractColunaSearchSpace inner::Coluna.Algorithm.BaBSearchSpace end # We create a TestBaBNode which wraps a "real" branch and bound Node and carries the node id. mutable struct TestBaBNode <: Coluna.TreeSearch.AbstractNode inner::Coluna.Algorithm.Node id::Int end # LightNode are used to contains the minimal information needed to create real nodes. In the tests only LightNode are defined, the re-implementation of the interface is then responsible to create real nodes when it is necessary. mutable struct LightNode id::Int depth::Int parent_ip_dual_bound::Coluna.Algorithm.Bound end # Deterministic conquer, a map with all the nodes ids matched to their optimization state struct DeterministicConquer <: Coluna.Algorithm.AbstractConquerAlgorithm conquer::Dict{Int, Coluna.OptimizationState} new_primal_sol::Dict{Int, Coluna.PrimalSolution} run_conquer_on_nodes::Vector{Int} ## FLAG, node ids of the nodes on which we have run conquer during the execution end ## the only information the deterministic conquer needs is the node id but we need to embedded it in a TestBaBConquerInput to match the implementation of children in treesearch struct TestBaBConquerInput node_id::Int inner::Coluna.Algorithm.ConquerInputFromBaB end Coluna.Algorithm.get_global_primal_handler(input::TestBaBConquerInput) = Coluna.Algorithm.get_global_primal_handler(input.inner) Coluna.Algorithm.get_conquer_input_ip_dual_bound(input::TestBaBConquerInput) = Coluna.Algorithm.get_conquer_input_ip_dual_bound(input.inner) Coluna.Algorithm.get_node_depth(input::TestBaBConquerInput) = Coluna.Algorithm.get_node_depth(input.inner) Coluna.Algorithm.get_units_to_restore(input::TestBaBConquerInput) = Coluna.Algorithm.get_units_to_restore(input.inner) struct TestBaBConquerOutput node_id::Int inner::Coluna.Algorithm.OptimizationState ## to update if we create a "real" BaBConquerOutput in the branch and bound end # Deterministic divide, match each node id to the nodes that should be generated as children from this node. struct DeterministicDivide <: Coluna.AlgoAPI.AbstractDivideAlgorithm divide::Dict{Int, Vector{LightNode}} ## the children are creating using the minimal information, they will turned into real nodes later in the algorithm run nodes_created_by_divide::Vector{Int} ## FLAG, node ids of the nodes that have been created by divide during the run of the branch-and-bound run_divide_on_nodes::Vector{Int} ## FLAG, node ids of the nodes on which we have run divide end ## the only information the deterministic divide needs is the node id but we need to embedded it in a TestBaBDivideInput to match the implementation of children in treesearch struct TestBaBDivideInput <: Coluna.Branching.AbstractDivideInput node_id::Int parent_conquer_output::Coluna.OptimizationState ## to update if we create a "real" BaBDivideInput in branch and bound end Coluna.Branching.get_conquer_opt_state(divide_input::TestBaBDivideInput) = return divide_input.parent_conquer_output # We redefine the interface for TestBaBSearchSpace: function Coluna.TreeSearch.new_space(::Type{TestBaBSearchSpace}, alg, model, input) inner_space = Coluna.TreeSearch.new_space( Coluna.Algorithm.BaBSearchSpace, alg, model, input) return TestBaBSearchSpace(inner_space) end # new_root returns a TestBaBNode with id 1 by default. The stack in treesearch (in explore.jl) will therefore contains nodes of type TestBaBNode. function Coluna.TreeSearch.new_root(space::TestBaBSearchSpace, input) inner = Coluna.TreeSearch.new_root(space.inner, input) return TestBaBNode(inner, 1) ## root id is set to 1 by default end function Coluna.TreeSearch.stop(space::TestBaBSearchSpace, untreated_nodes) inner_untreated_nodes = map(node->node.inner, untreated_nodes) return Coluna.TreeSearch.stop(space.inner, inner_untreated_nodes) end Coluna.TreeSearch.tree_search_output(space::TestBaBSearchSpace) = Coluna.TreeSearch.tree_search_output(space.inner) # methods called by native method children (in branch_and_bound.jl) Coluna.Algorithm.get_previous(space::TestBaBSearchSpace) = Coluna.Algorithm.get_previous(space.inner) Coluna.Algorithm.set_previous!(space::TestBaBSearchSpace, previous::TestBaBNode) = Coluna.Algorithm.set_previous!(space.inner, previous.inner) Coluna.Algorithm.get_reformulation(space::TestBaBSearchSpace) = Coluna.Algorithm.get_reformulation(space.inner) # extra methods needed by the new implementation of treesearch with the storage of the leaves state: Coluna.Algorithm.node_is_leaf(space::TestBaBSearchSpace, current::TestBaBNode, conquer_output::TestBaBConquerOutput) = Coluna.Algorithm.node_is_leaf(space.inner, current.inner, conquer_output.inner) Coluna.Algorithm.is_pruned(space::TestBaBSearchSpace, current::TestBaBNode) = Coluna.Algorithm.is_pruned(space.inner, current.inner) Coluna.Algorithm.node_is_pruned(space::TestBaBSearchSpace, current::TestBaBNode) = Coluna.Algorithm.node_is_pruned(space.inner, current.inner) # *********** redefinition of the methods to implement the deterministic conquer: ********* Coluna.Algorithm.get_conquer(space::TestBaBSearchSpace) = Coluna.Algorithm.get_conquer(space.inner) ## the only information the deterministic conquer needs is the node id, but we need to compute a BaBConquerInput as inner: function Coluna.Algorithm.get_input(alg::DeterministicConquer, space::TestBaBSearchSpace, node::TestBaBNode) inner = Coluna.Algorithm.get_input(alg, space.inner, node.inner) return TestBaBConquerInput(node.id, inner) end ## given the node id in the input, retrieves the corresponding optimization state in the dict, returns it together with the node id to pass them to the divide function Coluna.Algorithm.run!(alg::DeterministicConquer, env, reform, input) push!(alg.run_conquer_on_nodes, input.node_id) ## update flag new_primal_sol = get(alg.new_primal_sol, input.node_id, nothing) if !isnothing(new_primal_sol) # Tell the global primal bound handler that we found a new primal solution. Coluna.Algorithm.store_ip_primal_sol!(Coluna.Algorithm.get_global_primal_handler(input), new_primal_sol) end conquer_output = alg.conquer[input.node_id] return TestBaBConquerOutput(input.node_id, conquer_output) ## pass node id as a conquer output end function Coluna.Algorithm.after_conquer!(space::TestBaBSearchSpace, current, conquer_output) return Coluna.Algorithm.after_conquer!(space.inner, current.inner, conquer_output.inner) end # ********* redefinition of the methods to implement the deterministic divide: *********** Coluna.Algorithm.get_divide(space::TestBaBSearchSpace) = Coluna.Algorithm.get_divide(space.inner) ## call to the native routine to check if divide should be run Coluna.Algorithm.run_divide(space::TestBaBSearchSpace, input::TestBaBDivideInput) = Coluna.Algorithm.run_divide(space.inner, input) ## the only information the deterministic divide needs is the node id but we need to embedded it in a TestBaBDivideInput to match the implementation of children in treesearch function Coluna.Algorithm.get_input(::Coluna.AlgoAPI.AbstractDivideAlgorithm, ::TestBaBSearchSpace, ::TestBaBNode, conquer_output) return TestBaBDivideInput(conquer_output.node_id, conquer_output.inner) end ## takes the node id as the input, retrieve the list of (LightNode) children of the corresponding node and returns a DivideOutput made up of these (LightNode) children. function Coluna.Algorithm.run!(alg::DeterministicDivide, ::Coluna.Env, ::Coluna.MathProg.Reformulation, input::TestBaBDivideInput) push!(alg.run_divide_on_nodes, input.node_id) ##update flag children = alg.divide[input.node_id] for c in children ## update flag push!(alg.nodes_created_by_divide, c.id) end return Coluna.Algorithm.DivideOutput(children) end # constructs a real node from a LightNode, used in new_children to built real children from the minimal information contained in LightNode function Coluna.Algorithm.Node(node::LightNode) return Coluna.Algorithm.Node(node.depth, " ", nothing, node.parent_ip_dual_bound, Coluna.Algorithm.Records()) end ## The candidates are passed as LightNodes and the current node is passed as a TestBaBNode. The method retrieves the inner nodes to run the native method new_children of branch_and_bound.jl, gets the result as a vector of Nodes and then re-built a solution as a vector of TestBaBNodes using the nodes ids contained in LightNode structures. # branches input is a divide output with children of type LightNode. In the native method new_children in branch_and_bound.jl, those children are retrieved via get_children and then real nodes are created with a direct call to the constructor Node so it is sufficient to re-write a Node(child) method with child a LightNode (as above) to make the method works. function Coluna.Algorithm.new_children(space::TestBaBSearchSpace, branches::Coluna.Algorithm.DivideOutput{LightNode}, node::TestBaBNode) new_children_inner = Coluna.Algorithm.new_children(space.inner, branches, node.inner) ## vector of Nodes ids = map(node -> node.id, branches.children) ## vector of ids children = map( (n, id) -> TestBaBNode(n, id), new_children_inner, ids)## build the list of TestBaBNode return children end Coluna.Algorithm.node_change!(previous::Coluna.Algorithm.Node, current::TestBaBNode, space::TestBaBSearchSpace) = Coluna.Algorithm.node_change!(previous, current.inner, space.inner) # end of the interface's redefinition # Helper function _tree_search_reformulation() param = Coluna.Params() env = Coluna.Env{Coluna.MathProg.VarId}(param) origform = Coluna.MathProg.create_formulation!(env, Coluna.MathProg.Original()) master = Coluna.MathProg.create_formulation!(env, Coluna.MathProg.DwMaster()) dws = Dict{Coluna.MathProg.FormId, Coluna.MathProg.Formulation{Coluna.MathProg.DwSp}}() benders = Dict{Coluna.MathProg.FormId, Coluna.MathProg.Formulation{Coluna.MathProg.BendersSp}}() reform = Coluna.MathProg.Reformulation(env, origform, master, dws, benders) return reform, env end # Tests: #```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 20, \n ip_primal_sol = 40\| 1 # 1((1)) --> \|lp_dual_bound = 20, \n ip_primal_sol = 20\| 2((2)) # 1 --> \|should not be explored \n because gap is closed \n at node 2\| 3((3)) #``` # exploration should stop at node 2 because gap is gap_closed # conquer and divide should not be run on node 3 # divide should not be run on node 2 # status: OPTIMAL with prima solution = 20.0 function test_stop_gap_closed() ## create an empty formulation reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) ## empty input optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) ## should not be explored primalsol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 40.0, Coluna.ColunaBase.FEASIBLE_SOL) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 20.0, Coluna.ColunaBase.FEASIBLE_SOL) ## set up the conquer and the divide (and thus the shape of the branch-and-bound tree, see the mermaid diagram below) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => primalsol1, 2 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 20.0)), LightNode(2, 1, Coluna.DualBound(master, 20.0))], ##remark: should pass first the right child, and second the left child (a bit contre intuitive ?) ##TODO see and fix code 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test !(2 in dividealg.run_divide_on_nodes) ## we converge at node 2, we should not enter divide @test !(3 in conqueralg.run_conquer_on_nodes) @test !(3 in dividealg.run_divide_on_nodes) end register!(unit_tests, "treesearch", test_stop_gap_closed) #```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 55, \n ip_primal_sol = _\| 1((1)) # 1 --> \|INFEASIBLE\| 2((2)) # 1 --> \|INFEASIBLE\| 3((3)) #``` # exploration should stop at node 3 # divide should not be called on node 2 and 3 # should return status = INFEASIBLE function test_infeasible_pb() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) optstate3 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict(), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 55.0)), LightNode(2, 1, Coluna.DualBound(master, 55.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.INFEASIBLE @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test !(2 in dividealg.run_divide_on_nodes) @test !(2 in dividealg.run_divide_on_nodes) end register!(unit_tests, "treesearch", test_infeasible_pb) #```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 55, \n ip_primal_sol = 60\| 1((1)) # 1 --> \|INFEASIBLE\| 2((2)) # 1 --> \|lp_dual_bound = 60, \n ip_primal_sol = 60\| 3((3)) #``` # the exploration should stop on node 3 because the gap is closed # divide should not be run on node 2 because the subproblem is infeasible function test_infeasible_sp() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 60.0)) optimalsol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => optimalsol, 3 => optimalsol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 55.0)), LightNode(2, 1, Coluna.DualBound(master, 55.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == optimalsol @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test !(2 in dividealg.run_divide_on_nodes) @test !(3 in dividealg.run_divide_on_nodes) @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_infeasible_sp) #```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 53, \n ip_primal_sol = 60\| 1((1)) # 1 --> \|lp_dual_bound = 56, \n ip_primal_sol = 60\| 2((2)) # 1 --> \|lp_dual_bound = 57, \n ip_primal_sol = 60\| 3((3)) #``` # not enough information to branch on both leaves 2 and 3 and the gap is not closed at any node # should return OTHER_LIMIT with dual_bound = 56 (worst among the leaves) and primal bound = 60, all nodes should be explored function test_all_explored_with_pb() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 53.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 53.0)), LightNode(2, 1, Coluna.DualBound(master, 53.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test Coluna.get_ip_dual_bound(algstate).value ≈ 56.0 @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 1 in dividealg.run_divide_on_nodes @test 2 in dividealg.run_divide_on_nodes @test 3 in dividealg.run_divide_on_nodes @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_all_explored_with_pb) #```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 53, \n ip_primal_sol = 60\| 1((1)) # 1 --> \|lp_dual_bound = 56, \n ip_primal_sol = 60\| 2((2)) # 1 --> \|lp_dual_bound = 57, \n ip_primal_sol = 58\| 3((3)) #``` # not enough information to branch on leaves, should return OTHER_LIMIT with dual_bound = 56 and primal_bound = 58 function test_all_explored_with_pb_2() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 53.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) primal_sol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 58.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => primal_sol1, 3 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 53.0)), LightNode(2, 1, Coluna.DualBound(master, 53.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test Coluna.get_ip_dual_bound(algstate).value ≈ 56.0 @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 1 in dividealg.run_divide_on_nodes @test 2 in dividealg.run_divide_on_nodes @test 3 in dividealg.run_divide_on_nodes @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_all_explored_with_pb_2) #```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 55, \n ip_primal_bound = _\| 1((1)) # 1 --> \|lp_dual_bound = 56, \n ip_primal_bound = _\| 2((2)) # 1 --> \|lp_dual_bound = 57, \n ip_primal_bound = _\| 3((3)) # #``` # not enough information to branch on both leaves # no primal bound # should return OTHER_LIMIT with dual_bound = 56 function test_all_explored_without_pb() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict(), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 53.0)), LightNode(2, 1, Coluna.DualBound(master, 53.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @test Coluna.get_ip_dual_bound(algstate).value ≈ 56.0 @test isnothing(Coluna.get_best_ip_primal_sol(algstate)) @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 1 in dividealg.run_divide_on_nodes @test 2 in dividealg.run_divide_on_nodes @test 3 in dividealg.run_divide_on_nodes @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_all_explored_without_pb) # ```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 20, \n ip_primal_sol = 40\| 1 # 1((1)) --> \|lp_dual_bound = 20, \n ip_primal_sol = 40\| 2((2)) # 1 --> \|lp_dual_bound = 30, \n ip_primal_sol = 30\| 5((5)) # 2 --> \|lp_dual_bound = 45, \n ip_primal_sol = 40\| 3((3)) # 2 --> \|lp_dual_bound = 45, \n ip_primal_sol = 40\| 4((4)) # 5 --> \|STOP\| stop( ) # ``` # At nodes 3 and 4, the local lp_dual_bound > the primal bound but the global dual bound < primal bound so the algorithm should continue and stop at node 5 when gap is closed # status: OPTIMAL with primal solution = 30.0 function test_local_db() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, ip_primal_bound = Coluna.MathProg.PrimalBound(master, 40.0), lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 45.0)) optstate4 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 45.0)) optstate5 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 30.0)) primalsol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 40.0, Coluna.ColunaBase.FEASIBLE_SOL) optimalsol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 30.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3, 4 => optstate4, 5 => optstate5 ), Dict( 1 => primalsol1, 5 => optimalsol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(5, 1, Coluna.DualBound(master, 20.0)), LightNode(2, 1, Coluna.DualBound(master, 20.0))], 2 => [LightNode(4, 2, Coluna.DualBound(master, 20.0)), LightNode(3, 2, Coluna.DualBound(master, 20.0))], 3 => [], 4 => [], 5 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == optimalsol @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 4 in dividealg.nodes_created_by_divide @test 5 in dividealg.nodes_created_by_divide @test !(3 in dividealg.run_divide_on_nodes) ## 3 and 4 should not be in run_divide_on_nodes ; they are pruned because their local db is worst than the current best primal sol @test !(4 in dividealg.run_divide_on_nodes) @test !(5 in dividealg.run_divide_on_nodes) end register!(unit_tests, "treesearch", test_local_db) #```mermaid #graph TD # 0( ) --> \|lp_dual_bound = 55, \n ip_primal_sol = 60\| 1 # 1((1)) --> \|lp_dual_bound = 55, \n ip_primal_sol = 56\| 2((2)) # 2 --> \|lp_dual_bound = 56, \n ip_primal_sol = 56\| 3((3)) # 2 --> \|lp_dual_bound = 56, \n ip_primal_sol = 56\| 4((4)) # 1 --> \|lp_dual_bound = 57, \n ip_primal_sol = 60\| 5((5)) # 5 --> \|STOP \n because primal sol found at 2\n is better than current db\| 6( ) #``` # exploration should not stop at nodes 3 and 4 because the gap between the local lp_dual_bound and the ip_primal_bound is closed, but not with the global dual bound. However, it should be stopped at node 5 because the primal bound found at node 2 is better than the local dual bound of node 5. # status: OPTIMAL with primal solution = 56.0 function test_pruning() ## create an empty formulation reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate4 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate5 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) primal_sol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 56.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3, 4 => optstate4, 5 => optstate5, 6 => Coluna.OptimizationState(master), ##should not be called 7 => Coluna.OptimizationState(master) ##should not be called ), Dict( 1 => primal_sol1, 2 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(5, 1, Coluna.DualBound(master, 55.0)), LightNode(2, 1, Coluna.DualBound(master, 55.0))], 2 => [LightNode(4, 2, Coluna.DualBound(master, 55.0)), LightNode(3, 2, Coluna.DualBound(master, 55.0))], 3 => [], 4 => [], 5 => [LightNode(7, 2, Coluna.DualBound(master, 57.0)), LightNode(6, 2, Coluna.DualBound(master, 57.0))], ##should not be called 6 => [],##should not be called 7 => [] ##should not be called, ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test !(6 in dividealg.nodes_created_by_divide) # 6 and 7 should not be created as 5 is pruned @test !(7 in dividealg.nodes_created_by_divide) @test !(3 in dividealg.run_divide_on_nodes) ## 3 and 4 should not be in run_divide_on_nodes ; they are pruned because their local db is equal to the current best primal sol @test !(4 in dividealg.run_divide_on_nodes) @test !(5 in dividealg.run_divide_on_nodes) ## 5 is not in run_divide_on_nodes either ; it is pruned because best primal bound found at node 2 is better than its db @test 5 in conqueralg.run_conquer_on_nodes ## however, 5 is in run_conquer_on_nodes because when it inherites the db from its parent, this db is better than the best primal solution @test !(6 in conqueralg.run_conquer_on_nodes) @test !(7 in conqueralg.run_conquer_on_nodes) end register!(unit_tests, "treesearch", test_pruning) function test_one_leaf_infeasible_and_then_node_limit() ## create an empty formulation reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 65.0)) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 # should not be called ), Dict(), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(2, 1, Coluna.DualBound(master, 65.0)), LightNode(3, 1, Coluna.DualBound(master, 65.0))], 2 => [], ##should not be called 3 => [], ##should not be called ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), maxnumnodes = 2 ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @show Coluna.getterminationstatus(algstate) end register!(unit_tests, "treesearch", test_one_leaf_infeasible_and_then_node_limit)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	5474	# Coluna 0.8.1 This is a minor update Features: - A new parameter whether to presolve the DW reformulation or not - A new parameter whether to do strong integrality check in column generation Fixed bugs: - A bug in the presolve algorithm when the partial solution to fix contains deactivated variables # Coluna 0.8.0 This is a major update which implements the presolve algorithm. Other features: - Possibility for the user to get the custom data of a column (i.e., SP solution) in the global solution. - Print the master and DW subproblem with only user-defined variables and constraints. - One can now specify the branching priority of columns, either through branching priority of DW sub-problems, or directly in the CustomData of the SP solution It also resolves some bugs: - Correction in dual price smoothing stabilization - Correction in integrality check inside column generation. - Correction in calculating initial (global) bounds of the master representative (implicit) variables. - Corrected the "Sleeping bug" related to the Id type promotion, which appeared in Julia 1.10 - Removed superfluous Heuristics module. - Global dual bound printer is corrected - Strong branching printer is corrected. # Coluna 0.7.0 This is minor update with two breaking changes: - Bounds of the representative variables in the master are now global (multiplicity of subproblem * bound) - DivideOutput has only one argument now - Generating child nodes from branching candidates is moved from select!() to advanced_select!(). This allows us to simplify the interface of branching candidates (we remove nodes from them). This simplification also serves to prepare the diving implementation. (PR 1072) - SbNode and Node definitions have been changed. # Coluna 0.6.6 - DynamicSparseArrays 0.7 main features are tested against JET. - Bug with the integer tolerance of projected solutions fixed - Improvements about the construction of the reformulation - Strong branching was not returning ip solution found when evaluating candidates # Coluna 0.6.5 This is a minor update that provides documentation together with tests and several bug fixes for the tree search algorithm. Stabilization for the column generation algorithm is now in beta version. # Coluna 0.6.4 This is a minor update that provides documentation and a bug fix in the integration of the column generation algorithm with the branch-and-bound. # Coluna 0.6.3 This is a minor update that provides: - improvements in column generation interface and generic functions - bugfix in column generation (wrong calculation of the lagrangian dual bound when identical subproblems) - column generation stabilization (alpha version) # Coluna 0.6.2 This is a minor update that provides fixes in the Benders cut generation algorithm and documentation for the Benders API. # Coluna 0.6.1 This is a minor update but some changes may affect the integration of external algorithms with Coluna. Fixes: - Workflow of Benders algorithms is now fixed. More documentation will be available soon. Changes: - `ColunaBase.Bound{Space, Sense}` is now `ColunaBase.Bound`. The two parameters are now flags in the struct. All mathematical operations are not supported anymore, we need to convert the `Bound` to a `<:Real`. - `Algorithm.OptimizationState{F,S}` does not depend on the objective sense anymore and is now `Algorithm.OptimizationState{F}` - Improve Benders implementation & starting writing documentation # Coluna 0.6.0 This release is a major update of the algorithms as it implements the architectural choices of 0.5.0 in column generation and benders. About the algorithms: - We separated the generic codes and the interfaces from the implementation (doc will be available soon). The default implementation of algorithms is in the `Algorithm` module. Four new submodules `TreeSearch`, `Branching`, `ColGen`, and `Benders` contain generic code and interface. They are independent. - Refactoring of column generation - Refactoring and draft of benders cut generation - Tests and documentation - Various bug fixes - Some regressions as indicated in the Readme. # Coluna 0.5.0 This release is a major update of the algorithms. From now on, we will release new versions more frequently. In the `Algorithm` submodule: - Interface & generic implementation for the tree search algorithm; default implementation of a branch & bound; documentation - Simplified interface for storages; documentation - Interface & generic implementation for the branching algorithm; interface & default implementation for the strong branching; documentation - Preparation of the conquer algorithm refactoring - Preparation of the column generation algorithm refactoring - Preparation of the refactoring of the algorithms calling the subsolver - End of development of the Preprocessing algorithm (no unit tests and had bugs); it will be replaced by the Presolve algorithm that does not work - Increase of the reduced cost tolerance in the column generation algorithm - Separation of algorithm and printing logic - Various bug fixes In the `MathProg` submodule: - `VarIds` & `ConstrIds` are subtype of Integer so we can use them as indices of sparse vectors and arrays - Solution are stored in sparse array from `SparseArrays` (not a packed memory array from `DynamicSparseArray` anymore because the solution is static) Other: - Documentation of dynamic sparse arrays - Support of expressions in BlockDecomposition	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	4125	# Coluna.jl [![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://atoptima.github.io/Coluna.jl/stable) ![CI](https://github.com/atoptima/Coluna.jl/workflows/CI/badge.svg?branch=master) [![codecov](https://codecov.io/gh/atoptima/Coluna.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/atoptima/Coluna.jl) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![License: MPL 2.0](https://img.shields.io/badge/License-MPL%202.0-brightgreen.svg)](https://opensource.org/licenses/MPL-2.0) Coluna is a branch-and-price-and-cut framework written in Julia. You write an original MIP that models your problem using the [JuMP](https://github.com/jump-dev/JuMP.jl) modeling language and our specific extension [BlockDecomposition](https://github.com/atoptima/BlockDecomposition.jl) offers a syntax to specify the problem decomposition. Then, Coluna reformulates the original MIP and optimizes the reformulation using the algorithms you choose. Coluna aims to be very modular and tweakable so that you can define the behavior of your customized branch-and-price-and-cut algorithm. ## Installation Coluna is a [Julia Language](https://julialang.org/) package. You can install Coluna through the Julia package manager. Open Julia's interactive session (REPL) and type: ``` ] add Coluna ``` The documentation provides examples to run advanced branch-cut-and-price. Improvements in documentation are expected in the future. You can browse the [stable documentation](https://atoptima.github.io/Coluna.jl/stable) if you work with the latest release or the [dev documentation](https://atoptima.github.io/Coluna.jl/latest) if you work with the master version of Coluna. ## Features We aim to integrate into Coluna the state-of-the-art techniques used for branch-and-cut-and-price algorithms. - ![Stable](https://img.shields.io/badge/-stable-brightgreen) Features which are well-tested (but performance may still be improved). - Dantzig-Wolfe decomposition - Branch-and-bound algorithm (with branching in master) - Column generation (MILP pricing solver/pricing callback) - ![Beta](https://img.shields.io/badge/-beta-green) Features that work well but need more tests/usage and performance review before being stable: - Strong branching (with branching in master) - Stabilization for column generation - Cut generation (robust and non-robust) - Benders decomposition - Preprocessing (presolve) of formulations and reformulations - ![Alpha](https://img.shields.io/badge/-alpha-yellow) Features that should work. Structural work is done but these features may have bugs: - Benders cut generation - ![Dev](https://img.shields.io/badge/-dev-orange) Features in development. - Clean-up of the master formulation (removal of unpromising columns and cuts) - Saving/restoring LP basis when changing a node in branch-and-bound ## Contributing If you encounter a bug or something unexpected happens while using Coluna, please open an issue via the GitHub issues tracker. See the list of [contributors](https://github.com/atoptima/Coluna.jl/graphs/contributors) who make Coluna possible. ## Premium support Using Coluna for your business? [Contact us](https://atoptima.com/contact/?sup) to get tailored and qualified support. ## Acknowledgments The platform development has received an important support grant from the international scientific society [Mathematical Optimization Society (MOS)](http://www.mathopt.org/) and [Région Nouvelle-Aquitaine](https://www.nouvelle-aquitaine.fr/). [Atoptima](https://atoptima.com/) [University of Bordeaux](https://www.u-bordeaux.fr/) [Inria](https://www.inria.fr/fr) ## Related packages - [BlockDecomposition](https://github.com/atoptima/BlockDecomposition.jl) is a JuMP extension to model decomposition. - [DynamicSparseArrays](https://github.com/atoptima/DynamicSparseArrays.jl) provides data structures based on packed-memory arrays for dynamic sparse matrices.	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	624	--- name: Bug report about: Create a report to help us improve title: '' labels: bug assignees: '' --- Describe the bug A clear and concise description of the bug. To Reproduce Give us a link to your model and instance (a Julia project with the Manifest file for example). Expected behavior A clear and concise description of what you expected to happen. If possible, copy the error message with the stacktrace. Environment (please complete the following information): - Julia version [e.g. 1.2] - OS: [e.g. Linux, Windows, MacOs] Additional context Add any other context about the problem here.	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	470	--- name: Feature request about: Suggest an idea for this project title: '' labels: enhancement assignees: guimarqu --- Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] Describe the feature or solution you'd like A clear and concise description of what you want to happen. Do you feel able to code this feature or solution? Why? What do you need?	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	3897	# Dynamic Sparse Arrays This package aims to provide dynamic sparse vectors and matrices in Julia. Unlike the sparse arrays provided in `SparseArrays`, arrays from this package have unfixed sizes. It means that we can add or delete rows and columns after the instantiation of the array. `DynamicSparseArrays` is a registered package. ## Introduction Coluna is a branch-cut-and-price framework. It means that Coluna's algorithms dynamically generate constraints and variables. Therefore, the coefficient matrix (which is usually sparse) must support the addition of new rows and columns. For this purpose, we implemented the packed-memory array data structure to handle the dynamic sparse vector introduced in the following papers: > BENDER, Michael A. et HU, Haodong. An adaptive packed-memory array. ACM Transactions on Database Systems (TODS), 2007, vol. 32, no 4, p. 26. > BENDER, Michael A., DEMAINE, Erik D., et FARACH-COLTON, Martin. Cache-oblivious B-trees. SIAM Journal on Computing, 2005, vol. 35, no 2, p. 341-358. On top of the packed-memory array, we implemented the data structure introduced in the following paper to handle the dynamic sparse matrix. > WHEATMAN, Brian et XU, Helen. Packed Compressed Sparse Row: A Dynamic Graph Representation. In : 2018 IEEE High Performance extreme Computing Conference (HPEC). IEEE, 2018. p. 1-7. The implementation may vary from the description in the papers. If you find some enhancements, please contact [guimarqu](https://github.com/guimarqu). ## Overview The packed-memory array (`PackedMemoryArray{K,T}`) is a `Vector{Union{Nothing,Tuple{K,T}}}` where `K` is the type of the keys and `T` is the type of the values. We keep empty entries (i.e. `Nothing`) in the array to add new values later fast. Non-empty entries are sorted by ascending key order. The array is virtually split into segments of equal size. The goal is to maintain the density (i.e. number of non-empty values/size of the segment) of each segment between pre-defined bounds. We also consider the density of certain unions of segments represented by nodes of the tree in gray. The root node of the tree is the union of all segments, thus the whole array. When one node of the tree has a density outside the allowed bounds, we need to rebalance the parent. It means that we redistribute the empty and non-empty entries to fit the density bounds. If the density bounds are not respected at the root node, we resize the array. On top of the packed-memory array, there is the (`PackedCSC{K,T}`). This is a particular case of a matrix where values are of type `T`, row keys of type `K`, and column keys of type `Int`. Each column of the matrix (partition) is delimited by a semaphore which is a non-empty entry with a reserved key value defined by the `semaphore_key` function. In the example, the first partition has its semaphore at position 1, starts at position 2, and finishes at position 9. At position 10, it's the semaphore that signals the beginning of the second partition. In each partition, non-empty entries are sorted by ascending key order. As you can see, the `PackedCSC{K,T}` is not well suited to the matrix. Indeed, each column is associated with a partition. If you have a column with only zero values, the array will contain a partition with only empty entries. Lastly, the type of column key is `Int`. Therefore, built on top of `PackedCSC{K,T}`, `MappedCSC{K,L,T}` corrects all these shortcomings. This data structure just associates a column key of type `L` to each partition of `PackedCSC{K,T}`. ```@raw html <div style="width:75%; margin-left:auto; margin-right:auto"> ``` ![Dynamic Sparse Arrays](assets/img/dynamic_sparse_arrays.svg) ```@raw html <p style="text-align: center;">Architecture overview.</p> </div> ``` ## References ```@meta CurrentModule = DynamicSparseArrays ``` ```@docs dynamicsparsevec dynamicsparse ```	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	2829	# Introduction !!! warning We assume that readers are familiar with integer programming and exact optimization methods. Coluna is under active development. Some features are undocumented because they are very experimental. Current users are expected to read the source code. Coluna is a framework written in Julia to implement a decomposition approach to optimize block-structured mixed-integer programs (MIP). Coluna relies on the tools of the JuMP-dev community at both ends of the problem treatment. It uses the JuMP modeling language upfront and MathOptInterface (MOI) to delegate master and subproblems to MIP solvers. The user introduces an original MIP that models his problem using the JuMP along our specific extension BlockDecomposition that offers a syntax to specify the problem decomposition. Coluna reformulates the original MIP using Dantzig-Wolfe and Benders decomposition techniques. Then, Coluna optimizes the reformulation using the algorithm chosen by the user. Coluna offers a "black-box" implementation of the branch-and-cut-and-price algorithm: 1. The input is the set of constraints and variables of the MIP in its natural/compact formulation (formulated with JuMP or MOI). 2. BlockDecomposition allows the user to provide Coluna with his decomposition of the model. The BlockDecomposition syntax allows the user to implicitly define subsystems in the MIP on which the decomposition is based. These subsystems are described by rows and/or columns indices. 3. The reformulation associated with the decomposition defined by the user is automatically generated by Coluna, without requiring any input from the user to define master columns, their reduced cost, pricing/separation problem, or Lagrangian bound. 4. A default column (and cut) generation procedure is implemented. It relies on underlying MOI optimizers to handle master and subproblems. However, the user can use pricing callbacks to solve the subproblems. 5. A branching scheme that preserves the pricing problem structure is offered by default; it runs based on priorities and directives specified by the user on the original variables. ## Installation Coluna is a package for [Julia 1.6+](https://docs.julialang.org/en/v1/manual/documentation/index.html). It requires JuMP to model the problem, BlockDecomposition to define the decomposition, and a [MIP solver supported by MathOptInterface](https://jump.dev/JuMP.jl/stable/installation/#Getting-Solvers-1) to optimize the master and the subproblems. You can install Coluna and its dependencies through the package manager of Julia by entering : ``` ] add Coluna ``` ## Acknowledgements Atoptima, Mathematical Optimization Society (MOS), Région Nouvelle-Aquitaine, University of Bordeaux, and Inria	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	2601	# Question & Answer #### Default algorithms of Coluna do not beat the commercial solver I usually use. Is it normal ? Yes it is. Solvers such as Gurobi, Cplex ... are handy powerful black-box tools. They can run a very efficient presolve step to simplify the formulation, automatically apply lots of valid inequalities (such as MIR or cover cuts), choose good branching strategies, or also run heuristics. However, when your formulation reaches a certain size, commercial solvers may run for hours without finding anything. This is the point where you may want to decompose your formulation. Coluna is a framework, not a solver. It provides algorithms to try column generation on your problem very easily. Then, you can devise your own branch-cut-and-price algorithm on top of Coluna's algorithms. to scale up and hopefully beats the commercial solver. To start customizing Coluna for your own problem, you can [separate valid inequalities](../user/callbacks/#Separation-callbacks) or [call your own algorithm that optimizes subproblems](../user/callbacks/#Pricing-callback). ## I'm using Gurobi as a subsolver #### My license prevents me from running several environments at the same time. How can I use a single environment for the master and all subproblems? You can use the `Gurobi.Env` constructor to create a single environment and pass it to the optimizers. ```julia const GRB_ENV = Gurobi.Env() coluna = optimizer_with_attributes( Coluna.Optimizer, "params" => Coluna.Params( solver = Coluna.Algorithm.TreeSearchAlgorithm() # default branch-cut-and-price ), "default_optimizer" => () -> Gurobi.Optimizer(GRB_ENV) ); ``` If you get a Gurobi error 10002, you should wrap the Gurobi environment as a reference to initialize it during runtime instead of compile time ([reference](https://github.com/jump-dev/Gurobi.jl/issues/424)). ```julia const GRB_ENV_REF = Ref{Gurobi.Env}() function __init__() GRB_ENV_REF[] = Gurobi.Env() return nothing end coluna = optimizer_with_attributes( Coluna.Optimizer, "params" => Coluna.Params( solver = Coluna.Algorithm.TreeSearchAlgorithm() # default branch-cut-and-price ), "default_optimizer" => () -> Gurobi.Optimizer(GRB_ENV_REF[]) ); ``` #### How to disable all outputs from Gurobi? You can refer to the following [article](https://support.gurobi.com/hc/en-us/articles/360044784552-How-do-I-suppress-all-console-output-from-Gurobi-) from Gurobi's knowledge base. We confirm that adding the following entry in the `gurobi.env` file works with Gurobi 10+: ``` LogToConsole 0 ```	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	1571	# Algorithm API !!! danger This is WIP. The API will change in future releases. An algorithm is a procedure that given a model and and input performs some operations and returns an output. ```@docs run! ``` Parameters of an algorithm may contain its child algorithms which used by it. Therefore, the algoirthm tree is formed, in which the root is the algorithm called to solver the model (root algorithm should be an optimization algorithm, see below). > TODO: explain why the parent algorithm must manage the records/storages of child algorithm. Algorithms are divided into two types : "manager algorithms" and "worker algorithms". Worker algorithms just continue the calculation. They do not store and restore units as they suppose it is done by their master algorithms. Manager algorithms may divide the calculation flow into parts. Therefore, they store and restore units to make sure that their child worker algorithms have units prepared. A worker algorithm cannot have child manager algorithms. Examples of manager algorithms : TreeSearchAlgorithm (which covers both BCP algorithm and diving algorithm), conquer algorithms, strong branching, branching rule algorithms (which create child nodes). Examples of worker algorithms : column generation, SolveIpForm, SolveLpForm, cut separation, pricing algorithms, etc. ## Optimization algorithms Optimization algorithms return an `OptimizationState`. ```@docs OptimizationState ``` ### Conventions > TODO: WIP - infeasible: infinite bounds, no solution, infeasible termination status.	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	2162	# Algorithms !!! danger Work in progress. ## Parameters of an algorithm From a user perspective, the algorithms are objects that contains a set of parameters. The object must inherit from `Coluna.AlgoAPI.AbstractAlgorithm`. We usually provide a keyword constructor to define default values for parameters and therefore ease the definition of the object. ```julia struct MyCustomAlgorithm <: Coluna.AlgoAPI.AbstractAlgorithm param1::Int param2::Float64 child_algo::Coluna.AlgoAPI.AbstractAlgorithm end # Help the user to define the algorithm: function MyCustomAlgorithm(; param1 = 1, param2 = 2, child_algo = AnotherAlgorithm() ) return MyCustomAlgorithm(param1, param2, child_algo) end ``` Algorithms can use other algorithms. They are organized as a tree structure. Example for the TreeSearchAlgorithm : ```mermaid graph TD TreeSearchAlgorithm ---> ColCutGenConquer TreeSearchAlgorithm ---> ClassicBranching ColCutGenConquer --> ColumnGeneration ColCutGenConquer --> RestrictedHeuristicMaster RestrictedHeuristicMaster --> SolveIpForm#2 ColumnGeneration --> SolveLpForm#1 ColumnGeneration --> SolveIpForm#1 ColumnGeneration --> CutCallbacks ``` ```@docs Coluna.AlgoAPI.AbstractAlgorithm ``` ## Init ### Parameters checking When Coluna starts, it initializes the algorithms chosen by the user. A most important step is to check the consistency of the parameters supplied by the user and the compatibility of the algorithms with the model that will be received (usually `MathProg.Reformulation`). Algorithms usually have many parameters and are sometimes interdependent and nested. It is crucial to ensure that the user-supplied parameters are correct and give hints to fix them otherwise. The entry-point of the parameter consistency checking is the following method: ```@docs Coluna.Algorithm.check_alg_parameters ``` Developer of an algorithm must implement the following methods: ```@docs Coluna.Algorithm.check_parameter ``` ### Units usage ```@docs Coluna.AlgoAPI.get_child_algorithms Coluna.AlgoAPI.get_units_usage ``` ## Run ```@docs Coluna.AlgoAPI.run! ```	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	13232	```@meta CurrentModule = Coluna ``` # [Benders cut generation](@id api_benders) Coluna provides an interface and generic functions to implement a Benders cut generation algorithm. In this section, we are first going to present the generic functions, the implementation with some theory backgrounds and then give the references of the interface. The default implementation is based on the paper of You can find the generic functions and the interface in the `Benders` submodule and the default implementation in the `Algorithm` submodule at `src/Algorithm/benders`. ## Context The `Benders` submodule provides an interface and generic functions to implement a benders cut generation algorithm. The implementation depends on an object called `context`. ```@docs Coluna.Benders.AbstractBendersContext ``` Benders provides two types of context: ```@docs Coluna.Algorithm.BendersContext Coluna.Algorithm.BendersPrinterContext ``` ## Generic functions Generic functions are the core of the Benders cut generation algorithm. There are three generic functions: ```@docs Coluna.Benders.run_benders_loop! ``` See ... ```@docs Coluna.Benders.run_benders_iteration! ``` See ... These functions are independent of any other submodule of Coluna. You can use them to implement your own Benders cut generation algorithm. ## Reformulation The default implementation works with a reformulated problem contained in `MathProg.Reformulation` where master and subproblems are `MathProg.Formulation` objects. The master has the following form: ```math \begin{aligned} \min \quad& cx + \sum_{k \in K} \eta_k & &\\ \text{s.t.} \quad& Ax \geq a & & (1) \\ & \text{< benders cuts>} & & (2) \\ & l_1 \leq x \leq u_1 & & (3) \\ & \eta_k \in \mathbb{R} & \forall k \in K \quad& (4) \end{aligned} ``` where $x$ are first-stage variables, $\eta_k$ is the second-stage cost variable for the subproblem $k$, constraints $(1)$ are the first-stage constraints, constraints $(2)$ are the Benders cuts, constraints $(3)$ are the bounds on the first-stage variables, and expression $(4)$ shows that second-stage variables are free. The subproblems have the following form: ```math \begin{aligned} \min \quad& fy + {\color{gray} \mathbf{1}z' + \mathbf{1}z''} &&& \\ \text{s.t.} \quad& Dy {\color{gray} + z'} \geq d - B\bar{x} && (5) \quad& {\color{blue}(\pi)} \\ & Ey {\color{gray} + z''} \geq e && (6) \quad& {\color{blue}(\rho)} \\ & l_2 \leq y \leq u_2 && (7) \quad& {\color{blue}(\sigma)} \end{aligned} ``` where $y$ are second-stage variables, $z'$ and $z''$ are artificial variables (in grey because they are deactivated by default), constraints (5) are the reformulation of linking constraints using the first-stage solution $\bar{x}$, constraints (6) are the second-stage constraints, and constraints (7) are the bounds on the second-stage variables. In blue, we define the dual variables associated to these constraints. References: ```@docs Coluna.Benders.is_minimization Coluna.Benders.get_reform Coluna.Benders.get_master Coluna.Benders.get_benders_subprobs ``` ## Main loop This is a description of how the `Coluna.Benders.run_benders_loop!` generic function behaves with the default implementation. The loop stops if one of the following conditions is met: - the master is infeasible - a separation subproblem is infeasible - the time limit is reached - the maximum number of iterations is reached - no new cut is generated The default implementation returns: ```@docs Coluna.Algorithm.BendersOutput ``` References: ```@docs Coluna.Benders.setup_reformulation! Coluna.Benders.stop_benders Coluna.Benders.after_benders_iteration Coluna.Benders.AbstractBendersOutput Coluna.Benders.benders_output_type Coluna.Benders.new_output ``` ## Benders cut generation iteration This is a description of how the `Coluna.Benders.run_benders_iteration!` generic function behaves with the default implementation. These are the main steps of a Benders cut generation iteration without stabilization. Click on the step to go to the corresponding section. ```mermaid flowchart TB; id1(Optimize master) id2(Treat unbounded master) id3(Setup separation subproblems) id4(Separation subproblem iterator) id5(Optimize separation subproblem) id6(Push cut into set) id9(Master is unbounded?) id10(Error) id7(Insert cuts) id11(Build primal solution) id8(Iteration output) id1 --unbounded--> id2 id2 --certificate--> id3 id1 -- optimal --> id3 id3 --> id4 id4 -- subproblem --> id5 id5 --> id6 id6 --> id4 id4 -- end --> id9 id9 -- yes --> id10 id9 -- no --> id7 id7 --> id11 id11 --> id8 click id1 href "#Master-optimization" "Link to doc" click id2 href "#Unbounded-master-case" "Link to doc" click id3 href "#Setup-separation-subproblems" "Link to doc" click id4 href "#Subproblem-iterator" "Link to doc" click id5 href "#Separation-subproblem-optimization" "Link to doc" click id6 href "#Set-of-generated-cuts" "Link to doc" click id9 href "#Unboundedness-check" "Link to doc" click id11 href "#Current-primal-solution" "Link to doc" click id7 href "#Cuts-insertion" "Link to doc" click id8 href "#Iteration-output" "Link to doc" ``` In the default implementation, some sections may have different behaviors depending on the result of previous steps. ### Master optimization This operation consists in optimizing the master problem in order to find a first-level solution $\bar{x}$. In the default implementation, master optimization can be performed using `SolveLpForm` (LP solver) or `SolveIpForm` (MILP solver). When getting the solution, we store the current value of second stage variables $\bar{\eta}_k$ as incumbent value (see `Coluna.MathProg.getcurincval`). It returns an object of the following type: ```@docs Coluna.Algorithm.BendersMasterResult ``` References: ```@docs Coluna.Benders.optimize_master_problem! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Unbounded master case Second stage cost $\eta_k$ variables are free. As a consequence, the master problem is unbounded when there is no optimality Benders cuts. In this case, `Coluna.Benders.treat_unbounded_master_problem_case!` is called. The main goal of the default implementation of this method is to get the dual infeasibility certificate of the master problem. If the master has been solved with a MIP solver at the previous step, we need to relax the integrality constraints to get a dual infeasibility certificate. If the solver does not provide a dual infeasibility certificate, the implementation has an "emergency" routine to provide a first-stage feasible solution by solving the master LP with cost of second stage variables set to zero. We recommend using a solver that provides a dual infeasibility certificate and avoiding the "emergency" routine. References: ```@docs Coluna.Benders.treat_unbounded_master_problem_case! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Setup separation subproblems !!! info The separation subproblems differs depending on whether the restricted master is unbounded or not: - if the restricted master is optimal, the generic function calls `Coluna.Benders.update_sp_rhs!` - if the restricted master is unbounded, the generic function calls `Coluna.Benders.setup_separation_for_unbounded_master_case!` Default implementation of `Coluna.Benders.update_sp_rhs!` updates the right-hand side of the linking constraints (5). Reference: ```@docs Coluna.Benders.update_sp_rhs! ``` Default implementation of `Coluna.Benders.setup_separation_for_unbounded_master_case!` gives rise to the formulation proposed in Lemma 2 of Bonami et al: ```math \begin{aligned} (SepB) \equiv \min \quad& fy + {\color{gray} \mathbf{1}z' + \mathbf{1}z''} &&& \\ \text{s.t.} \quad& Dy {\color{gray} + z'} \geq -B\bar{x} && (5a) \quad& {\color{blue}(\pi)} \\ & Ey {\color{gray} + z''} \geq 0 && (6a) \quad& {\color{blue}(\rho)} \\ & y \geq 0 && (7a) \quad& {\color{blue}(\sigma)} \end{aligned} ``` where $y$ are second-stage variables, $z'$ and $z''$ are artificial variables (in grey because they are deactivated by default), and $\bar{x}$ is an unbounded ray of the restricted master. Reference: ```@docs Coluna.Benders.setup_separation_for_unbounded_master_case! ``` ### Subproblem iterator Not implemented yet. ### Separation subproblem optimization The default implementation first optimize the subproblem without the artificial variables $z'$ and $z''$. In the case where it finds $(\bar{\pi}, \bar{\rho}, \bar{\sigma})$ an optimal dual solution to the subproblem, the following cut is generated: ```math \eta_k + \bar{\pi}Bx \geq d\bar{\pi} + \bar{\rho}e + \bar{\sigma_{\leq}} l_2 + \bar{\sigma_{\geq}} u_2 ``` with $\bar{\sigma_{\leq}} l_2$ (respectively $\bar{\sigma_{\geq}} u_2$) the dual of the left part (respectively the right part) of constraint $l_2 \leq y \leq u_2$ of the subproblem. In the case where it finds the subproblem infeasible, it calls `Coluna.Benders.treat_infeasible_separation_problem_case!`. The default implementation of this method activates the artificial variables $z'$ and $z''$, sets the cost of second stage variables to 0, and optimizes the subproblem again. If a solution with no artificial variables is found, the following cut is generated: ```math \bar{\pi}Bx \geq d\bar{\pi} + \bar{\rho}e + \bar{\sigma_{\leq}} l_2 + \bar{\sigma_{\geq}} u_2 ``` Both methods return an object of the following type: ```@docs Coluna.Algorithm.BendersSeparationResult ``` References: ```@docs Coluna.Benders.optimize_separation_problem! Coluna.Benders.treat_infeasible_separation_problem_case! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Set of generated cuts You can define your data structure to manage the cuts generated at a given iteration. Columns are inserted after the optimization of all the separation subproblems to allow the parallelization of the latter. In the default implementation, cuts are represented by the following data structure: ```@docs Coluna.Algorithm.GeneratedCut ``` We use the following data structures to store the cuts and the primal solutions to the subproblems: ```@docs Coluna.Algorithm.CutsSet Coluna.Algorithm.SepSolSet ``` The default implementation of `push_in_set!` has the responsibility to check if the cut is violated. Given $\bar{\eta}_k$ solution to the restricted master and $\bar{y}$ solution to the separation problem, the cut is considered as violated when: - the separation subproblem was infeasible - or $\bar{\eta}_k \geq f\bar{y}$ References: ```@docs Coluna.Benders.set_of_cuts Coluna.Benders.set_of_sep_sols Coluna.Benders.push_in_set! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Unboundedness check !!! info This check is performed only when the restricted master is unbounded. To perform this check, we need a solution to each separation problem. Let $(\bar{\eta}_k)_{k \in K}$ be the value of second stage variables in the dual infeasibility certificate of the restricted master. Let $\bar{y}$ be an optimal solution to the separation problem (SepB). As indicated by Bonami et al., if $f\bar{y} \leq \sum\limits_{k \in K} \bar{\eta}_k$, then the original problem is unbounded (by definition of an unbounded ray of the original problem). References: ```@docs Coluna.Benders.master_is_unbounded ``` ### Cuts insertion The default implementation inserts into the master all the cuts stored in the `CutsSet` object. Reference: ```@docs Coluna.Benders.insert_cuts! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Current primal solution Lorem ipsum. References: ```@docs Coluna.Benders.build_primal_solution ``` ### Iteration output ```@docs Coluna.Algorithm.BendersIterationOutput ``` References: ```@docs Coluna.Benders.AbstractBendersIterationOutput Coluna.Benders.benders_iteration_output_type Coluna.Benders.new_iteration_output ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Getters for Result data structures \| Method name \| Master \| Separation \| \| ---------------- \| ------ \| ---------- \| \| `is_unbounded` \| X \| X \| \| `is_infeasible` \| X \| X \| \| `is_certificate` \| X \| \| \| `get_primal_sol` \| X \| X \| \| `get_dual_sol` \| X \| \| \| `get_obj_val` \| X \| X \| ```@docs Coluna.Benders.is_unbounded Coluna.Benders.is_infeasible Coluna.Benders.is_certificate Coluna.Benders.get_primal_sol Coluna.Benders.get_dual_sol Coluna.Benders.get_obj_val ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ## Stabilization Not implemented yet.	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	4462	```@meta CurrentModule = Coluna ``` # Branching API Coluna provides default implementations for the branching algorithm and the strong branching algorithms. Both implementations are built on top of an API that we describe here. ## Candidates selection Candidates selection is the first step (and sometimes the only step) of any branching algorithm. It chooses what are the possible branching constraints that will generate the children of the current node of the branch-and-bound tree. Coluna provides the following function for this step: ```@docs Branching.select! ``` It works as follows. The user chooses one or several branching rules that indicate the type of branching he wants to perform. This may be on a single variable or on a linear expression of variables for instance. The branching rule must implement `apply_branching_rule` that generates the candidates. The latter are the variables or expressions on which the branch-and-bound may branch with additional information that is requested by Coluna's branching implementation through the API. Then, candidates are sorted according to a selection criterion (e.g. most fractional). The algorithm keeps a certain number of candidates (one for classic branching, and several for strong branching). It generates the children of each candidate kept. At last, it returns the candidates kept. ### Branching rule ```@docs Branching.AbstractBranchingRule Branching.apply_branching_rule ``` ### Candidate ```@docs Branching.AbstractBranchingCandidate Branching.getdescription Branching.get_lhs Branching.get_local_id Branching.generate_children! ``` ### Selection criterion ```@docs Branching.AbstractSelectionCriterion Branching.select_candidates! ``` ### Branching API ```@docs Branching.get_selection_nb_candidates Branching.branching_context_type Branching.new_context Branching.get_int_tol Branching.get_rules Branching.get_selection_criterion ``` Method `advanced_select!` is part of the API but presented just below. ## Advanced candidates selection If the candidates' selection returns several candidates will all their children, advanced candidates selection must keep only one of them. The advanced candidates' selection is the place to evaluate the children to get relevant additional key performance indicators about each branching candidate. Coluna provides the following function for this step. ```@docs Branching.advanced_select! ``` Coluna has two default implementations for this method: - for the classic branching that does nothing because the candidates selection returns 1 candidate - for the strong branching that performs several evaluations of the candidates. Let us focus on the strong branching. Strong branching is a procedure that heuristically selects a branching constraint that potentially gives the best progress of the dual bound. The procedure selects a collection of branching candidates based on their branching rule (done in classic candidate selection) and their score (done in advanced candidate selection). Then, the procedure evaluates the progress of the dual bound in both branches of each branching candidate by solving both potential children using a conquer algorithm. The candidate that has the largest score is chosen to be the branching constraint. However, the score can be difficult to compute. For instance, when the score is based on dual bound improvement produced by the branching constraint which is time-consuming to evaluate in the context of column generation Therefore, one can let the branching algorithm quickly estimate the score of each candidate and retain the most promising branching candidates. This is called a phase. The goal is to first evaluate a large number of candidates with a very fast conquer algorithm and retain a certain number of promising ones. Then, over the phases, it evaluates the improvement with a more precise conquer algorithm and restricts the number of retained candidates until only one is left. ### Strong Branching API ```@docs Branching.get_units_to_restore_for_conquer Branching.get_phases Branching.get_score Branching.get_conquer Branching.get_max_nb_candidates ``` The following methods are part of the API but have a default implementation. We advise you to not change them. ```@docs Branching.perform_branching_phase! Branching.eval_candidate! Branching.eval_child_of_candidate! ``` #### Score ```@docs Branching.AbstractBranchingScore Branching.compute_score ```	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	22722	```@meta CurrentModule = Coluna ``` # Column generation Coluna provides an interface and generic functions to implement a multi-stage column generation algorithm together with a default implementation of this algorithm. In this section, we are first going to present the generic functions, the implementation with some theory backgrounds and then give the references of the interface. You can find the generic functions and the interface in the `ColGen` submodule and the default implementation in the `Algorithm` submodule at `src/Algorithm/colgen`. ## Context The `ColGen` submodule provides an interface and generic functions to implement a column generation algorithm. The implementation depends on an object called `context`. ```@docs Coluna.ColGen.AbstractColGenContext ``` Coluna provides two types of context: ```@docs Coluna.Algorithm.ColGenContext Coluna.Algorithm.ColGenPrinterContext ``` ## Generic functions Generic functions are the core of the column generation algorithm. There are three generic functions: ```@docs Coluna.ColGen.run! ``` See the [main loop](#Main-loop) section for more details. ```@docs Coluna.ColGen.run_colgen_phase! ``` See the [phase loop](#Phase-loop) section for more details. ```@docs Coluna.ColGen.run_colgen_iteration! ``` See the [column generation iteration](#Column-generation-iteration) section for more details. They are independent of any other submodule of Coluna. You can use them to implement your own column generation algorithm. ## Reformulation The default implementation works with a reformulated problem contained in `MathProg.Reformulation` where master and subproblems are `MathProg.Formulation` objects. The master has the following form: ```math \begin{aligned} \min \quad& \sum_{k \in K} c^k \lambda^k+\bar{c} y & \\ \text{s.t.} \quad& \sum_{k \in K} A^k \lambda^k+\bar{A} y \geq a & (1)\\ & l_k \leq \mathbf{1} \lambda^k \leq u_k & (2) \\ & \bar{l} \leq y \leq \bar{u} & (3) \end{aligned} ``` where $\lambda$ are the master columns, $y$ are the pure master variables, constraints (1) are the linking constraints, constraints (2) are the convexity constraints that depend on $l_k$ and $u_k$ (e.g. the lower and upper multiplicity of the subproblem $k$ respectively), and constraints (3) are the bounds on the pure master variables. The subproblems have the following form: ```math \begin{aligned} \min \quad& cx + 0z \\ \text{s.t.} \quad& Bx \geq b \\ & 1 \leq z \leq 1 \end{aligned} ``` where $x$ are the subproblem variables, $z$ is a setup variable that always takes the value one in a solution to the subproblem. The coefficients of the columns in constraints (1) and (2) of the master are computed using representative variables of the subproblems. You can read this section (TODO Natacha) to understand how we map the subproblem solutions into master columns. References: ```@docs Coluna.ColGen.get_reform Coluna.ColGen.get_master Coluna.ColGen.get_pricing_subprobs Coluna.ColGen.is_minimization ``` ## Main loop This is a description of how the `Coluna.ColGen.run!` generic function behaves in the default implementation. The main loop stops when the `Coluna.ColGen.stop_colgen` method returns `true`. This is the case when one of the following conditions holds: - the master or a pricing subproblem is infeasible - the time limit is reached - the maximum number of iterations is reached Otherwise, the main loop runs until there is no more phase or stage to execute. The method returns: ```@docs Coluna.Algorithm.ColGenOutput ``` References: ```@docs Coluna.ColGen.stop_colgen Coluna.ColGen.setup_reformulation! Coluna.ColGen.setup_context! Coluna.ColGen.AbstractColGenOutput Coluna.ColGen.colgen_output_type Coluna.ColGen.new_output ``` ## Phase loop This is a description of how the `Coluna.ColGen.run_colgen_phase!` generic function behaves in the default implementation. This function is responsible for maintaining the incumbent dual bound and the incumbent master IP primal solution. The phase loop stops when the `Coluna.ColGen.stop_colgen_phase` method returns `true`. This is the case when one of the following conditions holds: - the maximum number of iterations is reached - the time limit is reached - the master is infeasible - the master is unbounded - a pricing subproblem is infeasible - a pricing subproblem is unbounded - there is no new column generated at the last iteration - there is a new constraint or valid inequality in the master - the incumbent dual bound and the primal master LP solution value converged The method returns: ```@docs Coluna.Algorithm.ColGenPhaseOutput ``` References: ```@docs Coluna.ColGen.stop_colgen_phase Coluna.ColGen.before_colgen_iteration Coluna.ColGen.after_colgen_iteration Coluna.ColGen.is_better_dual_bound ``` ### Phase iterator In the first iterations, the restricted master LP contains a few columns and may be infeasible. To prevent this, we introduced artificial variables $v$ and we activate/deactivate these variables depending on whether we want to prove the infeasibility of the master LP or find the optimal LP solution. The default implementation provides three phases: ```@docs Coluna.Algorithm.ColGenPhase0 Coluna.Algorithm.ColGenPhase1 Coluna.Algorithm.ColGenPhase2 ``` Column generation always starts with Phase 0. The default implementation of the phase iterator belongs to the following type: ```@docs Coluna.Algorithm.ColunaColGenPhaseIterator ``` Transitions between the phases depend on four conditions: - (A) the presence of artificial variables in the master LP solution - (B) the generation of new essential constraints (may happen when a new master IP solution is found) - (C) the current stage is exact - (D) column generation converged Transitions are the following: ```mermaid flowchart TB; id1(Phase 0) id2(Phase 1) id3(Phase 2) id4(end) id5(error) id1 --A & !B & C--> id2 id1 --!A & !B & C & D--> id4 id1 -- otherwise --> id1 id2 --!A & !B--> id3 id2 --A & C & D--> id4 id2 -- otherwise --> id2 id3 -- !B & C & D --> id4 id3 -- otherwise --> id3 id3 -- B --> id2 id3 -- A --> id5 style id5 stroke:#f66 ``` References: ```@docs Coluna.ColGen.AbstractColGenPhase Coluna.ColGen.AbstractColGenPhaseIterator Coluna.ColGen.new_phase_iterator Coluna.ColGen.initial_phase Coluna.ColGen.decrease_stage Coluna.ColGen.next_phase ``` ### Phase output ```@docs Coluna.ColGen.AbstractColGenPhaseOutput Coluna.ColGen.colgen_phase_output_type Coluna.ColGen.new_phase_output ``` ## Stages A stage is a set of consecutive iterations in which we use a given pricing solver. The aim is to speed up the resolution of the pricing problem by first using an approximate but fast pricing algorithm and then switching to increasingly less heuristic algorithms until the last stage where an exact solver is used. and an exact solver at the last stage. Given a pricing solver, when the column generation does not progress anymore or the pricing solver does not return any new column, the default implementation switch to a more exact pricing solver. Stages are created using the `stages_pricing_solver_ids` of the `ColumnGenerationAlgorithm` parameter object. The default implementation implements the interface around the following object: ```@docs Coluna.Algorithm.ColGenStageIterator ``` References: ```@docs Coluna.ColGen.AbstractColGenStage Coluna.ColGen.AbstractColGenStageIterator Coluna.ColGen.new_stage_iterator Coluna.ColGen.initial_stage Coluna.ColGen.next_stage Coluna.ColGen.get_pricing_subprob_optimizer Coluna.ColGen.stage_id Coluna.ColGen.is_exact_stage ``` ## Column generation iteration This is a description of how the `Coluna.ColGen.run_colgen_iteration!` generic function behaves in the default implementation. These are the main steps of a column generation iteration without stabilization. Click on the step to go to the relevant section. ```mermaid flowchart TB; id1(Optimize master LP) id2{{Solution to master LP is integer?}} id3(Update incumbent primal solution if better than current one) id4(Compute reduced cost of subproblem variables) id5{{Subproblem iterator}} id6(Optimize pricing subproblem) id7(Push subproblem solution into set) id8(Compute dual bound) id9(Insert columns) id10(Iteration output) id1 --> id2 id2 --yes--> id3 id2 --no--> id4 id3 --> id4 id4 --> id5 id5 --subproblem--> id6 id6 --> id7 id7 --> id5 id5 --end--> id8 id8 --> id9 id9 --> id10 click id1 href "#Optimize-master-LP" "Link to doc" click id2 href "#Check-integrality-of-the-master-LP-solution" "Link to doc" click id3 href "#Update-incumbent-primal-solution" "Link to doc" click id4 href "#Reduced-costs-calculation" "Link to doc" click id5 href "#Pricing-subproblem-iterator" "Link to doc" click id6 href "#Pricing-subproblem-optimization" "Link to doc" click id7 href "#Set-of-generated-columns" "Link to doc" click id8 href "#Dual-bound-calculation" "Link to doc" click id9 href "#Columns-insertion" "Link to doc" click id10 href "#Iteration-output" "Link to doc" ``` #### Optimize master LP At each iteration, the algorithm requires a dual solution to the master LP to compute the reduced cost of subproblem variables. The default implementation optimizes the master with an LP solver through MathOptInterface. It returns a primal and a dual solution. In the default implementation, the master LP output is in the following data structure: ```@docs Coluna.Algorithm.ColGenMasterResult ``` References: ```@docs Coluna.ColGen.optimize_master_lp_problem! ``` You can see the additional methods to implement in the [result data structures](#Result-data-structures) section. Go back to the [column generation iteration overview](#Column-generation-iteration). #### Check the integrality of the master LP solution The algorithm checks the integrality of the primal solution to the master LP to improve the global primal bound of the branch-cut-price algorithm. By default, the integrality check is done using the `MathProg.proj_cols_is_integer` method. It implements the mapping procedure from the paper "F. Vanderbeck, Branching in branch-and-price: a generic scheme, Math.Prog. (2011)". Basically, it sorts the column used in the master LP primal solution in lexicographic order. It assigns a weight to each column equal to the value of the column in the master LP solution. It then forms columns of weight one by accumulating the columns of the fractional solution. If columns are integral, the solution is integral. This is a heuristic procedure so it can miss some integer solutions. In the case the pricing subproblems are solved by a callback, and some subproblem integer variables are "hidden" from _Coluna_ (values of these variables are usually stored in `CustomData` associated with the pricing problem solution), the mapping procedure may not be valid. In this case, the integrality should be checked in the "strict" way, i.e., by explicitly verifying that all columns are integer. Integrality check procedure is set using parameter `strict_integrality_check` (`false` by default) of the `ColumnGenerationAlgorithm`. If the solution is integral, the essential cut callback is called to make sure it is feasible. References: ```@docs Coluna.ColGen.check_primal_ip_feasibility! Coluna.ColGen.is_better_primal_sol ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Update incumbent primal solution If the solution to master LP is integral and better than the current best one, we need to update the incumbent. This solution is then used by the tree-search algorithm in the bounding mechanism that prunes the nodes. References: ```@docs Coluna.ColGen.update_inc_primal_sol! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Reduced costs calculation Reduced costs calculation is written as a math operation in the `run_colgen_iteration!` generic function. As a consequence, the dual solution to the master LP and the implementation of the two following methods must return data structures that support math operations. To speed up this operation, we cache data in the following data structure: ```@docs Coluna.Algorithm.ReducedCostsCalculationHelper ``` Reduced costs calculation also requires the implementation of the two following methods: ```@docs Coluna.ColGen.update_master_constrs_dual_vals! Coluna.ColGen.update_reduced_costs! Coluna.ColGen.get_subprob_var_orig_costs Coluna.ColGen.get_subprob_var_coef_matrix Coluna.ColGen.update_sp_vars_red_costs! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Pricing subproblem iterator The pricing strategy is basically an iterator used to iterate over the pricing subproblems to optimize at each iteration of the column generation. The context can serve as a memory of the pricing strategy to change the way we iterate over subproblems between each column generation iteration. The default implementation iterates over all subproblems. Here are the references for the interface: ```@docs Coluna.ColGen.AbstractPricingStrategy Coluna.ColGen.get_pricing_strategy Coluna.ColGen.pricing_strategy_iterate ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Pricing subproblem optimization At each iteration, the algorithm requires primal solutions to the pricing subproblems. The generic function supports multi-column generation so you can return any number of solutions. The default implementation supports optimization of the pricing subproblems using a MILP solver or a pricing callback. Non-robust valid inequalities are not supported by MILP solvers as they change the structure of the subproblems. When using a pricing callback, you must be aware of how Coluna calculates the reduced cost of a column: The reduced cost of a column is split into three contributions: - the contribution of the subproblem variables that is the primal solution cost given the reduced cost of subproblem variables - the contribution of the non-robust constraints (i.e. master constraints that cannot be expressed using subproblem variables except the convexity constraint) that is not supported by MILP solver but that you must take into account in the pricing callback - the contribution of the master convexity constraint that is automatically taken into account by Coluna once the primal solution is returned. Therefore, when you use a pricing callback, you must not discard some columns based only on the primal solution cost because you don't know the contribution of the convexity constraint. ```@docs Coluna.Algorithm.GeneratedColumn Coluna.Algorithm.ColGenPricingResult ``` References: ```@docs Coluna.ColGen.optimize_pricing_problem! ``` You can see the additional methods to implement in the [result data structures](#Result-data-structures) section. Go back to the [column generation iteration overview](#Column-generation-iteration). #### Set of generated columns You can define your data structure to manage the columns generated at a given iteration. Columns are inserted after the optimization of all pricing subproblems to allow the parallelization of the latter. In the default implementation, we use the following data structure: ```@docs Coluna.Algorithm.ColumnsSet Coluna.Algorithm.SubprobPrimalSolsSet ``` In the default implementation, `push_in_set!` is responsible for checking if the column has improving reduced cost. Only columns with improving reduced cost are inserted in the set. The `push_in_set!` is also responsible to insert he best primal solution to each pricing problem into the `SubprobPrimalSolsSet` object. References: ```@docs Coluna.ColGen.set_of_columns Coluna.ColGen.push_in_set! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Dual bound calculation In the default implementation, given a vector $\pi \geq 0$ of dual values to the master constraints (1), the Lagrangian dual function is given by: ```math L(\pi) = \pi a + \sum_{k \in K} \max_{l_k \leq \mathbf{1} \lambda^k \leq u^k} (c^k - \pi A^k)\lambda^k + \max_{ \bar{l} \leq y \leq \bar{u}} (\bar{c} - \pi \bar{A})y ``` Let: - element $z_k(\pi) \leq \min_i (c^k_i - \pi A^k_i)$ be a lower bound on the solution value of the pricing problem - element $\bar{z}_j(\pi) = \bar{c} - \pi \bar{A}$ be the reduced cost of pure master variable $y_j$ Then, the Lagrangian dual function can be lower bounded by: ```math L(\pi) \geq \pi a + \sum_{k \in K} \max\{ z_k(\pi) \cdot l_k, z_k(\pi) \cdot u_k \} + \sum_{j \in J} \max\{ \bar{z}_j(\pi) \cdot \bar{l}_j, \bar{z}_j(\pi) \cdot \bar{u}_j\} ``` More precisely: - the first term is the contribution of the master obtained by removing the contribution of the convexity constraints (computed by `ColGen.Algorithm._convexity_contrib`), and the pure master variables (but you should see the third term) from the master LP solution value - the second term is the contribution of the subproblem variables which is the sum of the best solution value of each pricing subproblem multiplied by the lower and upper multiplicity of the subproblem depending on whether the reduced cost is negative or positive (this is computed by `ColGen.Algorithm._subprob_contrib`) - the third term is the contribution of the pure master variables which is taken into account by master LP value. Therefore, we can compute the Lagrangian dual bound as follows: ```julia master_lp_obj_val - convexity_contrib + sp_contrib ``` However, if the smoothing stabilization is active, we compute the dual bound at the sep-point. As a consequence, we can't use the master LP value because it corresponds to the dual solution at the out-point. We therefore need to compute the lagrangian dual bound by strictly applying the above formula. References: ```@docs Coluna.ColGen.compute_sp_init_pb Coluna.ColGen.compute_sp_init_db Coluna.ColGen.compute_dual_bound ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Columns insertion The default implementation inserts into the master all the columns stored in the `ColumnsSet` object. Reference: ```@docs Coluna.ColGen.insert_columns! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Iteration output ```@docs Coluna.Algorithm.ColGenIterationOutput ``` References: ```@docs Coluna.ColGen.AbstractColGenIterationOutput Coluna.ColGen.colgen_iteration_output_type Coluna.ColGen.new_iteration_output ``` Go back to the [column generation iteration overview](#Column-generation-iteration). ### Getters for Result data structures \| Method name \| Master \| Pricing \| \| ---------------- \| ------ \| ---------- \| \| `is_unbounded` \| X \| X \| \| `is_infeasible` \| X \| X \| \| `get_primal_sol` \| X \| \| \| `get_primal_sols`\| \| X \| \| `get_dual_sol` \| X \| \| \| `get_obj_val` \| X \| \| \| `get_primal_bound` \| \| X \| \| `get_dual_bound` \| \| X \| References: ```@docs Coluna.ColGen.is_unbounded Coluna.ColGen.is_infeasible Coluna.ColGen.get_primal_sol Coluna.ColGen.get_primal_sols Coluna.ColGen.get_dual_sol Coluna.ColGen.get_obj_val Coluna.ColGen.get_primal_bound ``` Go back to the [column generation iteration overview](#Column-generation-iteration). ### Getters for Output data structures \| Method name \| ColGen \| Phase \| Iteration \| \| ---------------- \| ------ \| ----- \| --------- \| \| `get_nb_new_cols` \| \| \| X \| \| `get_master_ip_primal_sol` \| X \| X \| X \| \| `get_master_lp_primal_sol` \| X \| \| \| \| `get_master_dual_sol` \| X \| \| \| \| `get_dual_bound` \| X \| \| X \| \| `get_master_lp_primal_bound` \| X \| \| \| \| `is_infeasible` \| X \| \| \| References: ```@docs Coluna.ColGen.get_nb_new_cols Coluna.ColGen.get_master_ip_primal_sol Coluna.ColGen.get_master_lp_primal_sol Coluna.ColGen.get_master_dual_sol Coluna.ColGen.get_master_lp_primal_bound ``` Go back to the [column generation iteration overview](#Column-generation-iteration). ## Stabilization Coluna provides a default implementation of the smoothing stabilization with a self-adjusted $\alpha$ parameter, $0 \leq \alpha < 1$. At each iteration of the column generation algorithm, instead of generating columns for the dual solution to the master LP, we generate columns for a perturbed dual solution defined as follows: ```math \pi^{\text{sep}} = \alpha \pi^{\text{in}} + (1-\alpha) \pi^{\text{out}} ``` where $\pi^{\text{in}}$ is the dual solution that gives the best Lagrangian dual bound so far (also called stabilization center) and $\pi^{\text{out}}$ is the dual solution to the master LP at the current iteration. This solution is returned by the default implementation of `Coluna.ColGen.get_stab_dual_sol`. Some elements of the column generation change when using stabilization. - Columns are generated using the smoothed dual solution $\pi^{\text{sep}}$ but we still need to compute the reduced cost of the columns using the original dual solution $\pi^{\text{out}}$. - The dual bound is computed using the smoothed dual solution $\pi^{\text{sep}}$. - The pseudo bound is computed using the smoothed dual solution $\pi^{\text{sep}}$. - The smoothed dual bound can result in the generation of no improving columns. This is called a misprice. In that case, we need to move away from the stabilization center $\pi^{\text{in}}$ by decreasing $\alpha$. When using self-adjusted stabilization, the smoothing coefficient $\alpha$ is adjusted to make the smoothed dual solution $\pi^{\text{sep}}$ closer to the best possible dual solution on the line between $\pi^{\text{in}}$ and $\pi^{\text{out}}$ (i.e. where the subgradient of the current primal solution is perpendicular to the latter line). To compute the subgradient, we use the following data structure: ```@docs Coluna.Algorithm.SubgradientCalculationHelper ``` References: ```@docs Coluna.ColGen.setup_stabilization! Coluna.ColGen.update_stabilization_after_master_optim! Coluna.ColGen.get_stab_dual_sol Coluna.ColGen.check_misprice Coluna.ColGen.update_stabilization_after_pricing_optim! Coluna.ColGen.update_stabilization_after_misprice! Coluna.ColGen.update_stabilization_after_iter! Coluna.ColGen.get_output_str ```	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	5215	# Presolve Currently, the presolve algorithm supports only the Dantzig-Wolfe decomposition. The presolve algorithm operates on matrix representations of the formulation. It requires two representations of the master formulation: - the restricted master that contains master column variables, pure master variables and artificial variables; - the representative master that contains subproblem representative variables and pure master variables; and the representation of the pricing subproblems. The current presolve operations available are the following (taxonomy of Achterberg et al. 2016): - model cleanup & removal of redundant constraints - bound strengthening - removal of fixed variables ## Partial solution The presolve algorithm has the responsibility to define and fix a partial solution when it exists. When a variable $x$ has a value $\bar{x} > 0$ (resp. $\bar{x} < 0$) in the partial solution, it means that $x$ has a lower (resp. upper) bounds $\bar{x}$ that will definitely be part of the solution at the current branch-and-bound node and its successors. In other words, the partial solution describes a minimal distance of the variables from zero in the all the solutions to a problem at a given branch-and-bound node. It always restricts the domain of the variables (i.e. increase distance from zero). The only way to relax the domains is to backtrack to an ancestor of the current branch-and-bound node (i.e. go back to a previous partial solution). ### Augmenting the partial solution Consider a _local partial solution_ $(\bar{x}^{\rm pure}, \bar{\lambda})$ (where $\bar{x}^{\rm pure}$ is the vector of values for pure master variables, and $\bar{\lambda}$ is the vector of values for master columns), which should be added to the _global partial solution_ $(\bar{y}^{\rm pure}, \bar{\theta})$: 1. augment the global partial solution: $(\bar{y}^{\rm pure}, \bar{\theta})\leftarrow(\bar{x}^{\rm pure}+\bar{y}^{\rm pure}, \bar{\lambda}+\bar{\theta})$. 2. update the right-hand side values of the master constraints: ${\rm rhs}_i\leftarrow {\rm rhs}_i - {A}^{\rm pure}\cdot\bar{x}^{\rm pure} - {A}^{\rm col}\cdot\bar{ \lambda}$, where ${A}^{\rm pure}$ is the matrix of coefficients of pure master constraints and ${A}^{\rm col}$ is the matrix of coefficients of master columns. 3. update subproblem multiplicities $U_k\leftarrow U_k - \sum_{q\in Q_k}\bar\lambda_q$, and $L_k\leftarrow \max\left\{0,\; L_k - \sum_{q\in Q_k}\bar\lambda_q\right\}$, where $Q_k$ is the set of indices of columns associated with solutions from subproblem $k$. 4. update the bounds of pure master variables and representative master variables using the representative local partial solution: $\bar{x}^{\rm repr} = \sum_{q\in Q}{s_q}\cdot \bar\lambda_q$, where $Q$ is the total number of columns, and ${s_q}$ is the subproblem solution associated with column $\lambda_q$. ### Updating bounds of pure & representative master variables Consider a pure master variable $x^{\rm pure}_j$ with $\bar{x}^{\rm pure}_j\neq 0$ and bounds $[lb_j,ub_j]$ before augmenting the partial solution. If $\bar{x}^{\rm pure}_j > 0$, then we have $lb_j\leftarrow 0$, $ub_j\leftarrow ub_j - \bar{x}^{\rm pure}_j$. If $\bar{x}^{\rm pure}_j < 0$, then we have $lb_j\leftarrow lb_j - \bar{x}^{\rm pure}_j$, $ub_j\leftarrow 0$. Consider a representative master variable $x^{\rm repr}_j$ with bounds $[lb^g_j, ub^g_j]$ before augmenting the partial solution. Assume that $x^{\rm repr}_j$ represents exactly one variable $x^k_j$ in subproblem $k$ with bounds $[lb^l_j, ub^l_j]$ before augmenting the partial solution. _This assumption should be verified before augmenting the partial solution!_ For the clarity of presentation, we omit index $j$ for the remainder of this section. After augmenting the partial solution, the following inequalities should be satisfied: $$ lb^g - \bar{x}^{\rm repr}\leq x^{\rm repr} \leq ub^g - \bar{x}^{\rm repr}.$$ At the same time, we should have $$\min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\leq x^{\rm repr}\leq \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}$$ Thus, the following update should be done $$ lb^g\leftarrow \max\left\{lb^g - \bar{x}^{\rm repr},\; \min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\right\}$$ $$ ub^g\leftarrow \min\left\{ub^g - \bar{x}^{\rm repr},\; \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}\right\}$$ > _Example 1:_ $0\leq x^k\leq 3$, $0\leq x^{\rm repr}\leq 6$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=2$. Then after augmenting the partial solution, we have > $$ \max\left\{-2,\; 0\right\}\leq x^{\rm repr} \leq \min\left\{4,\; 3\right\} \Rightarrow 0 \leq x^{\rm repr} \leq 3$$ > _Example 2:_ $0\leq x^k\leq 5$, $3\leq x^{\rm repr}\leq 6$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=2$. Then after augmenting the partial solution, we have > $$ \max\left\{1,\; 0\right\}\leq x'_{\rm repr} \leq \min\left\{4,\; 5\right\} \Rightarrow 1 \leq x'_{\rm repr} \leq 4$$ > _Example 3:_ $-1\leq x^k\leq 4$, $-2\leq x^{\rm repr}\leq 2$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=-1$. Then after augmenting the partial solution, we have > $$ \max\left\{-1,\; -1\right\}\leq x^{\rm repr} \leq \min\left\{3,\; 4\right\} \Rightarrow -1 \leq x^{\rm repr} \leq 3$$	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	1168	```@meta EditURL = "<unknown>/src/api/storage.jl" ``` # Storage API ```@meta CurrentModule = Coluna ``` ## API To summarize from a developer's point of view, there is a one-to-one correspondence between storage unit types and record types. This correspondence is implemented by methods `record_type(StorageUnitType)` and `storage_unit_type(RecordType)`. The developer must also implement methods `storage_unit(StorageUnitType)` and `record(RecordType, id, model, storage_unit)` that must call constructors of the custom storage unit and one of its associated records. Arguments of `record` allow the developer to record the state of entities from both the storage unit and the model. At last, he must implement `restore_from_record!(storage_unit, model, record)` to restore the state of the entities represented by the storage unit. Entities can be in the storage unit, the model, or both of them. ```@docs ColunaBase.record_type ColunaBase.storage_unit_type ColunaBase.storage_unit ColunaBase.record ColunaBase.restore_from_record! ``` --- This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	3523	# Tree search API !!! danger Update needed. Now, we define the two concepts we'll use in the tree search algorithms: the node and the search space. The third concept is the explore strategy and implemented in Coluna. Every tree search algorithm must be associated to a search space. ## Implementing tree search interface First, we indicate the type of search space used by our algorithms. Note that the type of the search space can depends on the configuration of the algorithm. So there is a 1-to-n relation between tree search algorithm configurations and search space. because one search space can be used by several tree search algorithms configuration. Now, we implement the method that calls the constructor of a search space. The type of the search space is known from above method. A search space may receive information from the tree-search algorithm. The `model`, and `input` arguments are the same than those received by the tree search algorithm. We implement the method that returns the root node. The definition of the root node depends on the search space. Then, we implement the method that converts the branching rules into nodes for the tree search algorithm. We implement the `node_change` method to update the search space called by the tree search algorithm just after it finishes to evaluate a node and chooses the next one. Be careful, this method is not called after the evaluation of a node when there is no more unevaluated nodes (i.e. tree exploration is finished). There are two ways to store the state of a formulation at a given node. We can distribute information across the nodes or store the whole state at each node. We follow the second way (so we don't need `previous`). Method `after_conquer` is a callback to do some operations after the conquer of a node and before the divide. Here, we update the best solution found after the conquer algorithm. We implement one method for each search space. We implement getters to retrieve the input from the search space and the node. The input is passed to the conquer and the divide algorithms. At last, we implement methods that will return the output of the tree search algorithms. We return the cost of the best solution found. We write one method for each search space. ## API ### Search space ```@docs Coluna.TreeSearch.AbstractSearchSpace Coluna.TreeSearch.search_space_type Coluna.TreeSearch.new_space ``` ### Node ```@docs Coluna.TreeSearch.AbstractNode Coluna.TreeSearch.new_root Coluna.TreeSearch.get_parent Coluna.TreeSearch.get_priority ``` Additional methods needed for Coluna's algorithms: ```@docs Coluna.TreeSearch.get_opt_state Coluna.TreeSearch.get_records Coluna.TreeSearch.get_branch_description Coluna.TreeSearch.isroot ``` ### Tree search algorithm ```@docs Coluna.TreeSearch.AbstractExploreStrategy Coluna.TreeSearch.tree_search Coluna.TreeSearch.children Coluna.TreeSearch.stop Coluna.TreeSearch.tree_search_output ``` ### Tree search algorithm for Coluna ```@docs Coluna.Algorithm.AbstractColunaSearchSpace ``` The `children` method has a specific implementation for `AbstractColunaSearchSpace`` that involves following methods: ```@docs Coluna.Algorithm.get_previous Coluna.Algorithm.set_previous! Coluna.Algorithm.node_change! Coluna.Algorithm.get_divide Coluna.Algorithm.get_reformulation Coluna.Algorithm.get_input Coluna.Algorithm.after_conquer! Coluna.Algorithm.new_children ``` --- This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	2483	```@meta CurrentModule = Coluna ``` # Built-in Algorithms ## Branch-and-Bound Branch-and-Bound algorithm aims to find an optimal solution of a MIP by successive divisions of the search space. An introduction to the Branch-and-Bound algorithm can be found [here](https://en.wikipedia.org/wiki/Branch_and_bound). Coluna provides a generic Branch-and-Bound algorithm whose three main elements can be easily modified: ```@docs Algorithm.TreeSearchAlgorithm ``` Conquer, divide algorithms and the explore strategy available with the `TreeSearchAlgorithm` are listed in the following mind map. ```mermaid mindmap TreeSearchAlgorithm (conquer) BendersConquer ColCutGenConquer RestrMasterLpConquer (divide) NoBranching ClassicBranching StrongBranching (explore) DepthFirstStrategy BestDualBoundStrategy ``` ## Conquer algorithms ```@docs Algorithm.BendersConquer Algorithm.ColCutGenConquer Algorithm.RestrMasterLpConquer ``` ## Divide algorithms ```@docs Algorithm.NoBranching Algorithm.ClassicBranching ``` Strong branching is the main algorithm that we provide and it is the default implementation of the `Branching` submodule. You can have more information about the algorithm by reading the `Branching` submodule documentation. ```@docs Algorithm.StrongBranching ``` All the possible algorithms that can be used within the strong branching are listed in the following mind map. ```mermaid mindmap StrongBranching (phases) (conquer) BendersConquer ColCutGenConquer RestrMasterLpConquer (score) ProductScore TreeDepthScore (rules) SingleVarBranchingRule (selection_criterion) FirstFoundCriterion MostFractionalCriterion ``` ## Explore strategies ```@docs TreeSearch.DepthFirstStrategy TreeSearch.BestDualBoundStrategy ``` ## Cut generation algorithms ```@docs Algorithm.BendersCutGeneration ``` ```@docs Algorithm.CutCallbacks ``` ## Column generation algorithms ```@docs Algorithm.ColumnGeneration ``` ## External call to optimize a linear program ```@docs Algorithm.SolveLpForm ``` ## External call to optimize a mixed-integer program / combinatorial problem ```@docs Algorithm.SolveIpForm Algorithm.MoiOptimize Algorithm.UserOptimize Algorithm.CustomOptimize ```	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	1763	# Setup decomposition with BlockDecomposition BlockDecomposition allows the user to perform two types of decomposition using [`BlockDecomposition.@dantzig_wolfe_decomposition`](@ref) and [`BlockDecomposition.@benders_decomposition`](@ref). For both decompositions, the index-set of the subproblems is declared through an [`BlockDecomposition.@axis`](@ref). It returns an array. Each value of the array is a subproblem index wrapped into a `BlockDecomposition.AxisId`. Each time BlockDecomposition finds an `AxisId` in the indices of a variable and a constraint, it knows to which subproblem the variable or the constraint belongs. The macro creates a decomposition tree where the root is the master and the depth is the number of nested decompositions. A classic Dantzig-Wolfe or Benders decomposition produces a decomposition tree of depth 1. At the moment, nested decomposition is not supported. You can get the subproblem membership of all variables and constraints using the method [`BlockDecomposition.annotation`](@ref). BlockDecomposition does not change the JuMP model. It decorates the model with additional information. All this information is stored in the `ext` field of the JuMP model. ```@meta CurrentModule = BlockDecomposition ``` ## Errors and warnings ```@docs MasterVarInDwSp VarsOfSameDwSpInMaster ``` ## References ```@docs BlockModel ``` These are the methods to decompose a JuMP model : ```@docs @axis @benders_decomposition @dantzig_wolfe_decomposition ``` These are the methods to set additional information to the decomposition (multiplicity and optimizers) : ```@docs getmaster getsubproblems specify! ``` This method helps you to check your decomposition : ```@docs annotation ``` ```@meta CurrentModule = nothing ```	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	2269	# User-defined Callbacks Callbacks are functions defined by the user that allow him to take over part of the default conquer algorithm. The more classical callbacks in Branch-and-Cut and Branch-and-Price solvers are: - Pricing callback (only in Branch-and-Price solvers) that takes over the procedure to determine whether the current master LP solution is optimum or produces an entering variable with negative reduced cost by solving subproblems - Separation callback that takes over the procedure to determine whether the current master LP solution is feasible or produces a valid problem constraint that is violated - Branching callback that takes over the procedure to determine whether the current master LP solution is integer or produces a valid branching disjunctive constraint that rules out the current fractional solution. !!! note You can't change the original formulation in a callback because Coluna does not propagate the changes into the reformulation and does not check if the solutions found are still feasible. ## Pricing callbacks Pricing callbacks let you define how to solve the subproblems of a Dantzig-Wolfe decomposition to generate a new entering column in the master program. This callback is useful when you know an efficient algorithm to solve the subproblems, i.e. an algorithm better than solving the subproblem with a MIP solver. See the example in the [tutorial section](@ref tuto_pricing_callback). ### Errors and Warnings ```@docs Algorithm.IncorrectPricingDualBound Algorithm.MissingPricingDualBound Algorithm.MultiplePricingDualBounds ``` ## Separation callbacks Separation callbacks let you define how to separate cuts or constraints. ### Facultative & essential cuts (user cut & lazy constraint) This callback allows you to add cuts to the master problem. The cuts must be expressed in terms of the original variables. Then, Coluna expresses them over the master variables. You can find an example of [essential cut separation](https://jump.dev/JuMP.jl/stable/tutorials/Mixed-integer%20linear%20programs/callbacks/#Lazy-constraints) and [facultative cut separation](https://jump.dev/JuMP.jl/stable/tutorials/Mixed-integer%20linear%20programs/callbacks/#User-cut) in the JuMP documentation.	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	430	# Coluna Configuration todo ## Raw Parameters todo ### `params` ```@meta CurrentModule = Coluna ``` ```@docs Params ``` ```@meta CurrentModule = nothing ``` ### `default_optimizer` todo ## Other Supported Parameters ### From BlockDecomposition ```@meta CurrentModule = BlockDecomposition ``` ```@docs objectiveprimalbound! objectivedualbound! ``` ```@meta CurrentModule = nothing ``` ### From MathOptInterface todo	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	8369	# Dantzig-Wolfe and Benders decompositions Coluna is a framework to optimize mixed-integer programs that you can decompose. In other words, if you remove the linking constraints or linking variables from your program, you'll get sets of constraints (blocks) that you can solve independently. Decompositions are typically used on programs whose constraints or variables can be divided into a set of "easy" constraints (respectively easy variables) and a set of "hard" constraints (respectively hard variables). Decomposing on constraints leads to Dantzig-Wolfe transformation while decomposing on variables leads to the Benders transformation. Both of these decompositions are implemented in Coluna. ## Dantzig-Wolfe ### Original formulation Let's consider the following coefficient matrix that has a block diagonal structure in gray and some linking constraints in blue : ![Dantzig-Wolfe decomposition](../assets/img/dwdec.png) You penalize the violation of the linking constraints in the objective function. You can then solve the blocks independently. The Dantzig-Wolfe reformulation gives rise to a master problem with an exponential number of variables. Coluna dynamically generates these variables by solving the subproblems. It's the column generation algorithm. Let's consider the following original formulation in which we partition variables into two vectors $x_1$ and $x_2$ : ```math \begin{aligned} \min \quad& c_1' x_1 + c_2' x_2 & \\ \text{s.t.} \quad& A_1 x_1 + A_2 x_2 \geq b & (1)\\ & D_1 x_1 \quad \quad \quad \geq d_1 & (2) \\ & \quad \quad \quad D_2 x_2 \geq d_2 & (3) \\ \end{aligned} ``` - variables $x_1$ and $x_2$ are the original variables of the problem (duty: `OriginalVar`) - constraints $(1)$ are the linking constraints (duty: `OriginalConstr`) - constraints $(2)$ shapes the first subproblem (duty: `OriginalConstr`) - constraints $(3)$ shapes the second subproblem (duty: `OriginalConstr`) ### Master When you apply a Dantzig-Wofe decomposition to this formulation, Coluna reformulates it into the following master problem : ```math \begin{aligned} \min \quad& \sum\limits_{q \in Q_1} c_1' \tilde{x_1}^q \lambda_q + \sum\limits_{q \in Q_2} c_2' \tilde{x_2}^q \lambda_q + f'a \\ \text{s.t.} \quad& \sum\limits_{q \in Q_1} A_1 \tilde{x_1}^q \lambda_q + \sum\limits_{q \in Q_2} A_2 \tilde{x_2}^q \lambda_q + a \geq b & (1)\\ & L_1 \leq \sum\limits_{q \in Q_1} \tilde{z}_1\lambda_q \leq U_1 & (2)\\ & L_2 \leq \sum\limits_{q \in Q_2} \tilde{z}_2\lambda_q \leq U_2 & (3)\\ & \lambda_q \geq 0, \quad q \in Q_1 \cup Q_2 \end{aligned} ``` where: - set $Q_1$ is the index set of the solutions to the first subproblem - set $Q_2$ is the index set of the solutions to the second subproblem - set of the solutions to the first is $\{\tilde{x}^q_1\}_{q \in Q_1}$ (duty: ` MasterRepPricingVar`) - set of the solutions to the second subproblem is $\{\tilde{x}^q_2\}_{q \in Q_2}$ respectively (duty: ` MasterRepPricingVar`) - constraint $(1)$ is the reformulation of the linking constraints (duty: `MasterMixedConstr`) - constraint $(2)$ is the convexity constraint of the first subproblem and involves the lower $L_1$ and upper $U_1$ multiplicity of the subproblem (duty: `MasterConvexityConstr`) - constraint $(3)$ is the convexity constraint of the second subproblem and involves the lower $L_2$ and upper $U_2$ multiplicity of the subproblem (duty: `MasterConvexityConstr`) - variables $\tilde{z}_1$ and $\tilde{z}_2$ are representative of pricing setup variables in the master (always equal to $1$) (duty: `MasterRepPricingVar`) - variables $\lambda_q$ are the columns (duty: `MasterCol`) - variable $a$ is the artificial variable (duty: `MasterArtVar`) At the beginning of the column generation algorithm, the master formulation does not have any master columns. Therefore, the master may be infeasible. To prevent this, Coluna adds a local artificial variable $a$ specific to each constraint of the master and a global artificial variable. Costs $f$ of artificial and global artificial variables can be defined in [Coluna.Params](@ref). Lower and upper multiplicities of subproblems are $1$ by default. However, when some subproblems are identical (same coefficient matrix and right-hand side), you can avoid solving all of them at each iteration by defining only one subproblem and setting its multiplicity to the number of times it appears. See this [tutorial](@ref tuto_identical_sp) to get an example of Dantzig-Wolfe decomposition with identical subproblems. ### Pricing Subproblem Subproblems take the following form (here, it's the first subproblem): ```math \begin{aligned} \min \quad& \bar{c_1}' x_1 + z_1\\ \text{s.t.} \quad& D_1x_1 \geq d_1 & (1)\\ & \quad x_1 \geq 0 \end{aligned} ``` where: - vector $\bar{c}$ is the reduced cost of the subproblem variables computed by the column generation algorithm. - variables $x_1$ are the subproblem variables (duty: `DwSpPricingVar`) - constraint $(1)$ is the subproblem constraint (duty: `DwSpPureConstr`) - variable $z_1$ is the pricing setup variable (always equal to $1$) (duty: `DwSpSetupVar`) ## Benders ### Original formulation Let's consider the following coefficient matrix that has a block diagonal structure in gray and some linking variables in blue : ![Benders decomposition](../assets/img/bdec.png) The intuition behind Benders decomposition is that some hard problems can become much easier with some of their variables fixed. Benders aims to divide the variables of the problem into two "levels": the 1st level variables which, once fixed, make it easier to find a solution for the remaining variables, the so-called 2nd-level variables. The question is how to set the 1st level variables. Benders' theory proceeds by the successive generation of cuts: given a 1st-level solution, we ask the following questions: - Is the subproblem infeasible? If so, then the 1st-level solution is not correct and must be eliminated. A feasibility cut will be derived from the dual subproblem and added to the master. - Does the aggregation of the master and subproblem solutions give rise to an optimal solution to the problem? It depends on a criterion that can be computed. If it is the case, we are done, else, we derive an optimality cut from the dual subproblem and add it into the master. Formally, given an original MIP: ```math \begin{aligned} \min \quad& cx + fy & \\ \text{s.t.} \quad& Ax \geq a & (2) \\ & Ey \geq e & (3) \\ & Bx + Dy \geq d & (4)\\ & x, y \geq 0, ~ x \in \mathbb{Z}^n\\ \end{aligned} ``` where: - variables $x$ are the 1st-level variables (duty: `OriginalVar`) - variables $y$ are the 2nd-level variables (duty: `OriginalVar`) - constraints (2) are the 1st-level constraints (duty: `OriginalConstr`) - constraints (3) are the 2nd-level constraints (duty: `OriginalConstr`) - constraints (4) are the linking constraints (duty: `OriginalConstr`) ### Master When you apply a Benders decomposition to this formulation, Coluna reformulates it into the following master problem : ```math \begin{aligned} \min \quad& cx + \sum\limits_{k \in K}\eta_k & \\ \text{s.t.} \quad& Ax \geq a & (5)\\ & <~\text{benders cuts}~> & (6) \\ & \eta_k \in \mathbb{R} \quad \forall k \in K\\ \end{aligned} ``` where: - variables $x$ are the 1st-level variables (duty: `MasterBendFirstStageVar`) - variables $\eta$ are the second stage cost variables (duty: `MasterBendSecondStageCostVar`) - constraints (5) are the first-level constraints (duty: `MasterPureConstr`) - constraints (6) are the benders cuts (duty: ``) Note that the $\eta$ variables are free. ### Separation subproblem Here is the form of a given separation subproblem: ```math \begin{aligned} \min \quad& fy & \\ \text{s.t.} \quad& Dy \geq d - B\bar{x} & (7) \\ & Ey \geq e & (8) \\ & y \geq 0 \\ \end{aligned} ``` where: - variables $y$ are the 2nd-level variables (duty: `BendSpSepVar`) - values $\bar{x}$ are a solution to the master problem - constraints (7) are the linking constraints with the 1st-level variables fixed to $\bar{x}$ (duty: `BendSpTechnologicalConstr`) - constraints (8) are the 2nd-level constraints (duty: `BendSpPureConstr`) Note that in the special case where the master problem is unbounded, the shape of the subproblem is slightly modified. See the [API](@ref api_benders) section to get more information.	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	9087	# Presolve algorithm >### Ruslan Sadykov, 20/10/2023, revised 16/01/2024 The document presents the presolve algorithm implemented in _Coluna_. It is particularly important to run this algorithm after augmenting the partial solution in rounding and diving heuristics. ## 1. Augmenting the partial solution This is an optional step, which should be performed in the case a local partial solution is passed in the input of the preprocessing algorithm. Consider a _local partial solution_ $(\bar{\bm x}^{\rm pure}, \bar{\bm\lambda})$ (where $\bar{\bm x}^{\rm pure}$ is the vector of values for pure master variables, and $\bar{\bm\lambda}$ is the vector of values for master columns), which should be added to the _global partial solution_ $(\hat{\bm x}^{\rm pure}, \hat{\bm\lambda})$. First, we augment the global partial solution: $(\hat{\bm x}^{\rm pure}, \hat{\bm\lambda})\leftarrow(\hat{\bm x}^{\rm pure}+\bar{\bm x}^{\rm pure}, \hat{\bm\lambda}+\bar{\bm\lambda})$. Second, we update the right-hand side values of all master constraints (both robust and non-robust): ${\rm rhs}_i\leftarrow {\rm rhs}_i - {\bm A}^{\rm pure}\cdot\bar{\bm x}^{\rm pure} - {\bm A}^{\rm col}\cdot\bar{\bm \lambda}$, where ${\bm A}^{\rm pure}$ is the matrix of coefficients of pure master constraints and ${\bm A}^{\rm col}$ is the matrix of coefficients of master columns. Third, we update subproblem multiplicities $U_k\leftarrow U_k - \sum_{q\in Q_k}\bar\lambda_q$, and $L_k\leftarrow \max\left\{0,\; L_k - \sum_{q\in Q_k}\bar\lambda_q\right\}$, where $Q_k$ is the set of indices of columns associated with solutions from subproblem $k$. Afterwards, we should update the bounds of pure master variables and representative master variables. For that we first calculate the representative local partial solution: $\bar{\bm x}^{\rm repr} = \sum_{q\in Q}{\bm s}^q\cdot \bar\lambda_q$, where $Q$ is the total number of columns, and ${\bm s}^q$ is the subproblem solution associated with column $\lambda_q$. Update of bounds of variables can be performed one variable at a time. This is presented in the next two sections. ### Pure master variables Consider a pure master variable $x^{\rm pure}_j$ with $\bar{x}^{\rm pure}_j\neq 0$ and bounds $[lb_j,ub_j]$ before augmenting the partial solution. If $\bar{x}^{\rm pure}_j > 0$, then we have $lb_j\leftarrow \max\left\{0, lb_j - \bar{x}^{\rm pure}_j\right\}$, $ub_j\leftarrow ub_j - \bar{x}^{\rm pure}_j$. If $\bar{x}^{\rm pure}_j < 0$, then we have $lb_j\leftarrow lb_j - \bar{x}^{\rm pure}_j$, $ub_j\leftarrow \min\left\{0, ub_j - \bar{x}^{\rm pure}_j\right\}$. > _Note:_ An alternative would be to fix pure master variable $x^{\rm pure}_j$ by setting $lb_j\leftarrow 0$ and $ub_j\leftarrow 0$. This would leave less freedom for future updates of the partial solution (i.e. this would lead to a more aggressive diving for example). ### Representative variables We consider a representative master variable $x^{\rm repr}_j$ with bounds $[lb^g_j, ub^g_j]$ before augmenting the partial solution. We assume that $x^{\rm repr}_j$ represents exactly one variable $x^k_j$ in subproblem $k$ with bounds $[lb^l_j, ub^l_j]$ before augmenting the partial solution. _This assumption should be verified before augmenting the partial solution!_ For the clarity of presentation, we omit index $j$ for the remainder of this section. After augmenting the partial solution, the following inequalities should be satisfied: $$ lb^g - \bar{x}^{\rm repr}\leq x^{\rm repr} \leq ub^g - \bar{x}^{\rm repr}.$$ At the same time, we should have $$\min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\leq x^{\rm repr}\leq \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}$$ Thus, the following update should be done $$ lb^g\leftarrow \max\left\{lb^g - \bar{x}^{\rm repr},\; \min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\right\}$$ $$ ub^g\leftarrow \min\left\{ub^g - \bar{x}^{\rm repr},\; \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}\right\}$$ > _Example 1:_ Let $0\leq x^k\leq 3$, $0\leq x^{\rm repr}\leq 6$, $L_k=0$, and $U_k=2$ before augmenting the partial solution. Let local partial solution $\bar{x}^{\rm repr}=2$. After augmenting partial solution, we have $U_k\leftarrow 1$ and > $$ \max\left\{-2,\; 0\right\}\leq x^{\rm repr} \leq \min\left\{4,\; 3\right\} \Rightarrow 0 \leq x^{\rm repr} \leq 3$$ > _Example 2:_ Let $0\leq x^k\leq 5$, $3\leq x^{\rm repr}\leq 6$, $L_k=0$, and $U_k=2$ before augmenting the partial solution. Let local partial solution $\bar{x}^{\rm repr}=2$. Then after augmenting the partial solution, we have $U_k\leftarrow 1$ and > $$ \max\left\{1,\; 0\right\}\leq x'_{\rm repr} \leq \min\left\{4,\; 5\right\} \Rightarrow 1 \leq x'_{\rm repr} \leq 4$$ > _Example 3:_ $-1\leq x^k\leq 4$, $-2\leq x^{\rm repr}\leq 2$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=-1$. Then after augmenting the partial solution, we have > $$ \max\left\{-1,\; -1\right\}\leq x^{\rm repr} \leq \min\left\{3,\; 4\right\} \Rightarrow -1 \leq x^{\rm repr} \leq 3$$ ### Implementation details To update bounds of representative and pure master variables in an unified way after augmenting a partial solution, we first calculate so-called _variable domains_. For a subproblem variable $x_j^k$, its domain is obtained as follows: $$[{\rm dom}^-_j,\;{\rm dom}^+_j] = \left[\min\{lb^l_j\cdot L_k,\; lb^l_j\cdot U_k\}, \max\{ub^l_j\cdot U_k,\; ub^l_j\cdot L_k\}\right]$$ For a pure master variable $x^{\rm pure}_j$, its domain depends on value $\bar{x}^{\rm pure}_j$: $$ [{\rm dom}^-_j,\;{\rm dom}^+_j] = \left\{ \begin{array}{ll} [0,+\infty), & \text{ if } \bar{x}^{\rm pure}_j > 0, \\ (-\infty,0], & \text{ if } \bar{x}^{\rm pure}_j < 0, \\ (-\infty,+\infty), & \text{ if } \bar{x}^{\rm pure}_j = 0. \\ \end{array}\right.$$ After calculating variable domains, their bounds can be updated simply by $$ lb_j\leftarrow \max\left\{lb_j - \bar{x}_j,\; {\rm dom}_j^-\right\}$$ $$ ub_j\leftarrow \min\left\{ub_j - \bar{x}_j,\; {\rm dom}_j^+\right\}$$ ## 2. Preprocessing "core" This step is always performed. Preprocessing is done iteratively for a fixed number of iterations. Each iteration consists of the following steps. ### Presolving the representative master Here we apply the standard MIP presolving of the representative master formulation consisting of pure master constraints, representative master variables, and robust master constraints. _Non-robust constraints should be excluded from presolving!_ Such presolve updates slacks of constraints and bounds of variables. It may * deactivate redundant constraints, * fix pure master variables $x_j^{\rm pure}$ with bounds $lb_j=ub_j$ (in this case $\bar{x}_j^{\rm pure}=lb_j$ is added to the global partial solution, we set $lb_j=ub_j\leftarrow 0$ and update the corresponding right-hand-sides of constraints). * detect infeasibility due to variables $x_j^{\rm pure}$ or $x_j^{\rm repr}$ such that $lb_j>ub_j$ (in this case the whole procedure stops with infeasibility). * deactivate pure master variables $x_j^{\rm pure}$ with bounds $lb_j=ub_j=0$. ### Propagate bounds from representative master variables to subproblem variables For each subproblem $k$, $U_k\geq 1$, and each subproblem variable $x_j^k$, we set: $$lb^l_j\leftarrow \max\left\{lb^l_j,\; lb^g_j - (U_k-1)\cdot ub^l_j\right\}$$ $$ub^l_j\leftarrow \min\left\{ub^l_j,\; ub^g_j - \max\{0, L_k-1\}\cdot lb^l_j\right\}$$ ### Presolving the subproblems Again, the standard MIP presolving is applied for each subproblem $k$. Such presolve updates slacks of constraints and bounds of variables. It may * remove redundant constraints, * detect infeasibility due to variables $x_j^k$ such that $lb^l_j>ub^l_j$ (in this case we set $L_k=U_k\leftarrow 0$). * deactivate variables $x_j^k$ with bounds $lb_j^l=ub_j^l=0$ (in this case representative variables $x_j^{\rm repr}$ in the master are also deactivated). ### Updating subproblem multiplicities For each subproblem $k$, $U_k\geq 1$, we try to update its multiplicities, based on local and global bounds of its variables. Consider a variable $x_j^k$, * If $lb^g_j>0$ and $ub^l>0$, then $L_k\leftarrow\max\{L_k, \lceil lb^g_j/ub^l_j\rceil\}$ * If $ub^g_j<0$ and $lb^l<0$, then $L_k\leftarrow\max\{L_k, \lceil ub^g_j/lb^l_j\rceil\}$ * If $lb^l_j>0$ and $ub^g>0$, then $U_k\leftarrow\min\{U_k, \lfloor ub^g_j/lb^l_j\rfloor\}$ * If $ub^l_j<0$ and $lb^g<0$, then $U_k\leftarrow\min\{U_k, \lfloor lb^g_j/ub^l_j\rfloor\}$ ### Propagate bounds from subproblem variables to representative master variables For each representative variable $x^{\rm repr}_j$ representing variable $x_j^k$ in subproblem $k$: $$lb^g_j\leftarrow \max\left\{lb^g_j,\; \min\{lb^l_j\cdot L_k,\; lb^l_j\cdot U_k\}\right\}$$ $$ub^g_j\leftarrow \min\left\{ub^g_j,\; \max\{ub^l_j\cdot L_k,\; ub^l_j\cdot U_k\}\right\}$$ ## 3. Removing non-proper columns Finally, we should deactivate non-proper columns for each subproblem $k$, i.e., columns $\lambda_q$, $q\in Q_k$, such that $s^q_j<lb^l_j$ or $s^q_j>ub^l_j$, where $s^q_j$ is the value of variable $x_j^k$ in solution ${\bm s}_q$ associated with column $\lambda_q$.	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MPL-2.0" ]	0.8.1	828c61e9434b6af5f7908e42aacd17de35f08482	docs	281	Here is a non-exhaustive list of classic problems tackled with Coluna: - [Generalized Assignement](@ref tuto_gen_assignement) and some variants using [pricing callback](@ref tuto_pricing_callback) and [cut callback](@ref tuto_cut_callback) - [Bin Packing](@ref tuto_custom_data)	Coluna	https://github.com/atoptima/Coluna.jl.git
[ "MIT" ]	0.2.0	9e94fb961f02c4eaeb8e83268cb34245395030dd	code	751	module PartialWaveFunctions export f_logfact include("factorials.jl") export wignerd, wignerd_doublearg export wignerD, wignerD_doublearg export kronecker include("wignerd.jl") export CG, CG_doublearg export clebschgordan, clebschgordan_doublearg include("clebsch_gordan.jl") end # module # # # # # # # # # # # # # # # # # # # # # # # # # created with PkgTemplates # using PkgTemplates # t = Template(; # user="mmikhasenko", # license="MIT", # authors="Misha Mikhasenko", # dir=joinpath(DEPOT_PATH[1], "dev"), # julia_version=v"1.2", # plugins=[ # TravisCI(), # Codecov(), # AppVeyor(), # ], # ) # generate("PartialWaveFunctions", t) # # # # # # # # # # # # # # # # # # # # # # # # # #	PartialWaveFunctions	https://github.com/mmikhasenko/PartialWaveFunctions.jl.git
[ "MIT" ]	0.2.0	9e94fb961f02c4eaeb8e83268cb34245395030dd	code	2378	""" CG(j1,m1,j2,m2,j,m) a shortcut for `clebschgordan` """ CG(j1,m1,j2,m2,j,m) = clebschgordan(j1,m1,j2,m2,j,m) """ CG_doublearg(j1,m1,j2,m2,j,m) a shortcut for `clebschgordan_doublearg` """ CG_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) = clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) """ clebschgordan(j1,m1,j2,m2,j,m) gives a numerical value of the Clebsch-Gordan coefficient ``` ⟨ j₁ m₁ ; j₂ m₂ \| j m ⟩ ``` The input values are expected to be __integers__. For a general case including half-integers see `clebschgordan_doublearg`. """ clebschgordan(j1,m1,j2,m2,j,m) = clebschgordan_doublearg(2j1,2m1,2j2,2m2,2j,2m) """ clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) gives a numerical value of the Clebsch-Gordan coefficient ``` ⟨ j₁ m₁ ; j₂ m₂ \| j m ⟩ ``` The function requires __doubled value of the momenta__ for the input. """ function clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) ((abs(two_m1) > two_j1) \|\| (abs(two_m2) > two_j2) \|\| (abs(two_m ) > two_j )) && return 0.0 ((two_m1+two_m2 != two_m) \|\| !(abs(two_j1-two_j2) ≤ two_j ≤ two_j1+two_j2)) && return 0.0 # prefactor = sqrt(two_j+1)* exp( ( f_logfact2(two_j1+two_j2-two_j) + f_logfact2(two_j1+two_j -two_j2) + f_logfact2(two_j2+two_j -two_j1) - f_logfact2(two_j1+two_j2+two_j+2) + # f_logfact2(two_j1+two_m1) + f_logfact2(two_j1-two_m1) + f_logfact2(two_j2+two_m2) + f_logfact2(two_j2-two_m2) + f_logfact2(two_j +two_m ) + f_logfact2(two_j -two_m ) ) / 2) res = 0.0 two_t_min = max(0, two_j2-two_m1-two_j, two_j1+two_m2-two_j) two_t_max = min(two_j1+two_j2-two_j, two_j1-two_m1, two_j2+two_m2) # for two_t = two_t_min:2:two_t_max logs = f_logfact2(two_t) + f_logfact2(two_j-two_j2+two_m1+two_t) + f_logfact2(two_j-two_j1-two_m2+two_t) + f_logfact2(two_j1+two_j2-two_j-two_t) + f_logfact2(two_j1-two_m1-two_t) + f_logfact2(two_j2+two_m2-two_t); res += (abs(two_t) % 4 == 2 ? -1.0 : 1.0) * exp(-logs); end res *= prefactor return res; end	PartialWaveFunctions	https://github.com/mmikhasenko/PartialWaveFunctions.jl.git
[ "MIT" ]	0.2.0	9e94fb961f02c4eaeb8e83268cb34245395030dd	code	570	const logfact = [0,[sum(log(i) for i=1:n) for n=1:50]...]; """ f_logfact(n) The function returns logarithm of n! """ function f_logfact(n) (n < 0 \|\| n > 50) && error("n < 0 \|\| n > 50. Modify if needed.") @inbounds return logfact[n+1] end # special function used in the code: # (two_n/2)! for even numbers # (two_n-1/2)! for odd numbers const logfact2 = [f_logfact(div(two_n,2)) for two_n=1:100]; function f_logfact2(two_n) (two_n < 0 \|\| two_n > 100) && error("two_n < 0 \|\| two_n > 100. Modify if needed.") @inbounds return logfact2[two_n+1] end	PartialWaveFunctions	https://github.com/mmikhasenko/PartialWaveFunctions.jl.git
[ "MIT" ]	0.2.0	9e94fb961f02c4eaeb8e83268cb34245395030dd	code	5772	# _\| _\| # _\|_\|_\| _\|_\|_\|_\| _\|_\| _\|_\|_\| _\|_\| _\| _\|_\| # _\| _\| _\| _\| _\|_\|_\|_\| _\| _\| _\|_\|_\|_\| _\|_\| # _\| _\| _\| _\| _\| _\| _\| _\| _\| # _\| _\| _\| _\|_\| _\|_\|_\| _\|_\|_\| _\|_\|_\| _\| # _\| # _\|_\| function jacobi_pols(n, a, b, z) if (n+a > length(logfact) \|\| n+b > length(logfact)) error("Error: j is too high, please check the implementation of jacobi polynomials!") end # special case when I can not calculate log if (z ≈ 1) \|\| (z ≈ -1) return sum((s % 2 == 0 ? 1.0 : -1.0) * exp(logfact[n+a+1] + logfact[n+b+1]-logfact[n-s+1]-logfact[a+s+1]-logfact[s+1]-logfact[n+b-s+1])* ((1-z)/2.0)^s((1+z)/2.0)^(n-s) for s = 0:n) end # general case ls = log((1.0-z)/2.0); lc = log((1.0+z)/2.0); res = 0.0; for s = 0:n logs = logfact[(n+a+1)::Int] + logfact[(n+b+1)::Int]- logfact[(n-s+1)::Int]-logfact[(a+s+1)::Int]- logfact[(s+1)::Int]-logfact[(n+b-s+1)::Int]; args = sls + (n-s)lc; res += (s % 2 == 0 ? 1.0 : -1.0) exp(logs+args); end return res; end function wignerd_hat(j, m1, m2, z) if abs(m1) > j \|\| abs(m2) > j return zero(z) end factor = ((abs(m1-m2)+m1-m2)/2) % 2 == 0 ? one(z) : -one(z); am1 = abs(m1); am2 = abs(m2); M = (am1 > am2) ? am1 : am2; N = (am1 < am2) ? am1 : am2; gammas = logfact[Int(j-M+1)]+logfact[Int(j+M+1)]-(logfact[Int(j-N+1)]+logfact[Int(j+N+1)]) return factor / 2^M * exp(gammas/2)* jacobi_pols(Int(j-M), Int(abs(m1-m2)), Int(abs(m1+m2)), z); end """ wignerd(j, m1, m2, cosβ) Small wigner d-function for representation `j` with indices `m1`, `m2`, the argument `cosβ` is the cosine of the rotation angle. The function gives the value of the matrix element ``` ⟨ j m1 \| exp(-i Jy β)\| j m2 ⟩. ``` The input values are expected to be __integers__. For a general case including half-integers see `wignerd_doublearg`. """ function wignerd(j, m1, m2, z) (z ≈ 1) && return m1 == m2 ? one(z) : zero(z) (z ≈ -1) && return m1 == -m2 ? (iseven(j-m2) ? one(z) : -one(z)) : zero(z) # hat = wignerd_hat(j, m1, m2, z); xi = sqrt(1-z)^abs(Int(m1-m2))sqrt(1+z)^abs(Int(m1+m2)); return hatxi; end """ wignerD(j, m1, m2, α, cosβ, γ) Wigner D-function for representation `j` with indices `m1`, `m2`, α, β, and γ are the rotation angles The function gives the value of the matrix element ``` ⟨ j m1 \| exp(-i Jz α) exp(-i Jy β) exp(-i Jz γ) \| j m2 ⟩. ``` The input values are expected to be __integers__. For a general case including half-integers see `wignerD_doublearg`. """ wignerD(j, m1, m2, α, cosβ, γ) = wignerd(j, m1, m2, cosβ) * cis(-m1α-m2γ) # # _\| _\| _\|_\| _\| _\| # _\|_\|_\| _\|_\|_\| _\| _\| _\|_\|_\| _\|_\|_\|_\| _\|_\| _\|_\|_\| _\|_\| _\| _\|_\| # _\| _\| _\| _\| _\| _\|_\|_\|_\| _\|_\|_\|_\|_\| _\| _\| _\| _\| _\|_\|_\|_\| _\| _\| _\|_\|_\|_\| _\|_\| # _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| # _\| _\| _\|_\|_\| _\| _\| _\| _\| _\| _\|_\| _\|_\|_\| _\|_\|_\| _\|_\|_\| _\| # _\| # _\|_\| function wignerd_hat_doublearg(two_j, two_m1, two_m2, z) # @show j, m1, m2 if abs(two_m1) > two_j \|\| abs(two_m2) > two_j return zero(z) end factor = (abs(two_m1-two_m2)+two_m1-two_m2) % 8 == 4 ? -one(z) : one(z); two_am1 = abs(two_m1); two_am2 = abs(two_m2); (two_M, two_N) = (two_am1 > two_am2) ? (two_am1,two_am2) : (two_am2,two_am1); # j_mnus_M = div(two_j-two_M,2) j_plus_M = div(two_j+two_M,2) j_mnus_N = div(two_j-two_N,2) j_plus_N = div(two_j+two_N,2) # gammas = logfact[j_mnus_M+1] + logfact[j_plus_M+1] - logfact[j_mnus_N+1] - logfact[j_plus_N+1] return factor / 2^(two_M/2) * exp(gammas/2) * jacobi_pols(j_mnus_M, div(abs(two_m1-two_m2),2), div(abs(two_m1+two_m2),2), z); end """ wignerd_doublearg(j, m1, m2, cosβ) Small wigner d-function for representation `j` with indices `m1`, `m2`, the argument `cosβ` is the cosine of the rotation angle. The function gives the value of the matrix element ``` ⟨ j m1 \| exp(-i Jy β)\| j m2 ⟩. ``` The function requires __doubled value of the momenta__ for the input. """ function wignerd_doublearg(two_j, two_m1, two_m2, z) (z ≈ 1) && return two_m1 == two_m2 ? one(z) : zero(z) (z ≈ -1) && return two_m1 == -two_m2 ? (iseven(div(two_j-two_m2,2)) ? one(z) : -one(z)) : zero(z) # hat = wignerd_hat_doublearg(two_j, two_m1, two_m2, z); xi = (1-z)^(abs(two_m1-two_m2)/4)(1+z)^(abs(two_m1+two_m2)/4); return hatxi; end """ wignerD_doublearg(two_j, two_m1, two_m2, α, cosβ, γ) Wigner D-function for representation `j` with indices `m1`, `m2`, α, β, and γ are the rotation angles The function gives the value of the matrix element ``` ⟨ j m1 \| exp(-i Jz α) exp(-i Jy β) exp(-i Jz γ) \| j m2 ⟩. ``` The function requires __doubled value of the momenta__ for the input. """ wignerD_doublearg(two_j, two_m1, two_m2, α, cosβ, γ) = wignerd_doublearg(two_j, two_m1, two_m2, cosβ) * cis(-two_m1α/2-two_m2γ/2); """ kronecker(i, j) = (i==j) ? 1 : 0 """ kronecker(i, j) = (i==j) ? 1 : 0	PartialWaveFunctions	https://github.com/mmikhasenko/PartialWaveFunctions.jl.git
[ "MIT" ]	0.2.0	9e94fb961f02c4eaeb8e83268cb34245395030dd	code	625	using PartialWaveFunctions using Test @testset "PartialWaveFunctions.jl" begin # Clebsch Gordan coefficient @test clebschgordan_doublearg(3, 3, 1, 1, 22, 22) == 1 @test clebschgordan_doublearg(3, 3, 1, -1, 21, 21) ≈ sqrt(3)/2 @test clebschgordan_doublearg(3, -1, 1, 1, 21, 20) ≈ -sqrt(2)/2 # Wigner Function @test wignerD(3, 2, 1, π, 0, -π) ≈ -sqrt(10)/8 + 0im @test wignerd(2, 0, 0, 1/sqrt(2)) ≈ (3/2-1)/2 @test wignerd(1, 1, 0, 0.3) ≈ -sqrt(1-0.3^2)/sqrt(2) @test wignerd_doublearg(1, 1, 1, 0) ≈ 1/sqrt(2) # @test sum(kronecker(i,j) for i in 1:5, j in 1:5) == 5 end	PartialWaveFunctions	https://github.com/mmikhasenko/PartialWaveFunctions.jl.git
[ "MIT" ]	0.2.0	9e94fb961f02c4eaeb8e83268cb34245395030dd	code	4290	using Test using Parameters using BenchmarkTools # import PartialWaveFunctions: CG_doublearg, clebschgordan_doublearg ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) = clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) # using GSL function ClGd_gsl(two_j1,two_m1,two_j2,two_m2,two_j,two_m) factor = sqrt(two_j+1)(mod(two_j1-two_j2+two_m,4)==2 ? -1 : +1) three_j = sf_coupling_3j(two_j1,two_j2,two_j,two_m1,two_m2,-two_m) return factorthree_j; end # using SymPy import PyCall PyCall.pyimport_conda("sympy.physics.wigner","sympy") import_from(sympy.physics.wigner) # ClGd_sympy(two_j1,two_m1,two_j2,two_m2,two_j,two_m) = convert(Float64, clebsch_gordan(Sym(two_j1)/2, Sym(two_j2)/2, Sym(two_j)/2, Sym(two_m1)/2, Sym(two_m2)/2, Sym(two_m)/2)) # import HalfIntegers: half using WignerSymbols function ClGd_WS(two_j1,two_m1,two_j2,two_m2,two_j,two_m) ((abs(two_m1) > two_j1) \|\| (abs(two_m2) > two_j2) \|\| (abs(two_m ) > two_j )) && return 0.0 return clebschgordan(half(two_j1),half(two_m1),half(two_j2),half(two_m2),half(two_j),half(two_m)) end # _\| # _\| _\|_\| _\|_\|_\| _\|_\|_\| _\|_\|_\| # _\|_\| _\| _\| _\| _\| _\| _\| # _\| _\| _\| _\| _\| _\| _\| # _\| _\|_\|_\| _\| _\| _\|_\|_\| function rand_clebsch(;two_j_max::Int=15) two_j1 = rand(0:two_j_max); two_m1 = rand(-two_j1:2:two_j1) two_j2 = rand(0:two_j_max); two_m2 = rand(-two_j2:2:two_j2) two_j = rand(abs(two_j2-two_j1):2:(two_j1+two_j2)) two_m = two_m1+two_m2 abs(two_m) > two_j && return rand_clebsch(; two_j_max=two_j_max) (two_j1=two_j1, two_j2=two_j2, two_j=two_j, two_m1=two_m1, two_m2=two_m2, two_m=two_m) end # fraction of vanishing clebshces ≈ 0.6% sum(let @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) ≈ 0 end for _ in 1:10_000) # _\| # _\|_\|_\| _\|_\| _\|_\|_\| _\|_\| _\|_\|_\| _\|_\|_\| _\| _\|_\| _\|_\|_\| _\|_\| _\|_\|_\| # _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\|_\| _\| _\|_\| _\| _\| _\| _\| # _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| _\|_\| _\| _\| _\| _\| # _\|_\|_\| _\|_\| _\| _\| _\| _\|_\|_\| _\|_\|_\| _\| _\| _\|_\|_\| _\|_\| _\| _\| # _\| # _\| for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() # v1 = ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) v2 = ClGd_gsl(two_j1,two_m1,two_j2,two_m2,two_j,two_m) v3 = ClGd_sympy(two_j1,two_m1,two_j2,two_m2,two_j,two_m) v4 = ClGd_WS(two_j1,two_m1,two_j2,two_m2,two_j,two_m) # (abs(v1-v2) > 1e-5) && error("gsl is different: $(v1) $(v2)") (abs(v1-v3) > 1e-5) && error("sympy is different") (abs(v1-v4) > 1e-5) && error("WS is different") end # _\| _\| _\| # _\|_\|_\|_\| _\|_\|_\| _\|_\| _\|_\|_\| _\|_\|_\| # _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| # _\| _\| _\| _\| _\| _\| _\| _\| _\| _\| # _\|_\| _\| _\| _\| _\| _\| _\| _\| _\|_\|_\| # _\| # _\|_\| @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 227.200 μs @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_gsl(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 921.8 μs @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_WS(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 7.483 ms @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_sympy(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 1.61 s # Summary # ------------- # function \| `PartialWaveFunctions.jl` \| `GSL.jl` \| `WignerSymbols.jl` \| `SymPy.jl` \| # ------------- # random Clebsh-Gordan coef.(j<15) \| 227.2 μs \| 921.8 μs \| 7.483 ms \| 1.61 s \| # -------------	PartialWaveFunctions	https://github.com/mmikhasenko/PartialWaveFunctions.jl.git
[ "MIT" ]	0.2.0	9e94fb961f02c4eaeb8e83268cb34245395030dd	docs	2812	# PartialWaveFunctions [![Build Status](https://travis-ci.com/mmikhasenko/PartialWaveFunctions.jl.svg?branch=master)](https://travis-ci.com/mmikhasenko/PartialWaveFunctions.jl) [![Build Status](https://ci.appveyor.com/api/projects/status/github/mmikhasenko/PartialWaveFunctions.jl?svg=true)](https://ci.appveyor.com/project/mmikhasenko/PartialWaveFunctions-jl) [![Codecov](https://codecov.io/gh/mmikhasenko/PartialWaveFunctions.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/mmikhasenko/PartialWaveFunctions.jl) Julia native implementation of the special functions used in the Partial Wave Analysis for High Energy Physics. Currently, Wigner D-functions and Clebsch-Gordan(CG) coefficients are available. The implementation of the CG coefficient is by factor 4 faster than the C++ code from the GSL package (see details in [test/timing](test/timing_different_packages.jl)). ## Installation ```julia ] add PartialWaveFunctions ``` ## Usage ```julia using PartialWaveFunctions # convenient call for integer indices let j=3, (m1,m2) = (1,-1), cosθ=0.3 wignerd(j,m1,m2,cosθ) end # return 0.293 clebschgordan(1,0,1,0,1,0) # <1, 0; 1, 0 \| 1, 0> = 0.0 : ρ⁰ → π⁰ π⁰ CG(1,0,1,0,1,0) # a shortcut ``` General implementation includes the half-integer indices: ```julia let two_j=3, (two_m1,two_m2) = (1,-1), cosθ=0.3 wignerd_doublearg(two_j,two_m1,two_m2, cosθ) end # return -0.562 clebschgordan_doublearg(2,0,1,1,1,1) # <1, 0; 1/2, 1/2 \| 1/2, 1/2> = -0.577 CG_doublearg(2,0,1,1,1,1) # a shortcut ``` ## Related packages: * python calls via `SymPy.jl`. Ideal for symbolic calculations. Works pretty with jupyter notebooks due to the latex output. See details in the [test/physics](https://github.com/JuliaPy/SymPy.jl/blob/master/test/test-physics.jl). * [WignerD.jl](https://github.com/jishnub/WignerD.jl) interfaces `Fortran` for the `WignerD`. * [WignerSymbols.jl](https://github.com/Jutho/WignerSymbols.jl) `Julia` package specialized on Symbols. Particularly it contains the Clebsch-Gordan coefficients. * [GSL.jl](https://github.com/JuliaMath/GSL.jl) interfaces `C++`. It can calculate Sperical Harmionics, Legendre polynomials. `WignerD` is not [wrapped-up](https://github.com/JuliaMath/GSL.jl/issues/66). ## References * The Wigner functions are expressed via the Jacobi polynomials Pₙ⁽ᵃᵇ⁾(z) using Eq. (3.74) of L. Biedenharn, J. Louck, and P. Carruthers, Angular Momentum in Quantum Physics: Theory and Application * The Jacobi polynomials Pₙ⁽ᵃᵇ⁾(z) are codded using a series expression in powers of (1-z), see e.g. [wikipedia page](https://en.wikipedia.org/wiki/Jacobi_polynomials). * Clebsch-Gordan coefficients are computed from explicit expression via a finite series, see e.g. [wikipedia page](https://en.wikipedia.org/wiki/Table_of_Clebsch%E2%80%93Gordan_coefficients)	PartialWaveFunctions	https://github.com/mmikhasenko/PartialWaveFunctions.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	606	using QuanticsTCI using Documenter DocMeta.setdocmeta!(QuanticsTCI, :DocTestSetup, :(using QuanticsTCI); recursive=true) makedocs(; modules=[QuanticsTCI], authors="Ritter.Marc <[email protected]> and contributors", sitename="QuanticsTCI.jl", format=Documenter.HTML(; canonical="https://github.com/tensor4all/QuanticsTCI.jl", edit_link="main", assets=String[]), pages=[ "Home" => "index.md", "API Reference" => "apireference.md", ]) deploydocs(; repo="github.com/tensor4all/QuanticsTCI.jl.git", devbranch="main", )	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	2052	function berrycurvature_dets(H::Array{Matrix{ComplexF64}}, n::Integer) Heigen = eigen.(H) vecs00 = [vectors[:, 1:n] for (vals, vectors) in Heigen] vecs01 = circshift(vecs00, (0, 1)) vecs10 = circshift(vecs00, (1, 0)) vecs11 = circshift(vecs00, (1, 1)) function getdets(vecs1, vecs2) d = det(adjoint(vecs1) * vecs2) return d end dets = ( getdets.(vecs00, vecs10) .* getdets.(vecs10, vecs11) .* getdets.(vecs11, vecs01) .* getdets.(vecs01, vecs00) ) # bc = angle.(dets) bc = @. mod(angle(dets) + pi / 2, pi) - pi / 2 return bc end function modindex(i::Integer, n::Integer) return mod(i - 1, n) + 1 end function berrycurvature_quantics_dets( Hfunc, n::Integer, q::Vector{<:Integer}, nquantics::Integer ) k = [quantics_to_index(qi)[1] for qi in split_dimensions(q, 2)] Hplaquette = [ Hfunc(modindex.(k .+ [dkx, dky], 2^nquantics)) for dkx in -1:0, dky in -1:0] return berrycurvature_dets(Hplaquette, n)[1, 1] end function berrycurvature_derivatives( H::Matrix{ComplexF64}, Hderivative1::Matrix{ComplexF64}, Hderivative2::Matrix{ComplexF64}, n::Integer ) E, U = eigen(Hermitian(H)) return -1 * sum(imag( ( (U[:, v]' * Hderivative1 * U[:, c]) * (U[:, c]' * Hderivative2 * U[:, v]) - (U[:, v]' * Hderivative2 * U[:, c]) * (U[:, c]' * Hderivative1 * U[:, v]) ) / (E[c] - E[v])^2 ) for v in 1:n, c in n+1:length(E)) # Ediff = E[n+1:end] .- E[1:n]' # v1 = U[:, 1:n]' * Hderivative1 * U[:, n+1:end] ./ Ediff' # v2 = U[:, n+1:end]' * Hderivative2 * U[:, 1:n] ./ Ediff # return 2 * tr(imag.(v1 * v2)) end function berrycurvature_quantics_derivatives( Hfunc, Hderivfunc, n::Integer, q::Vector{<:Integer} ) k = [quantics_to_index(qi)[1] for qi in deinterleave_dimensions(q, 2)] return berrycurvature_derivatives( Hfunc(k), Hderivfunc(k, 1), Hderivfunc(k, 2), n) end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	5622	module chern using LinearAlgebra using PyPlot import TensorCrossInterpolation as TCI using QuanticsTCI using BenchmarkTools using ITensors include("honeycomb.jl") include("kanemele.jl") include("latticeplot.jl") include("berry.jl") function maxlinkdim(n::Integer, localdim::Integer=2) return 0:n-2, [min(localdim^i, localdim^(n - i)) for i in 1:(n-1)] end function sumqtt(qtt) return prod(sum(T, dims=2)[:, 1, :] for T in qtt)[1] end function sum_quantics_mps(mps) m = mps[1] * ITensor(1, siteind(mps, 1)) for i in 2:length(mps) m = mps[i] ITensor(1, siteind(mps, i)) end return scalar(m) end function mps_to_array(mps) result = Vector{Array{Float64, 3}}() T1 = Array(mps[1], siteind(mps, 1), linkind(mps, 1)) push!(result, reshape(T1, 1, size(T1)...)) for i in 2:length(mps)-1 push!(result, Array(mps[i], linkind(mps, i-1), siteind(mps, i), linkind(mps, i))) end Tlast = Array(mps[end], linkind(mps, length(mps)-1), siteind(mps, length(mps))) push!(result, reshape(Tlast, size(Tlast)..., 1)) return result end mutable struct functioncounter f::Function n::Int end Base.broadcastable(m::functioncounter) = Ref(m) function functioncounter(f::Function) return functioncounter(f, 0) end function (f::functioncounter)(k) f.n += 1 return f.f(k) end function getberryqtt_dets( nquantics::Integer, kxvals::Vector{Float64}, kyvals::Vector{Float64}, n::Integer, lattice::TCI.IndexSet{HoneycombSite}, q::Integer, lambdaSO::Float64; spinindex=1, tolerance=1e-12 ) Hcached = TCI.CachedFunction{Matrix{ComplexF64}}( kindex -> get_H( q, lambdaSO, [kxvals[kindex[1]], kyvals[kindex[2]]], lattice )[:, spinindex, :, spinindex], [2^nquantics, 2^nquantics] ) f = functioncounter(k -> berrycurvature_quantics_dets(Hcached, n, k, nquantics)) firstpivot = TCI.optfirstpivot(f, fill(2, 2 * nquantics)) f.n = 0 tci, ranks, errors = TCI.crossinterpolate( Float64, f, fill(2, 2 * nquantics), firstpivot, tolerance=tolerance, maxiter=200, verbosity=1, ) qtt = TCI.tensortrain(tci) return sumqtt(qtt) / 2pi, qtt, ranks, errors, f.n end function getberrymps_dets( nquantics::Integer, kxvals::Vector{Float64}, kyvals::Vector{Float64}, n::Integer, lattice::TCI.IndexSet{HoneycombSite}, q::Integer, lambdaSO::Float64; spinindex=1, tolerance=1e-12 ) H = [ get_H(q, lambdaSO, [kx, ky], lattice)[:, spinindex, :, spinindex] for kx in kxvals, ky in kyvals ] quanticsindices = [ Index(2, i % 2 == 0 ? "qx$(div(i, 2))" : "qy$(div(i, 2))") for i in 1:2nquantics ] A = ITensor(berrycurvature_dets(H, n), quanticsindices) mps = MPS(A, quanticsindices, cutoff=tolerance, maxdim=200) return sum_quantics_mps(mps) / 2pi, mps_to_array(mps), linkdims(mps), prod(size(A)) end function getberryqtt_derivs( nquantics::Integer, kxvals::Vector{Float64}, kyvals::Vector{Float64}, n::Integer, lattice::TCI.IndexSet{HoneycombSite}, q::Integer, lambdaSO::Float64; spinindex=1, tolerance=1e-12 ) Hfunc(kindex) = get_H(q, lambdaSO, [kxvals[kindex[1]], kyvals[kindex[2]]], lattice)[:, spinindex, :, spinindex] Hderivfunc(kindex, derivdirection) = get_H(q, lambdaSO, [kxvals[kindex[1]], kyvals[kindex[2]]], lattice, derivative_direction=derivdirection)[:, spinindex, :, spinindex] f = functioncounter(k -> berrycurvature_quantics_derivatives(Hfunc, Hderivfunc, n, k)) firstpivot = TCI.optfirstpivot(f, fill(2, 2 * nquantics)) f.n = 0 tci, ranks, errors = TCI.crossinterpolate( Float64, f, fill(2, 2 * nquantics), firstpivot, tolerance=tolerance, maxiter=200, verbosity=1, ) qtt = TCI.tensortrain(tci) return sumqtt(qtt) / 2pi, qtt, ranks, errors, f.n end struct BerryResult nq::Int chernnumber::Float64 qtt::Vector{Array{Float64,3}} ranks::Vector{Int} errors::Vector{Float64} nevals::Int timeestimate::Float64 chernnumbermps::Float64 mps::Vector{Array{Float64,3}} mpsranks::Vector{Int} mpsnevals::Int mpstimeestimate::Float64 end function testberry(q::Integer, lambdaSO::Float64, nquantics=5:10) lattice = honeycomblattice(0, 1, 0, q - 1) BZedgex = pi / 1.5 BZedgey = pi / sqrt(3) results = BerryResult[] for nq in nquantics ndiscretization = 2^nq kxvals = collect(range(-BZedgex, BZedgex; length=ndiscretization)) .+ (BZedgex / ndiscretization) kyvals = collect(range(-BZedgey, BZedgey; length=ndiscretization)) .+ (BZedgey / ndiscretization) timeestimate = @elapsed result = getberryqtt_dets(nq, kxvals, kyvals, 2q, lattice, q, lambdaSO, tolerance=1e-5) mpstime = @elapsed mpsresult = getberrymps_dets(nq, kxvals, kyvals, 2q, lattice, q, lambdaSO, tolerance=1e-5) push!( results, BerryResult( nq, result..., timeestimate, mpsresult..., mpstime )) println( "Finished nq = $nq. Chern number from qtt is $(last(results).chernnumber). Time elapsed is $timeestimate for $(last(results).nevals) function evaluations. Chern number from mps is $(last(results).chernnumbermps). Time elapsed is $mpstime for $(last(results).mpsnevals) function evaluations.") end return results end end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	505	using PyPlot using JLD2 include("chern.jl") nq = parse(Int, ARGS[1]) result = chern.testberry(4, 0.2, nq)[1] jldsave("chern_results/nq$nq.jld2"; nq=result.nq, chernnumber=result.chernnumber, qtt=result.qtt, ranks=result.ranks, errors=result.errors, nevals=result.nevals, timeestimate=result.timeestimate, chernnumbermps=result.chernnumbermps, mps=result.mps, mpsranks=result.mpsranks, mpsnevals=result.mpsnevals, mpstimeestimate=result.mpstimeestimate)	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	7719	import TensorCrossInterpolation as TCI using QuanticsTCI using JLD2 include("chern.jl") pauli0 = [1. 0.; 0. 1.] pauli = [ [0. 1.; 1. 0.], [0. -1.0im; 1.0im 0.], [1. 0.; 0. -1.] ] antisymmetricproduct(u, v) = u[1] * v[2] - u[2] * v[1] function haldane(k, t2, ϕ, m) a::Vector{Vector{Float64}} = [ [1, 0], [-0.5, 0.5sqrt(3)], [-0.5, -0.5sqrt(3)] ] b::Vector{Vector{Float64}} = [a[2] - a[3], a[3] - a[1], a[1] - a[2]] return 2 * t2 * cos(ϕ) * sum(cos(k' * bi) for bi in b) * pauli0 + # NNN hopping sum(cos(k' * ai) * pauli[1] + sin(k' * ai) * pauli[2] for ai in a) + # NN hopping (m - 2 * t2 * sin(ϕ) * sum(sin(k' * bi) for bi in b)) * pauli[3] # staggered offset end function scalar(a::Matrix) if size(a) == (1, 1) return first(a) else throw(ArgumentError("$a is not a scalar.")) end end function evaluate_qtt(qtt, q::Vector{<:Integer}) return scalar(prod(T[:, i, :] for (T, i) in zip(qtt, q))) end struct cachedfunc{ValueType} f::Function d::Dict{Vector{Int}, ValueType} function cachedfunc(::Type{ValueType}, f::Function) where ValueType new{ValueType}(f, Dict()) end end function (cf::cachedfunc{ValueType})(x::Vector{Int})::ValueType where {ValueType} if haskey(cf.d, x) return cf.d[x] else val = cf.f(x) cf.d[deepcopy(x)] = val return val end end Base.broadcastable(x::cachedfunc) = Ref(x) function sumqtt(qtt) return prod(sum(T, dims=2)[:, 1, :] for T in qtt)[1] end function maxrelerror(f, qtt::Vector{Array{Float64, 3}}, indices::Vector{Vector{Int}}) return maximum(abs(f(i) - evaluate_qtt(qtt, i)) / abs(f(i)) for i in indices) end function maxabserror(f, qtt::Vector{Array{Float64, 3}}, indices::Vector{Vector{Int}}) return maximum(abs(f(i) - evaluate_qtt(qtt, i)) for i in indices) end function crossinterpolate_chern( ::Type{ValueType}, f, localdims::Vector{Int}, firstpivot::TCI.MultiIndex=ones(Int, length(localdims)); tolerance::Float64=1e-8, maxiter::Int=200, sweepstrategy::TCI.SweepStrategies.SweepStrategy=TCI.SweepStrategies.back_and_forth, pivottolerance::Float64=1e-12, normalizeerror=true, verbosity::Int=0, additionalpivots::Vector{TCI.MultiIndex}=TCI.MultiIndex[], evalooserror::Bool=false, oosindices::Vector{TCI.MultiIndex}=[rand([1, 2], length(localdims)) for _ in 1:2000], ) where {ValueType} tci = TCI.TensorCI{ValueType}(f, localdims, firstpivot) n = length(tci) errors = Float64[] cherns = Float64[] ranks = Int[] inserrors = Float64[] ooserrors = Float64[] for pivot in additionalpivots println("Adding pivot $pivot") TCI.addglobalpivot!(tci, f, pivot, tolerance) println("Rank $(TCI.rank(tci))") end for iter in TCI.rank(tci)+1:maxiter foward_sweep = ( sweepstrategy == TCI.SweepStrategies.forward \|\| (sweepstrategy != TCI.SweepStrategies.backward && isodd(iter)) ) if foward_sweep TCI.addpivot!.(tci, 1:n-1, f, pivottolerance) else TCI.addpivot!.(tci, (n-1):-1:1, f, pivottolerance) end push!(errors, TCI.lastsweeppivoterror(tci)) push!(ranks, maximum(TCI.rank(tci))) if evalooserror tt = TCI.tensortrain(tci) insindices = collect(setdiff(keys(f.d), oosindices)) push!(inserrors, maxabserror(f, tt, insindices)) push!(ooserrors, maxabserror(f, tt, oosindices)) push!(cherns, sumqtt(tt) / 4 / 2pi) end if verbosity > 0 && (mod(iter, 10) == 0 \|\| last(errors) < tolerance) if evalooserror println("rank = $(last(ranks)), error = $(last(errors)), chern = $(last(cherns))") else println("rank = $(last(ranks)), error = $(last(errors))") end end errornormalization = normalizeerror ? tci.maxsamplevalue : 1.0 if last(errors) < tolerance * errornormalization break end end errornormalization = normalizeerror ? tci.maxsamplevalue : 1.0 return tci, ranks, errors ./ errornormalization, cherns, inserrors, ooserrors end function evaluatechern_haldane( deltam::Float64, nquantics::Int; t2::Float64=1e-1, tolerance::Float64=1e-4, evalooserror::Bool=false, ) phi = pi/2 m = 3sqrt(3) * t2 + deltam domainboundx = [-4pi/3, 4pi/3] domainboundy = [-6pi/(3sqrt(3)), 6pi/(3sqrt(3))] ndiscretization = 2^nquantics kxvals = range(domainboundx..., length=ndiscretization+1)#[2:end] kxvals = 0.5 .* (kxvals[1:ndiscretization] .+ kxvals[2:end]) kyvals = range(domainboundy..., length=ndiscretization+1)#[2:end] kyvals = 0.5 .* (kyvals[1:ndiscretization] .+ kyvals[2:end]) f(q) = chern.berrycurvature_quantics_dets( kindex -> haldane([kxvals[kindex[1]], kyvals[kindex[2]]], t2, phi, m), 1, q, nquantics) localdims = fill(4, nquantics) #cf = TCI.CachedFunction{Float64}(f, localdims) cf = cachedfunc(Float64, f) # proposedpivots = [ # TCI.optfirstpivot(cf, dims, rand([1, 2, 3, 4], nquantics)) for p in 1:1000 # ] #firstpivot = proposedpivots[argmax(cf.(proposedpivots))] firstpivot = TCI.optfirstpivot(cf, localdims) println("$firstpivot, $(cf(firstpivot))") # [2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, # 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1][1:2nquantics]) additionalpivots = [] # TCI.optfirstpivot(cf, localdims, # [2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, # 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1][1:2nquantics]), # TCI.optfirstpivot(cf, localdims), # TCI.optfirstpivot(cf, localdims, localdims)] # TCI.optfirstpivot(cf, localdims, repeat([1, 2], nquantics)), # TCI.optfirstpivot(cf, localdims, repeat([2, 1], nquantics))] # sort!(additionalpivots, by=abs ∘ cf, rev=true) # for a in additionalpivots # println(a, cf(a)) # end cutxstep = div(ndiscretization, 4) #quarter = div(ndiscretization, 4) cutxvals = 1:cutxstep:ndiscretization cutystep = div(ndiscretization, 8192) oosindices = [ index_to_quantics([kxi, kyi], nquantics) for kxi in cutxvals, kyi in 684:cutystep:ndiscretization ] walltime = @elapsed tci, ranks, errors, cherns, inserrors, ooserrors = crossinterpolate_chern( Float64, cf, localdims, firstpivot, tolerance=tolerance, maxiter=200, verbosity=1, pivottolerance=1e-16, #additionalpivots = additionalpivots, evalooserror=evalooserror, oosindices=oosindices[:] ) chernnumber = NaN if !evalooserror walltimeint = @elapsed chernnumber = sumqtt(TCI.tensortrain(tci)) / 4 / 2pi walltime += walltimeint else chernnumber = last(cherns) end println("δm = $deltam, R = $nquantics : C = $chernnumber") savepath::String = ( evalooserror ? "example/chern/haldane_results_oos/nq$(nquantics)_deltam$(deltam).jld2" : "example/chern/haldane_results/nq$(nquantics)_deltam$(deltam).jld2" ) jldsave( savepath; deltam=deltam, nquantics=nquantics, cherns=cherns, chernnumber=chernnumber, tci=tci, ranks=ranks, errors=errors, nevals=length(cf.d), walltime=walltime, inserrors=inserrors, ooserrors=ooserrors ) end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	1857	using TensorCrossInterpolation struct HoneycombSite R::Vector{Int} c::Int function HoneycombSite(R::Vector{Int}, c::Int) if length(R) != 2 \|\| (c != 1 && c != 2) throw(ArgumentError("Invalid site specification R = $R, c = $c")) end return new(R, c) end end function Base.isequal(l::HoneycombSite, r::HoneycombSite) return l.R == r.R && l.c == r.c end function Base.hash(s::HoneycombSite, h::UInt) return foldr(hash, [s.R, s.c, :HoneycombSite]; init=h) end function realspacecoordinates(s::HoneycombSite) A1::Vector{Float64} = [3 / 2, sqrt(3) / 2] A2::Vector{Float64} = [0, sqrt(3)] cshift::Vector{Float64} = s.c == 1 ? [0, 0] : [1, 0] return A1 * s.R[1] + A2 * s.R[2] + cshift end function neighbours(s::HoneycombSite) if s.c == 1 return [ HoneycombSite(s.R, 2), HoneycombSite(s.R + [-1, 0], 2), HoneycombSite(s.R + [-1, 1], 2) ] else return [ HoneycombSite(s.R, 1), HoneycombSite(s.R + [1, 0], 1), HoneycombSite(s.R + [1, -1], 1) ] end end function nextneighbours(s::HoneycombSite) return [ HoneycombSite(s.R + [1, 0], s.c), HoneycombSite(s.R + [1, -1], s.c), HoneycombSite(s.R + [-1, 0], s.c), HoneycombSite(s.R + [-1, 1], s.c), HoneycombSite(s.R + [0, 1], s.c), HoneycombSite(s.R + [0, -1], s.c) ] end function reducesite(s::HoneycombSite, Lx::Int, Ly::Int) xnew = mod(s.R[1], 2 * Lx) return HoneycombSite( [xnew, mod(s.R[2] - div(xnew - s.R[1], 2), Ly)], s.c ) end function honeycomblattice(xmin::Integer, xmax::Integer, ymin::Integer, ymax::Integer) return TCI.IndexSet( [HoneycombSite([x, y], c) for c in 1:2, x in xmin:xmax, y in ymin:ymax][:] ) end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	3342	function peierls(B::Real, i::HoneycombSite, j::HoneycombSite) ri = realspacecoordinates(i) rj = realspacecoordinates(j) phase = -B * (ri[1] - rj[1]) * (ri[2] + rj[2]) / 2 return mod(phase, 2pi) # return 0.0 end function rashba(distance::Vector{Float64}) pauli = [ [0. 1.; 1. 0.], [0. -1.0im; 1.0im 0.], [1. 0.; 0. -1.] ] return pauli[1] .* distance[2] .- pauli[2] .* distance[1] end function get_Ht( q::Integer, k::Vector{Float64}, lattice::TCI.IndexSet{HoneycombSite}; mass::Float64=0.0, derivative_direction::Union{Nothing,Int}=nothing ) B = 4pi / sqrt(3) / q Ht = zeros(ComplexF64, 4q, 2, 4q, 2) for (i, s) in enumerate(lattice.fromint) # Alternating "mass term" Ht[i, 1, i, 1] += s.c == 1 ? mass : -mass # Hopping for n in neighbours(s) rs, rn = realspacecoordinates.([s, n]) phase = -k' * (rn - rs) + peierls(B, s, n) j = TCI.pos(lattice, reducesite(n, 1, q)) result = -exp(1im * phase) if !isnothing(derivative_direction) result = rs[derivative_direction] - rn[derivative_direction] end Ht[i, 1, j, 1] += result end end Ht[:, 2, :, 2] = Ht[:, 1, :, 1] return Ht end function get_HR( q::Integer, k::Vector{Float64}, lattice::TCI.IndexSet{HoneycombSite}; derivative_direction::Union{Nothing,Int}=nothing ) @assert isnothing(derivative_direction) B = 4pi / sqrt(3) / q HR = zeros(ComplexF64, 4q, 2, 4q, 2) for (i, s) in enumerate(lattice.fromint) for n in neighbours(s) rs, rn = realspacecoordinates.([s, n]) j = TCI.pos(lattice, reducesite(n, 1, q)) HR[i, :, j, :] = 1im exp(1im * peierls(B, s, n)) * rashba(rn - rs) end end return HR end antisymmetricproduct(u, v) = u[1] * v[2] - u[2] * v[1] function get_Hlambda( q::Integer, k::Vector{Float64}, lattice::TCI.IndexSet{HoneycombSite}; derivative_direction::Union{Nothing,Int}=nothing ) B = 4pi / sqrt(3) / q Hlambda = zeros(ComplexF64, 4q, 2, 4q, 2) # "Spin-orbit coupling" term for (i, s) in enumerate(lattice.fromint) for n in neighbours(s) for nn in neighbours(n) j = TCI.pos(lattice, reducesite(nn, 1, q)) rs, rn, rnn = realspacecoordinates.([s, n, nn]) phase = -(k' * (rnn - rs)) + peierls(B, s, nn) nu = sign(antisymmetricproduct(rn - rs, rnn - rn)) result = nu * exp(1im * phase) if !isnothing(derivative_direction) result = rs[derivative_direction] - rnn[derivative_direction] end Hlambda[i, 1, j, 1] += 1im result end end end Hlambda[:, 2, :, 2] = -Hlambda[:, 1, :, 1] return Hlambda end function get_H( q::Integer, lambda_SO::Float64, lambda_R::Float64, k::Vector{Float64}, lattice::TCI.IndexSet{HoneycombSite}; mass::Float64=0.0, derivative_direction::Union{Nothing,Int}=nothing ) return get_Ht(q, k, lattice; mass, derivative_direction) .+ lambda_SO .* get_Hlambda(q, k, lattice; derivative_direction) .+ lambda_R .* get_HR(q, k, lattice) end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	740	function displayhamiltonian( ax, H::Matrix{Float64}, lattice::TCI.IndexSet; vmax::Float64=maximum(abs.(H)), cm=get_cmap("Purples") ) ax.set_aspect(1) for s in lattice.fromint for n in neighbours(s) rs, rn = realspacecoordinates.([s, n]) ax.plot([rs[1], rn[1]], [rs[2], rn[2]], color="gray", linewidth=0.5) end end coords = realspacecoordinates.(lattice.fromint) for (i, ri) in enumerate(coords) for (j, rj) in enumerate(coords) value = H[i, j] / vmax ax.plot( [ri[1], rj[1]], [ri[2], rj[2]], color=cm(value), zorder=1 + value, alpha=0.5) end end end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	321	module QuanticsTCI using TensorCrossInterpolation import TensorCrossInterpolation as TCI import QuanticsGrids as QG import LinearAlgebra: rank import Base: sum export quanticscrossinterpolate, evaluate, sum, integral export cachedata, quanticsfouriermpo include("tciinterface.jl") include("fouriertransform.jl") end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	5034	module fourierimpl import TensorCrossInterpolation as TCI struct LagrangePolynomials{T} grid::Vector{T} baryweights::Vector{T} end function (P::LagrangePolynomials{T})(alpha::Int, x::T)::T where {T} if abs(x - P.grid[alpha+1]) >= 1e-14 return prod(x .- P.grid) * P.baryweights[alpha+1] / (x - P.grid[alpha+1]) else return one(T) end end function getChebyshevGrid(K::Int)::LagrangePolynomials{Float64} chebgrid = 0.5 * (1.0 .- cospi.((0:K) / K)) baryweights = [ prod(j == m ? 1.0 : 1.0 / (chebgrid[j+1] - chebgrid[m+1]) for m in 0:K) for j in 0:K ] return LagrangePolynomials{Float64}(chebgrid, baryweights) end function dftcoretensor( P::LagrangePolynomials{Float64}, alpha::Int, beta::Int, sigma::Int, tau::Int; sign::Float64 )::ComplexF64 x = (sigma + P.grid[beta+1]) / 2 return P(alpha, x) * cispi(2 * sign * x * tau) end end @doc raw""" function quanticsfouriermpo( R::Int; sign::Float64=-1.0, maxbonddim::Int=12, tolerance::Float64=1e-14, K::Int=25, method::Symbol=:SVD, normalize::Bool=true )::TCI.TensorTrain{ComplexF64} Generate a quantics Fourier transform operator in tensor train form. When contracted with a quantics tensor train ``F_{\boldsymbol{\sigma}}`` representing a function, the result will be the fourier transform of the function in quantics tensor train form, ``\tilde{F}_{\boldsymbol{\sigma}'} = \sum_{\boldsymbol{\sigma}} F_{\boldsymbol{\sigma}} \exp(-2\pi i (k_{\boldsymbol{\sigma'}}-1) (m_{\boldsymbol{\sigma}} - 1)/M)``, where ``k_{\boldsymbol{\sigma}} = \sum_{\ell=1}^R 2^{R-\ell} \sigma_\ell``, ``m_{\boldsymbol{\sigma}'} = \sum_{\ell=1}^R 2^{R-\ell} \sigma'_\ell``, and ``M=2^R``. !!! note "Index ordering" Before the Fourier transform, the left most index corresponds to ``\sigma_1``, which describes the largest length scale, and the right most index corresponds to ``\sigma_R``, which describes the smallest length scale. The indices ``\sigma_1' \ldots \sigma_{R}'`` in the fourier transformed QTT are aligned in the inverse order; that is, the left most index corresponds to ``\sigma'_R``, which describes the smallest length scale. This allows construction of an operator with small bond dimension (see reference 1). If necessary, a call to `TCI.reverse(tt)` can restore large-to-small index ordering. The Fourier transform operator is implemented using a direct analytic construction of the tensor train by Chen and Lindsey (see reference 2). The tensor train thus obtained is then re-compressed to the user-given bond dimension and tolerance. Arguments: - `R`: number of bits of the fourier transform. - `sign`: sign in the exponent ``\exp(2i\pi \times \mathrm{sign} \times (k_{\boldsymbol{\sigma'}}-1) (x_{\boldsymbol{\sigma}}-1)/M)``, usually ``\pm 1``. - `maxbonddim`: bond dimension to compress the operator to. From observations, `maxbonddim = 12` is generally big enough to reach an accuracy of `1e-12`. - `tolerance`: tolerance of the TT compression. Note that the error in the fourier transform is generally a bit larger than this error tolerance. - `K`: bond dimension of the TT before compression, i.e. number of basis functions to approximate the Fourier transform with (see reference 2). The TT will become inaccurate for `K < 22`; higher values may be necessary for very high precision. - `method`: method with which to compress the TT. Choose between `:SVD` and `:CI`. - `normalize`: whether or not to normalize the operator as an isometry. !!! details "References" 1. [J. Chen, E. M. Stoudenmire, and S. R. White, Quantum Fourier Transform Has Small Entanglement, PRX Quantum 4, 040318 (2023).](https://link.aps.org/doi/10.1103/PRXQuantum.4.040318) 2. [J. Chen and M. Lindsey, Direct Interpolative Construction of the Discrete Fourier Transform as a Matrix Product Operator, arXiv:2404.03182.](http://arxiv.org/abs/2404.03182) """ function quanticsfouriermpo( R::Int; sign::Float64=-1.0, tolerance::Float64=1e-14, maxbonddim::Int=12, K::Int=25, method::Symbol=:SVD, normalize::Bool=true )::TCI.TensorTrain{ComplexF64} P = fourierimpl.getChebyshevGrid(K) A = [ fourierimpl.dftcoretensor(P, alpha, beta, sigma, tau; sign) for alpha in 0:K, tau in [0, 1], sigma in [0, 1], beta in 0:K ] Afirst = reshape(sum(A, dims=1), (1, 2, 2, K + 1)) Alast = reshape(A[:, :, :, 1], (K + 1, 2, 2, 1)) tt = TCI.TensorTrain{ComplexF64,4}([Afirst, fill(A, R - 2)..., Alast]) TCI.compress!(tt, method; tolerance, maxbonddim) if normalize for t in tt.sitetensors t ./= sqrt(2.0) end end return tt end function swaphalves!(tt::TCI.TensorTrain{V,3}) where {V} tt.sitetensors[1][:, :, :] = tt.sitetensors[1][:, [2, 1], :] nothing end function swaphalves(tt::TCI.AbstractTensorTrain{V}) where {V} ttcopy = TCI.tensortrain(deepcopy(TCI.sitetensors(tt))) swaphalves!(ttcopy) return ttcopy end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	10176	struct QuanticsTensorCI2{ValueType} tci::TensorCrossInterpolation.TensorCI2{ValueType} grid::QG.Grid quanticsfunction::TCI.CachedFunction{ValueType} end function evaluate( qtci::QuanticsTensorCI2{ValueType}, indices::Union{Array{Int},NTuple{N,Int}} )::ValueType where {N,ValueType} bitlist = QG.grididx_to_quantics(qtci.grid, Tuple(indices)) return TensorCrossInterpolation.evaluate(qtci.tci, bitlist) end function evaluate(qtci::QuanticsTensorCI2{V}, indices::Int...)::V where {V} return evaluate(qtci, collect(indices)::Vector{Int}) end function (qtci::QuanticsTensorCI2{V})(indices)::V where {V} return evaluate(qtci, indices) end function (qtci::QuanticsTensorCI2{V})(indices::Int...)::V where {V} return evaluate(qtci, indices...) end function sum(qtci::QuanticsTensorCI2{V})::V where {V} return sum(qtci.tci) end function integral(qtci::QuanticsTensorCI2{V})::V where {V} return sum(qtci) * prod(QG.grid_step(qtci.grid)) end function cachedata(qtci::QuanticsTensorCI2{V}) where {V} return Dict( QG.quantics_to_origcoord(qtci.grid, k) => v for (k, v) in TCI.cachedata(qtci.quanticsfunction) ) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, grid::QuanticsGrids.Grid{n}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; nrandominitpivot=5, kwargs... ) where {ValueType} Interpolate a function ``f(\mathbf{x})`` as a quantics tensor train. The tensor train itself is constructed using the 2-site tensor cross interpolation algorithm implemented in [`TensorCrossInterpolation.crossinterpolate2`](https://tensor4all.github.io/TensorCrossInterpolation.jl/dev/documentation/#TensorCrossInterpolation.crossinterpolate2-Union{Tuple{N},%20Tuple{ValueType},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}}},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}},%20Vector{Vector{Int64}}}}%20where%20{ValueType,%20N}). Arguments: - `ValueType` is the return type of `f`. Automatic inference is too error-prone. - `f` is the function to be interpolated. `f` may take multiple arguments. The return type should be `ValueType`. - `grid` is a `Grid{n}` object from [`QuanticsGrids.jl`](https://github.com/tensor4all/QuanticsGrids.jl) that describes a d-dimensional grid of discrete points indexed by binary digits. To avoid constructing a grid explicitly, use one of the other overloads. - `initialpivots` is a vector of pivots to be used for initialization. - `nrandominitpivot` determines how many random pivots should be used for initialization if no initial pivot is given. All other arguments are forwareded to `crossinterpolate2`. Most importantly: - `tolerance::Float64` is a float specifying the target tolerance for the interpolation. Default: `1e-8`. - `pivottolerance::Float64` is a float that specifies the tolerance for adding new pivots, i.e. the truncation of tensor train bonds. It should be <= tolerance, otherwise convergence may be impossible. Default: `tolerance`. - `maxbonddim::Int` specifies the maximum bond dimension for the TCI. Default: `typemax(Int)`, i.e. effectively unlimited. - `maxiter::Int` is the maximum number of iterations (i.e. optimization sweeps) before aborting the TCI construction. Default: `200`. For all other arguments, see the documentation for [`TensorCrossInterpolation.crossinterpolate2`](https://tensor4all.github.io/TensorCrossInterpolation.jl/dev/documentation/#TensorCrossInterpolation.crossinterpolate2-Union{Tuple{N},%20Tuple{ValueType},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}}},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}},%20Vector{Vector{Int64}}}}%20where%20{ValueType,%20N}). """ function quanticscrossinterpolate( ::Type{ValueType}, f, grid::QG.Grid{n}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; nrandominitpivot=5, kwargs... ) where {ValueType,n} R = grid.R qlocaldimensions = if grid.unfoldingscheme === :interleaved fill(2, n * R) else fill(2^n, R) end qf_ = (n == 1 ? q -> f(only(QG.quantics_to_origcoord(grid, q))) : q -> f(QG.quantics_to_origcoord(grid, q)...)) qf = TCI.CachedFunction{ValueType}(qf_, qlocaldimensions) qinitialpivots = (initialpivots === nothing ? [ones(Int, length(qlocaldimensions))] : [QG.grididx_to_quantics(grid, Tuple(p)) for p in initialpivots]) # For stabity kwargs_ = Dict{Symbol,Any}(kwargs) if !(:nsearchglobalpivot ∈ keys(kwargs)) kwargs_[:nsearchglobalpivot] = 5 end if !(:strictlynested ∈ keys(kwargs)) kwargs_[:strictlynested] = false end # random initial pivot for _ in 1:nrandominitpivot pivot = [rand(1:d) for d in qlocaldimensions] push!( qinitialpivots, TensorCrossInterpolation.optfirstpivot(qf, qlocaldimensions, pivot) ) end qtt, ranks, errors = TensorCrossInterpolation.crossinterpolate2( ValueType, qf, qlocaldimensions, qinitialpivots; kwargs_...) return QuanticsTensorCI2{ValueType}(qtt, grid, qf), ranks, errors end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector{<:AbstractVector}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; unfoldingscheme::Symbol=:interleaved, nrandominitpivot=5, kwargs... ) where {ValueType} Interpolate a function ``f(\mathbf{x})`` as a quantics tensor train. This overload automatically constructs a Grid object from the ``\mathbf{x}`` points given in `xvals`. Arguments: - `xvals::AbstractVector{<:AbstractVector}`: A set of discrete points where `f` can be evaluated, given as a set of arrays, where `xvals[i]` describes the `i`th axis. Each array in `xvals` should contain `2^R` points for some integer `R`. - For all other arguments, see the documentation of the main overload. """ function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector{<:AbstractVector}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; unfoldingscheme::Symbol=:interleaved, nrandominitpivot=5, kwargs... ) where {ValueType} localdimensions = log2.(length.(xvals)) if !allequal(localdimensions) throw(ArgumentError( "This method only supports grids with equal number of points in each direction. If you need a different grid, please use index_to_quantics and quantics_to_index and determine the index ordering yourself.")) elseif !all(isinteger.(localdimensions)) throw(ArgumentError("This method only supports grid sizes that are powers of 2.")) end n = length(localdimensions) R = Int(first(localdimensions)) grid = QG.DiscretizedGrid{n}(R, Tuple(minimum.(xvals)), Tuple(maximum.(xvals)); unfoldingscheme=unfoldingscheme, includeendpoint=true) return quanticscrossinterpolate(ValueType, f, grid, initialpivots; nrandominitpivot=nrandominitpivot, kwargs...) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector, initialpivots::AbstractVector=[1]; kwargs... ) where {ValueType} Interpolate a function ``f(x)`` as a quantics tensor train. This is an overload for 1d functions. For an explanation of arguments and return type, see the documentation of the main overload. """ function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector, initialpivots::AbstractVector=[1]; nrandominitpivot=5, kwargs... ) where {ValueType} return quanticscrossinterpolate( ValueType, f, [xvals], [initialpivots]; nrandominitpivot=nrandominitpivot, kwargs...) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, size::NTuple{d,Int}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; unfoldingscheme::Symbol=:interleaved, kwargs... ) where {ValueType,d} Interpolate a function ``f(\mathbf{x})`` as a quantics tensor train. This overload automatically constructs a Grid object using the information contained in `size`. Here, the `i`th argument runs from `1` to `size[i]`. """ function quanticscrossinterpolate( ::Type{ValueType}, f, size::NTuple{d,Int}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; unfoldingscheme::Symbol=:interleaved, kwargs... ) where {ValueType,d} localdimensions = log2.(size) if !allequal(localdimensions) throw(ArgumentError( "This method only supports grids with equal number of points in each direction. If you need a different grid, please use index_to_quantics and quantics_to_index and determine the index ordering yourself.")) elseif !all(isinteger.(localdimensions)) throw(ArgumentError("This method only supports grid sizes that are powers of 2.")) end R = Int(first(localdimensions)) grid = QG.InherentDiscreteGrid{d}(R; unfoldingscheme=unfoldingscheme) return quanticscrossinterpolate(ValueType, f, grid, initialpivots; kwargs...) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, size::NTuple{d,Int}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; unfoldingscheme::Symbol=:interleaved, kwargs... ) where {ValueType,d} Interpolate a Tensor ``F`` as a quantics tensor train. For an explanation of arguments, etc., see the documentation of the main overload. """ function quanticscrossinterpolate( F::Array{ValueType,d}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; kwargs... ) where {ValueType,d} return quanticscrossinterpolate( ValueType, (i...) -> F[i...], size(F), initialpivots; kwargs...) end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	208	using QuanticsTCI using Test using LinearAlgebra include("test_with_aqua.jl") include("test_with_jet.jl") include("test_tciinterface.jl") include("test_fouriertransform.jl") include("test_samplescripts.jl")	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	857	import QuanticsTCI as QTCI import TensorCrossInterpolation as TCI import QuanticsGrids as QG import Random @testset "Quantics Fourier Transform, R=$R" for R in [4, 16, 62] Random.seed!(23593243) r = 12 coeffs = randn(ComplexF64, r) fm(x) = sum(coeffs .* cispi.(2 * (0:r-1) * x)) fq(q) = fm((QG.quantics_to_index_fused(q)[1] - 1) / 2^big(R)) qtci, = TCI.crossinterpolate2(ComplexF64, fq, fill(2, R); tolerance=1e-14) fouriertt = QTCI.quanticsfouriermpo(R; normalize=false) / 2^big(R) qtcif = TCI.contract(fouriertt, qtci) for i in 1:min(r, 2^big(R)) q = QG.index_to_quantics(i, numdigits=R) @test qtcif(reverse(q)) ≈ coeffs[i] end for i in Int.(round.(range(r+2, 2^big(R); length=100))) q = QG.index_to_quantics(i, numdigits=R) @test abs(qtcif(reverse(q))) < 1e-12 end end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	890	using Test using Glob # Directory where the script files are located script_dir = joinpath(@__DIR__, "samplescripts") # Get all .jl files in the directory script_files = glob("*.jl", script_dir) # Function to execute a script using a new Julia process function run_script_in_new_process(script_file) cmd = `julia --project=@. --startup-file=no $script_file` result = read(cmd, String) # Run the command and capture the output return result end # Test each script file @testset "External Script Tests" begin for script_file in script_files @testset "Testing $script_file" begin try run_script_in_new_process(script_file) @test true # If no error occurs catch e @test false # If an error occurs println("Error in $script_file: ", e) end end end end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	3177	using Random import QuanticsGrids as QG @testset "quanticscrossinterpolate" for unfoldingscheme in [ :interleaved, :fused ] f(x, y) = 0.1 * x^2 + 0.01 * y^3 - pi * x * y + 5 xvals = range(-3, 2; length=32) yvals = range(-17, 12; length=32) qtt, ranks, errors = quanticscrossinterpolate( Float64, f, [xvals, yvals]; unfoldingscheme, tolerance=1e-8) @test last(errors) < 1e-8 cache = cachedata(qtt) for (k, v) in cache @test v ≈ f(k...) end for (i, x) in enumerate(xvals) for (j, y) in enumerate(yvals) @test f(x, y) ≈ qtt(i, j) end end @test sum(qtt) ≈ sum(f.(xvals, transpose(yvals))) end @testset "quanticscrossinterpolate, 1d overload" begin f(x) = 0.1 * x^2 - pi * x + 2 g(x) = f(x[1]) xvals = range(-3, 2; length=128) Random.seed!(1234) qttf, ranksf, errorsf = quanticscrossinterpolate(Float64, f, xvals; tolerance=1e-8) Random.seed!(1234) qttg, ranksg, errorsg = quanticscrossinterpolate(Float64, g, [xvals]; tolerance=1e-8) @test last(errorsf) < 1e-8 @test last(errorsg) < 1e-8 @test ranksf == ranksg @test errorsf == errorsg for (i, x) in enumerate(xvals) @test f(x) == g([x]) @test f(x) ≈ qttf(i) @test g([x]) ≈ qttg([i]) end end @testset "quanticscrossinterpolate with DiscretizedGrid" for unfoldingscheme in [ :interleaved, :fused ] R = 5 f(x, y) = 0.1 * x^2 + 0.01 * y^3 - pi * x * y + 5 grid = QG.DiscretizedGrid{2}( R, (-3, -17), (2, 12); unfoldingscheme ) Random.seed!(1234) qtt, ranks, errors = quanticscrossinterpolate(Float64, f, grid; tolerance=1e-8) @test last(errors) < 1e-8 for i in 1:2^R for j in 1:2^R @test f(QG.grididx_to_origcoord(grid, (i, j))...) ≈ qtt(i, j) end end end @testset "quanticscrossinterpolate with InherentDiscreteGrid" for unfoldingscheme in [ :interleaved, :fused ] R = 3 Random.seed!(1234) A = rand(2^R, 2^R, 2^R) grid = QG.InherentDiscreteGrid{3}(R; unfoldingscheme) qtt, ranks, errors = quanticscrossinterpolate( Float64, (i...) -> A[i...], grid; tolerance=1e-8) @test last(errors) < 1e-8 for i in CartesianIndices(size(A)) @test A[i] ≈ qtt(Tuple(i)) end qtt, ranks, errors = quanticscrossinterpolate( Float64, (i...) -> A[i...], size(A); unfoldingscheme, tolerance=1e-8) @test last(errors) < 1e-8 for i in CartesianIndices(size(A)) @test A[i] ≈ qtt(Tuple(i)) end qtt, ranks, errors = quanticscrossinterpolate( A; unfoldingscheme, tolerance=1e-8) @test last(errors) < 1e-8 for i in CartesianIndices(size(A)) @test A[i] ≈ qtt(Tuple(i)) end end @testset "quanticscrossinterpolate for integrals" begin R = 40 xgrid = QG.DiscretizedGrid{1}(R, 0, 1) F(x) = sin(1/(x^2 + 0.01)) f(x) = -2x cos(1/(x^2 + 0.01)) / (x^2 + 0.01)^2 tci, ranks, errors = quanticscrossinterpolate(Float64, f, xgrid; tolerance=1e-13) @test sum(tci) * QG.grid_step(xgrid) ≈ F(1) - F(0) @test integral(tci) ≈ F(1) - F(0) end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	152	using Aqua import QuanticsTCI @testset "Aqua" begin Aqua.test_all(QuanticsTCI; ambiguities = false, unbound_args = false, deps_compat = false) end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	154	using JET import QuanticsTCI @testset "JET" begin if VERSION ≥ v"1.9" JET.test_package(QuanticsTCI; target_defined_modules=true) end end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	970	using QuanticsTCI import QuanticsGrids as QG R = 40 # Number of bits <@$\cR$@> M = 2^R # Number of discretization points <@$M$@> xgrid = QG.DiscretizedGrid{1}(R, -10, 10) # Discretization grid <@$x(\bsigma)$@> function f(x) # Function of interest <@$f(x)$@> return ( sinc(x) + 3 * exp(-0.3 * (x - 4)^2) * sinc(x - 4) - cos(4 * x)^2 - 2 * sinc(x + 10) * exp(-0.6 * (x + 9)) + 4 * cos(2 * x) * exp(-abs(x + 5)) + 6 * 1 / (x - 11) + sqrt(abs(x)) * atan(x / 15)) end # Construct and optimize quantics TCI <@$\tf_\bsigma$@> f_tci, ranks, errors = quanticscrossinterpolate(Float64, f, xgrid; maxbonddim=12) # Print a table to compare <@$f(x)$@> and <@$\tF_\bsigma$@> on some regularly spaced points println("x\t f(x)\t\t\t f_tt(x)") for m in 1:2^(R-5):M x = QG.grididx_to_origcoord(xgrid, m) println("$x\t$(f(x))\t$(f_tci(m))") end	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	851	using QuanticsTCI import QuanticsGrids as QG R = 40 # Number of bits <@$\cR$@> xygrid = QG.DiscretizedGrid{2}(R, (-5.0, -5.0), (5.0, 5.0)) # Discretization grid <@$\vec{x}(\bsigma)$@> function f(x, y) # Function of interest <@$f(x)$@> return exp(-0.4(x^2 + y^2)) + 1 + sin(x y) * exp(-x^2) + cos(3xy) * exp(-y ^ 2) + cos(x+y) end # Construct and optimize quantics TCI <@$\tf_\bsigma$@> f_tci, ranks, errors = quanticscrossinterpolate(Float64, f, xygrid; tolerance=1e-10) # Print a table to compare <@$f(x)$@> and <@$\tF_\bsigma$@> on some regularly spaced points println("x\t y\t f(x)\t\t\t f_tt(x)") for index in CartesianIndices((10, 10)) m = Tuple(index) .* div(2^R, 10) x, y = QG.grididx_to_origcoord(xygrid, m) println("$x\t$y\t$(f(x, y))\t$(f_tci(m))") end println("Value of the integral: $(integral(f_tci))")	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	710	using QuanticsTCI import TensorCrossInterpolation as TCI # Number of bits R = 8 # Replace with your dataset grid = range(-pi, pi; length=2^R+1)[1:end-1] # exclude the end point dataset = [cos(x) + cos(y) + cos(z) for x in grid, y in grid, z in grid] # Perform QTCI tolerance = 1e-5 qtt, ranks, errors = quanticscrossinterpolate( dataset, tolerance=tolerance, unfoldingscheme=:fused) # Check error qttdataset = [qtt([i, j, k]) for i in axes(grid, 1), j in axes(grid, 1), k in axes(grid, 1)] error = abs.(qttdataset .- dataset) println( "Quantics TCI compression of the dataset with tolerance $tolerance has " * "link dimensions $(TCI.linkdims(qtt.tci)), for a max error of $(maximum(error))." )	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	666	import TensorCrossInterpolation as TCI # Replace this line with the dataset to be tested for compressibility. grid = range(-pi, pi; length=200) dataset = [cos(x) + cos(y) + cos(z) for x in grid, y in grid, z in grid] # Construct TCI tolerance = 1e-5 tt, ranks, errors = TCI.crossinterpolate2( Float64, i -> dataset[i...], collect(size(dataset)), tolerance=tolerance) # Check error ttdataset = [tt([i, j, k]) for i in axes(grid, 1), j in axes(grid, 1), k in axes(grid, 1)] errors = abs.(ttdataset .- dataset) println( "TCI of the dataset with tolerance $tolerance has link dimensions $(TCI.linkdims(tt)), " * "for a max error of $(maximum(errors))." )	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	1583	import TensorCrossInterpolation as TCI import Random import QuanticsGrids as QD #using PythonPlot: pyplot as plt # Number of bits R = 4 tol = 1e-4 # f(q) = 1 if q = (1, 1, ..., 1) or q = (2, 2, ..., 2), 0 otherwise f(q) = (all(q .== 1) \|\| all(q .== 2)) ? 1.0 : 0.0 localdims = fill(2, R) # Perform TCI with an initial pivot at (1, 1, ..., 1) firstpivot = ones(Int, R) tci, ranks, errors = TCI.crossinterpolate2( Float64, f, localdims, [firstpivot]; tolerance=tol, nsearchglobalpivot=0 # Disable automatic global pivot search ) # TCI fails to capture the function at (2, 2, ..., 2) globalpivot = fill(2, R) @assert isapprox(TCI.evaluate(tci, globalpivot), 0.0) # Add (2, 2, ..., 2) as a global pivot tci_globalpivot = deepcopy(tci) TCI.addglobalpivots2sitesweep!( tci_globalpivot, f, [globalpivot], tolerance=tol ) @assert isapprox(TCI.evaluate(tci_globalpivot, globalpivot), 1.0) # Plot the function and the TCI reconstructions grid = QD.InherentDiscreteGrid{1}(R) ref = [f(QD.grididx_to_quantics(grid, i)) for i in 1:2^R] reconst_tci = [tci(QD.grididx_to_quantics(grid, i)) for i in 1:2^R] reconst_tci_globalpivot = [tci_globalpivot(QD.grididx_to_quantics(grid, i)) for i in 1:2^R] #== fig, ax = plt.subplots() ax.plot(ref, label="ref", marker="", linestyle="--") ax.plot(reconst_tci, label="TCI without global pivot", marker="x", linestyle="") ax.plot(reconst_tci_globalpivot, label="TCI with global pivot", marker="+", linestyle="") ax.set_title("Adding global pivot") ax.set_xlabel("Index") ax.legend() fig.savefig("global_pivot.pdf") ==#	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	562	import TensorCrossInterpolation as TCI N = 5 # Number of dimensions <@$\cN$@> tolerance = 1e-10 # Tolerance of the internal TCI GKorder = 15 # Order of the Gauss-Kronrod rule to use f(x) = 2^N / (1 + 2 * sum(x)) # Integrand integralvalue = TCI.integrate(Float64, f, fill(0.0, N), fill(1.0, N); tolerance, GKorder) # Exact value of integral for <@$\cN = 5$@> i5 = (-65205 * log(3) - 6250 * log(5) + 24010 * log(7) + 14641 * log(11)) / 24 error = abs(integralvalue - i5) @info "TCI integration with GK$GKorder: " integralvalue i5 error	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	code	1077	import QuanticsGrids as QG import TensorCrossInterpolation as TCI N = 5 # Number of dimensions <@$\cN$@> tolerance = 1e-10 # Tolerance of the internal TCI R = 40 # Number of bits <@$\cR$@> f(x) = 2^N / (1 + 2 * sum(x)) # Integrand <@$f(\vec{x})$@> # Discretization grid with <@$2^{\scN \scR}$@> points grid = QG.DiscretizedGrid{N}(R, Tuple(fill(0.0, N)), Tuple(fill(1.0, N)), unfoldingscheme=:interleaved) quanticsf(sigma) = f(QG.quantics_to_origcoord(grid, sigma)) # <@$f(\vec{x}(\bsigma))$@> # Obtain the QTCI representation and evaluate the integral via factorized sum tci, ranks, errors = TCI.crossinterpolate2(Float64, quanticsf, QG.localdimensions(grid); tolerance) # Integral is sum multiplied with discretization volumne integralvalue = TCI.sum(tci) * prod(QG.grid_step(grid)) # Exact value of integral for <@$\cN = 5$@> i5 = (-65205 * log(3) - 6250 * log(5) + 24010 * log(7) + 14641 * log(11)) / 24 error = abs(integralvalue - i5) # Error for <@$\cN = 5$@> @info "Quantics TCI integration with R=$R: " integralvalue i5 error	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	docs	3534	# QuanticsTCI [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://tensor4all.github.io/QuanticsTCI.jl/dev) [![CI](https://github.com/tensor4all/QuanticsTCI.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/tensor4all/QuanticsTCI.jl/actions/workflows/CI.yml) This module contains utilities for interpolations of functions in the quantics TCI / quantics tensor train (QTT) format. It is a small wrapper around [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl) and [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) with more convenient functionality intended to cover the most common use cases. For more advanced or unusual use cases, it is likely that you will need to rely on those two libraries directly. ## Installation This module has been registered in the General registry. It can be installed by typing the following in a Julia REPL: ```julia using Pkg; Pkg.add("QuanticsTCI.jl") ``` This module depends on: - [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl) - [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) ## Usage This section only contains the bare minimum to get you started. More examples, including more advanced use cases, can be found in the [the tensor4all website](https://tensor4all.github.io). For a documentation of the API, see the [package documentation](https://tensor4all.github.io/QuanticsTCI.jl/dev/index.html). The easiest way to construct a quantics tensor train is the `quanticscrossinterpolate` function. For example, the function `f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y)` can be interpolated as follows. ```julia using QuanticsTCI f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y) xvals = range(-6, 6; length=256) yvals = range(-12, 12; length=256) qtt, ranks, errors = quanticscrossinterpolate(Float64, f, [xvals, yvals]; tolerance=1e-8) ``` The output object `qtt` now represents a quantics tensor train. It can then be evaluated a function of indices enumerating the `xvals` and `yvals` arrays: ```julia @show qttvalue = qtt(212, 92) @show truevalue = f(xvals[212], yvals[92]) @show error = abs(qttvalue - truevalue) ``` Output: ``` qttvalue = qtt(212, 92) = -0.2525252152789011 truevalue = f(xvals[212], yvals[92]) = -0.2525252152789314 error = abs(qttvalue - truevalue) = 3.0309088572266774e-14 ``` The output shows that the approximation has an error of only `3 * 10^-14` at `[212, 92]`. This example is continued in the [package documentation](https://tensor4all.github.io/QuanticsTCI.jl/dev/index.html), and more examples can be found in the [the tensor4all website](https://tensor4all.github.io). ## Related libraries - [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl) to calculate tensor cross interpolations. - [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library. - [ITensors.jl](https://github.com/ITensor/ITensors.jl) for MPS / MPO algorithms. ## References - M. K. Ritter, Y. N. Fernández, M. Wallerberger, J. von Delft, H. Shinaoka, and X. Waintal, Quantics Tensor Cross Interpolation for High-Resolution, Parsimonious Representations of Multivariate Functions in Physics and Beyond, [arXiv:2303.11819](http://arxiv.org/abs/2303.11819), [Phys. Rev. Lett. 132, 056501 (2024)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.056501).	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	docs	57	# Documentation ```@autodocs Modules = [QuanticsTCI] ```	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	0.7.0	2724301b6bc6390d8d43d210cecd7dbaabe519d5	docs	3435	# QuanticsTCI.jl user guide ```@meta CurrentModule = QuanticsTCI ``` This module allows easy translation of functions to quantics representation. It meshes well with the `TensorCrossInterpolation.jl` module, together with which it provides quantics TCI functionality. # Quickstart The easiest way to construct a quantics tensor train is the `quanticscrossinterpolate` function. For example, the function ``f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y)`` can be interpolated as follows. ```@example simple using QuanticsTCI f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y) xvals = range(-6, 6; length=256) yvals = range(-12, 12; length=256) qtt, ranks, errors = quanticscrossinterpolate(Float64, f, [xvals, yvals]; tolerance=1e-8) ``` The output object `qtt` now represents a quantics tensor train. It can then be evaluated a function of indices enumerating the `xvals` and `yvals` arrays. ```@example simple using Plots qttvals = qtt.(1:256, collect(1:256)') contour(xvals, yvals, qttvals, fill=true) xlabel!("x") ylabel!("y") savefig("simple.svg"); nothing # hide ``` ![](simple.svg) The convergence criterion can be controlled using the keywords `tolerance`, `pivottolerance`, and `maxbonddim`. - `tolerance` is the value of the error estimate at which the optimization algorithm will stop. - `pivottolerance` is the threshold at which each local optimization will truncate the bond. - `maxbonddim` sets the maximum bond dimension along the links. A common default setting is to control convergence using `tolerance`, and to set `pivottolerance` equal or slightly smaller than that. Specifying `maxbonddim` can be useful as a safety. However, if `maxbonddim` is set, one should check the error estimate for convergence afterwards. In the following example, we specify all 3 parameters, but set `maxbonddim` too small. ```@example simple qtt, ranks, errors = quanticscrossinterpolate( Float64, f, [xvals, yvals]; tolerance=1e-8, pivottolerance=1e-8, maxbonddim=8) print(last(errors)) qttvals = qtt.(1:256, collect(1:256)') contour(xvals, yvals, qttvals, fill=true) xlabel!("x") ylabel!("y") savefig("simpletrunc.svg"); nothing # hide ``` ![](simpletrunc.svg) The plot shows obvious noise due to the insufficient maximum bond dimension. Accordingly, the error estimate of ``0.08`` shows that convergence has not been reached, and an increase of the maximum bond dimension is necessary. # Further reading - See the API Reference for all variants of calling [`quanticscrossinterpolate`](@ref). - If you are having trouble with convergence / efficiency of the TCI, you might have to tweak some of its options. All keyword arguments are forwarded to `TensorCrossInterpolation.crossinterpolate2()` internally. See its [documentation](https://tensor4all.github.io/TensorCrossInterpolation.jl/dev/documentation/#TensorCrossInterpolation.crossinterpolate2-Union{Tuple{N},%20Tuple{ValueType},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}}},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}},%20Vector{Vector{Int64}}}}%20where%20{ValueType,%20N}) for further information. - If you intend to work directly with the quantics representation, [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) is useful for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library.	QuanticsTCI	https://github.com/tensor4all/QuanticsTCI.jl.git
[ "MIT" ]	2.0.0	d50c73e4abd8f7c58eb76a8884dfd531fa8dac81	code	1381	function check_hsg() assert_clean_working_directory() run_hsg() assert_clean_working_directory() return nothing end function assert_clean_working_directory() if !isempty(strip(read(`git status --short`, String))) msg = "The working directory is dirty" @error msg println("Output of `git status`:") println(strip(read(`git status`, String))) run(`git add -A`) println("Output of `git diff HEAD`:") println(strip(read(`git diff HEAD`, String))) run(`git reset`) throw(ErrorException(msg)) else @info "The working directory is clean" return nothing end end function run_hsg() env2 = copy(ENV) delete!(env2, "JULIA_DEPOT_PATH") delete!(env2, "JULIA_LOAD_PATH") delete!(env2, "JULIA_PROJECT") env2["JULIA_DEPOT_PATH"] = mktempdir(; cleanup = true) julia_binary = Base.julia_cmd().exec[1] hsg_directory = joinpath("ext", "HistoricalStdlibGenerator") hsg_generate_file = joinpath(hsg_directory, "generate_historical_stdlibs.jl") cmd_1 = `$(julia_binary) --project=$(hsg_directory) -e 'import Pkg; Pkg.instantiate()'` cmd_2 = `$(julia_binary) --project=$(hsg_directory) --threads $(min(Sys.CPU_THREADS, 8)) $(hsg_generate_file)` run(setenv(cmd_1, env2)) run(setenv(cmd_2, env2)) return nothing end check_hsg()	HistoricalStdlibVersions	https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git
[ "MIT" ]	2.0.0	d50c73e4abd8f7c58eb76a8884dfd531fa8dac81	code	11224	#!/usr/bin/env julia using Downloads, JSON3, Base.BinaryPlatforms, Scratch, SHA, Pkg, TOML using Base: UUID # Download versions.json, start iterating over Julia versions versions_json_url = "https://julialang-s3.julialang.org/bin/versions.json" num_concurrent_downloads = 8 @info("Downloading versions.json...") json_buff = IOBuffer() Downloads.download(versions_json_url, json_buff) versions_json = JSON3.read(String(take!(json_buff))) # Collect all versions that are >= 1.0.0, and are a stable release versions = filter(versions_json) do (v, d) if VersionNumber(string(v)) < v"1.0.0" return false end if !d["stable"] return false end return true end # Build download URLs for each one, and then tack on the next release as well function select_url_hash(data, host = HostPlatform()) d = first(filter(f -> platforms_match(f.triplet, host), data.files)) return (d.url, d.sha256) end version_urls = sort(select_url_hash.(values(versions)), by = pair -> pair[1]) function generate_nightly_url(jlver, host = HostPlatform()) # Map arch arch_str = Dict("x86_64" => "x64", "i686" => "x86", "aarch64" => "aarch64", "armv7l" => "armv7l", "ppc64le" => "ppc64le")[arch(host)] # Map OS name os_str = Dict("linux" => "linux", "windows" => "winnt", "macos" => "mac", "freebsd" => "freebsd")[os(host)] # Map wordsize tag wordsize_str = Dict("x86_64" => "64", "i686" => "32", "aarch64" => "aarch64", "armv7l" => "armv7l", "ppc64le" => "ppc64")[arch(host)] # If `jlver` is nothing, we don't namespace by version and just get the absolute latest version ver_str = "" if jlver !== nothing ver_str = string(jlver.major, ".", jlver.minor, "/") end return string( "https://julialangnightlies-s3.julialang.org/bin/", # linux/ os_str, "/", # x64/ arch_str, "/", # 1.6/ (or nothing, if `jlver === nothing`) ver_str, "julia-latest-", # linux64 os_str, wordsize_str, ".tar.gz", ) end highest_release = maximum(VersionNumber.(string.(keys(versions)))) next_release = VersionNumber(highest_release.major, highest_release.minor + 1, 0) push!(version_urls, (generate_nightly_url(next_release), "")) push!(version_urls, (generate_nightly_url(nothing), "")) @info("Identified $(length(version_urls)) versions to try...") # Next, we're going to download each of these to a scratch space scratch_dir = @get_scratch!("julia_installers") # Ensure we always download the nightly nightly_url = last(version_urls)[1] nightly_url_tag = bytes2hex(sha256(nightly_url)) rm(joinpath(scratch_dir, string(nightly_url_tag, "-", basename(nightly_url))); force=true) # Helper function to print out stdlibs from a Julia installation function get_stdlibs(scratch_dir, julia_installer_name) installer_path = joinpath(scratch_dir, julia_installer_name) mktempdir() do dir @info("Extracting $(julia_installer_name)") mount_dir = joinpath(dir, "mount_dir") try if endswith(installer_path, ".dmg") mkdir(mount_dir) # Try to mount many times, as this seems to fail randomly mount_cmd = `hdiutil mount $(installer_path) -mountpoint $(mount_dir)` tries = 0 while !success(mount_cmd) if tries > 10 error("Unable to mount via hdiutil!") end sleep(0.1) tries += 1 end app_dir = first(filter(d -> startswith(basename(d), "Julia-"), readdir(mount_dir; join=true))) symlink(joinpath(app_dir, "Contents", "Resources", "julia", "bin"), joinpath(dir, "bin")) elseif endswith(installer_path, ".exe") error("This script doesn't work with `.exe` downloads") else run(`tar -C $(dir) --strip-components=1 -zxf $(installer_path)`) end jlexe = joinpath(dir, "bin", @static Sys.iswindows() ? "julia.exe" : "julia") jlflags = ["--startup-file=no", "-O0"] jlvers = VersionNumber(readchomp(`$(jlexe) $(jlflags) -e 'print(VERSION)'`)) jlvers = VersionNumber(jlvers.major, jlvers.minor, jlvers.patch) @info("Auto-detected Julia version $(jlvers)") if jlvers < v"1.1" stdlibs_str = readchomp(`$(jlexe) $(jlflags) -e 'import Pkg; print(repr(Pkg.Types.gather_stdlib_uuids()))'`) else stdlibs_str = readchomp(`$(jlexe) $(jlflags) -e 'import Pkg; print(repr(Pkg.Types.load_stdlib()))'`) end # This will give us a dictionary of UUID => (name, version, deps, weakdeps) mappings for all standard libraries stdlibs = Dict{Base.UUID, Tuple}() stdlib_path = readchomp(`$(jlexe) $(jlflags) -e 'import Pkg; print(Pkg.Types.stdlib_path(""))'`) stdlib_names = [isa(name, Tuple) ? first(name) : name for (_, name) in eval(Meta.parse(stdlibs_str))] for name in stdlib_names project_path = joinpath(stdlib_path, name, "Project.toml") version = nothing deps = UUID[] weakdeps = UUID[] if isfile(project_path) d = TOML.parsefile(project_path) uuid = Base.UUID(d["uuid"]) if haskey(d, "version") version = VersionNumber(d["version"]) end if haskey(d, "deps") deps = Base.UUID.(values(d["deps"])) end if haskey(d, "weakdeps") weakdeps = Base.UUID.(values(d["weakdeps"])) end end stdlibs[uuid] = (name, version, deps, weakdeps) end return (jlvers, stdlibs) finally # Clean up mounted directories if isdir(mount_dir) unmount_cmd = `hdiutil detach $(mount_dir)` tries = 0 while !success(unmount_cmd) if tries > 10 error("Unable to unmount $(mount_dir)") end sleep(0.1) tries += 1 end end end end end jobs = Channel() output = Channel() versions_dict = Dict() @sync begin # Feeder task Threads.@spawn begin for (url, hash) in version_urls put!(jobs, (url, hash)) end close(jobs) end # Consumer tasks work_tasks = Task[] for _ in 1:Threads.nthreads() task = Threads.@spawn begin for (url, hash) in jobs try # We might try to download two files that have the same basename url_tag = bytes2hex(sha256(url)) fname = joinpath(scratch_dir, string(url_tag, "-", basename(url))) if !isfile(fname) @info("Downloading $(url)") Downloads.download(url, fname) end if !isempty(hash) calc_hash = bytes2hex(open(io -> sha256(io), fname, "r")) if calc_hash != hash @error("Hash mismatch on $(fname); deleting and re-downloading") rm(fname; force=true) Downloads.download(url, fname) calc_hash = bytes2hex(open(io -> sha256(io), fname, "r")) if calc_hash != hash @error("Hash mismatch on $(fname); re-download failed!") continue end end end version, stdlibs = get_stdlibs(scratch_dir, basename(fname)) put!(output, (version, stdlibs)) catch e if isa(e, InterruptException) rethrow() end @error(e, exception=(e, catch_backtrace())) end end end push!(work_tasks, task) end # output-closing thread Threads.@spawn begin wait.(work_tasks) close(output) end # Collector task Threads.@spawn begin for (version, stdlibs) in output versions_dict[version] = stdlibs end end end # Next, drop versions that are the same as the one "before" them: sorted_versions = sort(collect(keys(versions_dict))) versions_to_drop = VersionNumber[] for idx in 2:length(sorted_versions) if versions_dict[sorted_versions[idx-1]] == versions_dict[sorted_versions[idx]] push!(versions_to_drop, sorted_versions[idx]) end end for v in versions_to_drop delete!(versions_dict, v) end # Next, figure out which stdlibs are actually unresolvable, because they've never been registered all_stdlibs = Dict{UUID,Tuple}() for (julia_ver, stdlibs) in versions_dict merge!(all_stdlibs, stdlibs) end registries = Pkg.Registry.reachable_registries() unregistered_stdlibs = filter(all_stdlibs) do (uuid, _) return !any(haskey(reg.pkgs, uuid) for reg in registries) end # Helper function for getting these printed out in a nicely-sorted order function print_sorted(io::IO, d::Dict; indent::Int=0) println(io, "Dict{UUID,StdlibInfo}(") for (uuid, (name, version, deps, weakdeps)) in sort(collect(d), by = kv-> kv[2][1]) println(io, " "^indent, repr(uuid), " => StdlibInfo(\n", " "^(indent + 4), repr(name), ",\n", " "^(indent + 4), repr(uuid), ",\n", " "^(indent + 4), repr(version), ",\n", " "^(indent + 4), repr(deps), ",\n", " "^(indent + 4), repr(weakdeps), ",\n", " "^indent, "),", ) end print(io, " "^(max(indent - 4, 0)), ")") end output_fname = joinpath(dirname(dirname(@__DIR__)), "src", "version_map.jl") @info("Outputting to $(output_fname)") sorted_versions = sort(collect(keys(versions_dict))) open(output_fname, "w") do io print(io, """ ## This file autogenerated by ext/HistoricalStdlibGenerator/generate_historical_stdlibs.jl # Julia standard libraries with duplicate entries removed so as to store only the # first release in a set of releases that all contain the same set of stdlibs. const STDLIBS_BY_VERSION = [ """) for v in sorted_versions print(io, " $(repr(v)) => ") print_sorted(io, versions_dict[v]; indent=8) println(io, ",") println(io) end println(io, "]") println(io) print(io, """ # Next, we also embed a list of stdlibs that must _always_ be treated as stdlibs, # because they cannot be resolved in the registry; they have only ever existed within # the Julia stdlib source tree, and because of that, trying to resolve them will fail. const UNREGISTERED_STDLIBS =""") print_sorted(io, unregistered_stdlibs; indent=4) end	HistoricalStdlibVersions	https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git
[ "MIT" ]	2.0.0	d50c73e4abd8f7c58eb76a8884dfd531fa8dac81	code	2888	""" HistoricalStdlibVersions Loads historical stdlib version information into Pkg to allow Pkg to resolve stdlib versions for prior julia versions. """ module HistoricalStdlibVersions using Pkg import Base: UUID # Use the `Pkg` `StdlibInfo` type if it exists, otherwise just re-define it if !isdefined(Pkg.Types, :StdlibInfo) struct StdlibInfo name::String uuid::UUID # This can be `nothing` if it's an unregistered stdlib version::Union{Nothing,VersionNumber} deps::Vector{UUID} weakdeps::Vector{UUID} end else import Pkg.Types: StdlibInfo end include("version_map.jl") let max_hsg_version = maximum(first.(STDLIBS_BY_VERSION)) # Throw a warning at compile-time if VERSION looks like it's a major or minor version ahead # of the latest version captured within `version_map.jl`. This assumes that we bump at least # one stdlib every minor release, which so far appears to be a safe bet. if VersionNumber(max_hsg_version.major, max_hsg_version.minor) < VersionNumber(VERSION.major, VERSION.minor) @warn("HistoricalStdlibVersions seems to be out of date; please open an issue at https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl/issues") end end function register!() if isdefined(Pkg.Types, :STDLIBS_BY_VERSION) unregister!() if isdefined(Pkg.Types, :StdlibInfo) # We can directly use the datatypes in this package append!(Pkg.Types.STDLIBS_BY_VERSION, STDLIBS_BY_VERSION) merge!(Pkg.Types.UNREGISTERED_STDLIBS, UNREGISTERED_STDLIBS) else # We have to convert our `StdlibInfo` types into the more limited (name, version) format # from earlier julias. Those julias are unable to resolve dependencies of stdlibs properly. for (version, stdlibs) in STDLIBS_BY_VERSION push!(Pkg.Types.STDLIBS_BY_VERSION, version => Dict{UUID,Tuple{String,Union{VersionNumber,Nothing}}}( uuid => (info.name, info.version) for (uuid, info) in stdlibs )) end function find_first_info(uuid) for (_, stdlibs) in STDLIBS_BY_VERSION for (stdlib_uuid, info) in stdlibs if stdlib_uuid == uuid return info end end end return nothing end for (uuid, info) in UNREGISTERED_STDLIBS Pkg.Types.UNREGISTERED_STDLIBS[uuid] = (info.name, nothing) end end end end function unregister!() empty!(Pkg.Types.STDLIBS_BY_VERSION) empty!(Pkg.Types.UNREGISTERED_STDLIBS) end function __init__() if get(ENV, "HISTORICAL_STDLIB_VERSIONS_AUTO_REGISTER", "true") == "true" register!() end end end # module HistoricalStdlibVersions	HistoricalStdlibVersions	https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git
[ "MIT" ]	2.0.0	d50c73e4abd8f7c58eb76a8884dfd531fa8dac81	code	531236	## This file autogenerated by ext/HistoricalStdlibGenerator/generate_historical_stdlibs.jl # Julia standard libraries with duplicate entries removed so as to store only the # first release in a set of releases that all contain the same set of stdlibs. const STDLIBS_BY_VERSION = [ v"1.0.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), nothing, UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.0.2" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.0.2", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.0.3" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.0.4" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.0.4", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.1.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.1.2", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.1.1" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.1.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.2.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.2.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.3.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.3.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.3.1" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.3.1", UUID[UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.4.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.4.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.5.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.5.3" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.6.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.3.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.35.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.3.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.1", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.2" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.3.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.3" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.4" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+9", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.5" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+10", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.6" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+10", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.6", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.7" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+10", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.7", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.7.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.5.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"11.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+4", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+2", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.13+9", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.7.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"12.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"3.0.4+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.7.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.5.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"11.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+4", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+2", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.13+11", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.7.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"12.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"3.0.4+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.7.3" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.5.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"11.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+4", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+2", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.13+11", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.7.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"12.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"3.0.4+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.8.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.3.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.2.1", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.20+0", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.8.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"13.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.1.1+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.8.2" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.3.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+11", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.2.1", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.20+0", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.8.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.1", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"13.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.1.1+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.8.4" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.3.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+11", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.2.1", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.20+0", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.8.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.1", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"13.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.1.1+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.7.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.2" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.4" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.10.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.5+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+6", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"15.0.7+10", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.6.4+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+14", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.0+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.1.10", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.23+2", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.10.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.10.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.10.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.2.1+1", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"15.0.7+10", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.10.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+6", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"15.0.7+10", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.6.4+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+14", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.0+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.1.10", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.23+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.10.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.10.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.10.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.2.1+1", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"15.0.7+10", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.10.3" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+6", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"15.0.7+10", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.6.4+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+14", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.0+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.1.10", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.23+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.10.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.10.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.10.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.2.1+1", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"15.0.7+10", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.11.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.2", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), v"1.11.0", UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), v"1.11.0", UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), v"1.11.0", UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), v"1.11.0", UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), v"1.11.0", UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), v"1.11.0", UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.3.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), v"1.11.0", UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011") => StdlibInfo( "JuliaSyntaxHighlighting", UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"16.0.6+4", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), v"1.11.0", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.6.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.7.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+16", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.7.2+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), v"1.11.0", UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), v"1.11.0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), v"1.11.0", UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), v"1.11.0", UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.6+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), v"1.11.0", UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.12.12", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.27+1", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.11.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb")], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), v"1.11.0", UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), v"1.11.0", UUID[], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), v"1.11.0", UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), v"1.11.0", UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), v"1.11.0", UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b") => StdlibInfo( "StyledStrings", UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), v"1.11.0", UUID[], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.6.0+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), v"1.11.0", UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"16.0.6+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.59.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.12.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.2", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), v"1.11.0", UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), v"1.11.0", UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), v"1.11.0", UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), v"1.11.0", UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), v"1.11.0", UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), v"1.11.0", UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.3.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), v"1.11.0", UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011") => StdlibInfo( "JuliaSyntaxHighlighting", UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"17.0.6+4", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), v"1.11.0", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.6.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.8.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+16", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.7.2+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), v"1.11.0", UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), v"1.11.0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), v"1.11.0", UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.6+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), v"1.11.0", UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2024.3.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.27+1", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.43.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.12.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb")], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), v"1.11.0", UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), v"1.11.0", UUID[], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), v"1.11.0", UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), v"1.11.0", UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), v"1.11.0", UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.12.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b") => StdlibInfo( "StyledStrings", UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), v"1.11.0", UUID[], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.6.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), v"1.11.0", UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.3.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"17.0.6+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.9.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.60.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), ] # Next, we also embed a list of stdlibs that must _always_ be treated as stdlibs, # because they cannot be resolved in the registry; they have only ever existed within # the Julia stdlib source tree, and because of that, trying to resolve them will fail. const UNREGISTERED_STDLIBS =Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011") => StdlibInfo( "JuliaSyntaxHighlighting", UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.1.2", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), )	HistoricalStdlibVersions	https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git
[ "MIT" ]	2.0.0	d50c73e4abd8f7c58eb76a8884dfd531fa8dac81	code	100	import HistoricalStdlibVersions import Pkg using Test @test !isempty(Pkg.Types.STDLIBS_BY_VERSION)	HistoricalStdlibVersions	https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

5485

# opt: x1 = 0.0, x2 = 1.0 # y1 = 1.2727272727272727, y2 = 0.36363636363636365, y3 = 0.0 # mlp = 3.909090909090909 # sub-opt: x1 = 1.0, x2 = 0.6666666666666666 # y1 = 0.8484848484848484, y2 = 0.5757575757575757, y3 = 0.0 # mlp = 5.9393939393939394 # infeasible: x1 = x2 = 0 function benders_form_D() #using JuMP, GLPK #m = Model(GLPK.Optimizer) #@variable(m, x[1:2] >= 0) #@variable(m, y[1:3] >= 0) #@constraint(m, x[1] + x[2] >= 1) ok #@constraint(m, 2x[1] - x[2] + 5y[1] - y[2] >= 5) ok #@constraint(m, x[1] + 3x[2] - 2y[3] >= 3) ok #@constraint(m, y[1] + 2y[2] + y[3] >= 2) ok #@objective(m, Min, 3x[1] + 1x[2] + 2y[1] + y[2] + y[3]) ok #optimize!(m) #@show objective_value(m) #@show value.(x) #@show value.(y) form = """ master min 3x1 + 1x2 + z s.t. x1 + x2 >= 1 benders_sp min 0x1 + 0x2 + 2y1 + y2 + y3 + art1 + art2 + art3 + z s.t. y1 + 2y2 + y3 + art1 >= 2 2x1 - x2 + 5y1 - y2 + art2 >= 5 {BendTechConstr} x1 + 3x2 - 2y3 + art3 >= 3 {BendTechConstr} integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2, y3 second_stage_artificial art1, art2, art3 bounds -Inf <= z <= Inf 0 <= x1 <= 1 1 <= x2 <= 1 1 <= y1 <= 2 0 <= y2 <= 2 0 <= y3 <= 2 0 <= art1 <= Inf 0 <= art2 <= Inf 0 <= art3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end # function test_benders_form_D() # env, reform = benders_form_D() # master = Coluna.MathProg.getmaster(reform) # master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty # ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # for (sp_id, sp) in Coluna.MathProg.get_benders_sep_sps(reform) # sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty # ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # end # # alg = Coluna.Algorithm.BendersCutGeneration( # max_nb_iterations = 10, # restr_master_solve_alg = Coluna.Algorithm.SolveIpForm() # ) # ctx = Coluna.Algorithm.BendersContext( # reform, alg; # ) # Coluna.set_optim_start_time!(env) # # result = Coluna.Benders.run_benders_loop!(ctx, env) # @test result.mlp ≈ 3.909090909090909 # #end #register!(unit_tests, "benders_default", test_benders_form_D) function get_name_to_constrids(form) d = Dict{String, ClMP.ConstrId}() for (constrid, constr) in ClMP.getconstrs(form) d[ClMP.getname(form, constr)] = constrid end return d end function get_name_to_varsids(form) d = Dict{String, ClMP.VarId}() for (varid, var) in ClMP.getvars(form) d[ClMP.getname(form, var)] = varid end return d end function test_benders_cut_lhs() _, reform = benders_form_D() master = Coluna.MathProg.getmaster(reform) sps = Coluna.MathProg.get_benders_sep_sps(reform) ##one sp, spid = 4 sp = sps[4] cids = get_name_to_constrids(sp) vids = get_name_to_varsids(sp) alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100, ) ctx = Coluna.Algorithm.BendersContext( reform, alg, ) dual_sol = Coluna.MathProg.DualSolution( master, [cids["sp_c1"], cids["sp_c2"], cids["sp_c3"]], [2.0, 4.0, 1.0], ##dumb dual sol Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) coeff_cut_lhs = Coluna.Algorithm._compute_cut_lhs(ctx, sp, dual_sol, false) ##opt cut @test coeff_cut_lhs[vids["x1"]] ≈ 9.0 @test coeff_cut_lhs[vids["x2"]] ≈ -1.0 @test coeff_cut_lhs[sp.duty_data.second_stage_cost_var] ≈ 1.0 ## η coeff_cut_lhs = Coluna.Algorithm._compute_cut_lhs(ctx, sp, dual_sol, true) ##feas cut @test coeff_cut_lhs[vids["x1"]] ≈ 9.0 @test coeff_cut_lhs[vids["x2"]] ≈ -1.0 @test coeff_cut_lhs[sp.duty_data.second_stage_cost_var] ≈ 0.0 ## η end register!(unit_tests, "benders_default", test_benders_cut_lhs) function test_benders_cut_rhs() _, reform = benders_form_D() master = Coluna.MathProg.getmaster(reform) sps = Coluna.MathProg.get_benders_sep_sps(reform) ##one sp, spid = 4 sp = sps[4] cids = get_name_to_constrids(sp) vids = get_name_to_varsids(sp) alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 100, ) ctx = Coluna.Algorithm.BendersContext( reform, alg, ) dual_sol = Coluna.MathProg.DualSolution( master, [cids["sp_c1"], cids["sp_c2"], cids["sp_c3"]], [2.0, 4.0, 1.0], ##dumb dual sol Coluna.MathProg.VarId[vids["y1"], vids["x1"], vids["y2"], vids["x2"]], Float64[10.0, 5.0, 2.0, 3.0], Coluna.MathProg.ActiveBound[MathProg.LOWER, MathProg.UPPER, MathProg.UPPER, MathProg.LOWER], ## x2 fixed to 1.0 0.0, Coluna.MathProg.FEASIBLE_SOL ) coeff_cut_rhs = Coluna.Algorithm._compute_cut_rhs_contrib(ctx, sp, dual_sol) @test coeff_cut_rhs == 27.0 + (1*10.0 + 1*5.0 + 2*2.0 + 3.0) ## πr + bounding_constraints ##TODO: update when we know how to deal with equalities in the rhs computation end register!(unit_tests, "benders_default", test_benders_cut_rhs)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

38432

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

9026

#################### tests with flags #################### struct TestBendersMaster end struct TestBendersSubproblem end mutable struct TestBendersMasterRes ## mock of a master opt. result infeasible_master::Bool unbounded_master::Bool is_certificate::Bool end function Coluna.Benders.is_unbounded(master_res::TestBendersMasterRes) return master_res.unbounded_master end function Coluna.Benders.is_infeasible(master_res::TestBendersMasterRes) return master_res.infeasible_master end function Coluna.Benders.is_certificate(master_res::TestBendersMasterRes) return master_res.is_certificate end function Coluna.Benders.get_primal_sol(master_res::TestBendersMasterRes) return nothing end function Coluna.Benders.get_obj_val(sep_res::TestBendersMasterRes) return 0.0 end struct TestBendersSepRes ## mock of a sep. problem opt. result infeasible_sp::Bool unbounded_sp::Bool end function Coluna.Benders.is_infeasible(sep_res::TestBendersSepRes) return sep_res.infeasible_sp end function Coluna.Benders.is_unbounded(sep_res::TestBendersSepRes) return sep_res.unbounded_sp end function Coluna.Benders.get_obj_val(sep_res::TestBendersSepRes) return 0.0 end mutable struct TestBendersFlowFlagContext <: Coluna.Benders.AbstractBendersContext master_opt_res::TestBendersMasterRes sp_opt_res::TestBendersSepRes flag_unbounded_master::Bool ## check we enter treat_unbounded_master_problem_case! flag_unbounded_master_sp::Bool ## check we enter setup_separation_for_unbounded_master_case! flag_infeasible_sp::Bool ## check we enter treat_infeasible_separation_problem_case! end function Coluna.Benders.get_master(ctx::TestBendersFlowFlagContext) return TestBendersMaster() end function Coluna.Benders.get_reform(ctx::TestBendersFlowFlagContext) return nothing end function Coluna.Benders.is_minimization(ctx::TestBendersFlowFlagContext) return true end function Coluna.Benders.benders_iteration_output_type(ctx::TestBendersFlowFlagContext) return Coluna.Algorithm.BendersIterationOutput end function Coluna.Benders.optimize_master_problem!(master, ctx::TestBendersFlowFlagContext, env) return ctx.master_opt_res end function Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx::TestBendersFlowFlagContext, env) ctx.flag_unbounded_master = true ctx.master_opt_res.unbounded_master = false return ctx.master_opt_res end function Coluna.Benders.get_benders_subprobs(ctx::TestBendersFlowFlagContext) return [(1, TestBendersSubproblem())] end function Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx::TestBendersFlowFlagContext, sp, mast_primal_sol) ctx.flag_unbounded_master_sp = true return end function Coluna.Benders.update_sp_rhs!(ctx::TestBendersFlowFlagContext, sp, mast_primal_sol) return end function Coluna.Benders.optimize_separation_problem!(ctx::TestBendersFlowFlagContext, sp, env, unbounded_master) return ctx.sp_opt_res end function Coluna.Benders.treat_infeasible_separation_problem_case!(ctx::TestBendersFlowFlagContext, sp, env, unbounded_master_case) ctx.flag_infeasible_sp = true return ctx.sp_opt_res end function Coluna.Benders.master_is_unbounded(ctx::TestBendersFlowFlagContext, second_stage_cost, unbounded_master_case) return ctx.master_opt_res.unbounded_master ## TODO check end function Coluna.Benders.insert_cuts!(reform, ctx::TestBendersFlowFlagContext, cuts) return [] end function Coluna.Benders.build_primal_solution(ctx::TestBendersFlowFlagContext, mast_primal_sol, sep_sp_sols) return end function Coluna.Benders.set_of_cuts(ctx::TestBendersFlowFlagContext) return [] end function Coluna.Benders.set_of_sep_sols(ctx::TestBendersFlowFlagContext) return [] end function Coluna.Benders.push_in_set!(ctx::TestBendersFlowFlagContext, set, elem) return false end function benders_flow_infeasible_master() ctx = TestBendersFlowFlagContext( ## bounded master TestBendersMasterRes( true, false, false ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test res.infeasible == true @test ctx.flag_unbounded_master == false @test ctx.flag_unbounded_master_sp == false @test ctx.flag_infeasible_sp == false ctx = TestBendersFlowFlagContext( ## unbounded master with certificate = true to ensure we stop before entering setup_separation_for_unbounded_master_case! TestBendersMasterRes( true, true, true ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test res.infeasible == true @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == false @test ctx.flag_infeasible_sp == false end register!(unit_tests, "benders_default", benders_flow_infeasible_master) function benders_flow_unbounded_master() ctx = TestBendersFlowFlagContext( TestBendersMasterRes( false, true, false ## with certificate = false ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == false @test ctx.flag_infeasible_sp == false ctx = TestBendersFlowFlagContext( TestBendersMasterRes( false, true, true ## with certificate = true ), TestBendersSepRes( false, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == true @test ctx.flag_infeasible_sp == false end register!(unit_tests, "benders_default", benders_flow_unbounded_master) function benders_flow_infeasible_sp() ctx = TestBendersFlowFlagContext( ## bounded sp, bounded master TestBendersMasterRes( false, false, false ), TestBendersSepRes( true, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true @test ctx.flag_unbounded_master == false @test ctx.flag_unbounded_master_sp == false ctx = TestBendersFlowFlagContext( ## bounded sp, unbounded master TestBendersMasterRes( false, true, false ), TestBendersSepRes( true, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == false ctx = TestBendersFlowFlagContext( ## bounded sp, unbounded master with certificate TestBendersMasterRes( false, true, true ), TestBendersSepRes( true, false ), false, false, false ) res = Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true @test ctx.flag_unbounded_master == true @test ctx.flag_unbounded_master_sp == true end register!(unit_tests, "benders_default", benders_flow_infeasible_sp) ## test unbounded sp flow when sp is either feasible or infeasible -> in both cases an error should be thrown function benders_flow_unbounded_sp() ctx = TestBendersFlowFlagContext( ## feasible unbounded sp TestBendersMasterRes( false, false, false ), TestBendersSepRes( false, true ), false, false, false ) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) ctx = TestBendersFlowFlagContext( ## infeasible unbounded sp TestBendersMasterRes( false, false, false ), TestBendersSepRes( true, true ), false, false, false ) @test_throws Coluna.Benders.UnboundedError Coluna.Benders.run_benders_iteration!(ctx, nothing, nothing, nothing) @test ctx.flag_infeasible_sp == true end register!(unit_tests, "benders_default", benders_flow_unbounded_sp)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

6485

#################### tests with formulations #################### mutable struct TestBendersFlowFormContext <: Coluna.Benders.AbstractBendersContext context::ClA.BendersContext flag_infeasible_master::Bool flag_unbounded_master::Bool##flag to check that we enter treat_unbounded_master_problem_case! flag_infeasible_sp::Bool ## flag to check that we enter treat_infeasible_separation_problem_case end Coluna.Benders.get_master(ctx::TestBendersFlowFormContext) = Coluna.Benders.get_master(ctx.context) Coluna.Benders.get_reform(ctx::TestBendersFlowFormContext) = Coluna.Benders.get_reform(ctx.context) Coluna.Benders.is_minimization(ctx::TestBendersFlowFormContext) = Coluna.Benders.is_minimization(ctx.context) Coluna.Benders.get_benders_subprobs(ctx::TestBendersFlowFormContext) = Coluna.Benders.get_benders_subprobs(ctx.context) Coluna.Benders.optimize_master_problem!(master, ctx::TestBendersFlowFormContext, env) = Coluna.Benders.optimize_master_problem!(master, ctx.context, env) function Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx::TestBendersFlowFormContext, env) output = Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx.context, env) ctx.flag_unbounded_master = true return output end Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx::TestBendersFlowFormContext, sp, mast_primal_sol) = Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx.context, sp, mast_primal_sol) Coluna.Benders.optimize_separation_problem!(ctx::TestBendersFlowFormContext, sp::Formulation{BendersSp}, env, unbounded_master) = Coluna.Benders.optimize_separation_problem!(ctx.context, sp, env, unbounded_master) Coluna.Benders.master_is_unbounded(ctx::TestBendersFlowFormContext, second_stage_cost, unbounded_master_case) = Coluna.Benders.master_is_unbounded(ctx.context, second_stage_cost, unbounded_master_case) function Coluna.Benders.treat_infeasible_separation_problem_case!(ctx::TestBendersFlowFormContext, sp::Formulation{BendersSp}, env, unbounded_master_case) output = Coluna.Benders.treat_infeasible_separation_problem_case!(ctx.context, sp, env, unbounded_master_case) ctx.flag_infeasible_sp = true return output end Coluna.Benders.push_in_set!(ctx::TestBendersFlowFormContext, set::Coluna.Algorithm.CutsSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.push_in_set!(ctx::TestBendersFlowFormContext, set::Coluna.Algorithm.SepSolSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.insert_cuts!(reform, ctx::TestBendersFlowFormContext, cuts) = Coluna.Benders.insert_cuts!(reform, ctx.context, cuts) Coluna.Benders.build_primal_solution(ctx::TestBendersFlowFormContext, mast_primal_sol, sep_sp_sols) = Coluna.Benders.build_primal_solution(ctx.context, mast_primal_sol, sep_sp_sols) Coluna.Benders.benders_iteration_output_type(ctx::TestBendersFlowFormContext) = Coluna.Benders.benders_iteration_output_type(ctx.context) Coluna.Benders.update_sp_rhs!(ctx::TestBendersFlowFormContext, sp, mast_primal_sol) = Coluna.Benders.update_sp_rhs!(ctx.context, sp, mast_primal_sol) Coluna.Benders.set_of_cuts(ctx::TestBendersFlowFormContext) = Coluna.Benders.set_of_cuts(ctx.context) Coluna.Benders.set_of_sep_sols(ctx::TestBendersFlowFormContext) = Coluna.Benders.set_of_sep_sols(ctx.context) function benders_flow_form_unbounded_master() env, reform, _ = benders_form_unbounded_master() alg = Coluna.Algorithm.BendersCutGeneration() ctx = TestBendersFlowFormContext( Coluna.Algorithm.BendersContext( reform, alg ), false, false, false ) master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_iteration!(ctx, nothing, env, nothing) @test ctx.flag_unbounded_master == true end register!(unit_tests, "benders_default", benders_flow_form_unbounded_master) ## x2 fixed to zero ## z cost fixed to dumb value function benders_infeasible_sp() form = """ master min x1 + 4x2 + z s.t. x1 + x2 >= 1 benders_sp min 0x1 + 0x2 + 2y1 + 3y2 + y3 + art1 + art2 + z s.t. x1 + x2 + y1 + 2y3 + art1 >= 1 {BendTechConstr} x2 + y2 + art2 >= 2 {BendTechConstr} integer first_stage x1, x2 continuous second_stage_cost z second_stage y1, y2, y3 second_stage_artificial art1, art2 bounds 10 <= z <= 10 0 <= x1 <= 1 0 <= x2 <= 0 0 <= y1 <= 1 0 <= y2 <= 1 0 <= y3 <= 1 0 <= art1 <= Inf 0 <= art2 <= Inf 0 <= art3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_flow_form_infeasible_sp() env, reform = benders_infeasible_sp() alg = Coluna.Algorithm.BendersCutGeneration() master = ClMP.getmaster(reform) sps = ClMP.get_benders_sep_sps(reform) #@show master #for sp in sps # @show sp #end ctx = TestBendersFlowFormContext( Coluna.Algorithm.BendersContext( reform, alg ), false, false, false ) master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) Coluna.Algorithm._deactivate_art_vars(sp) end Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_iteration!(ctx, nothing, env, nothing) @test ctx.flag_infeasible_sp == true end register!(unit_tests, "benders_default", benders_flow_form_infeasible_sp)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

6004

## original MIP: ## min cx + dy s.t. ## Ax >= b ## Tx + Qy >= r ## x, y >= 0, x ∈ Z^n ## master: ## min cx + η ## Ax >= B ## < benders cuts > ## SP: ## min dy ## Tx* + Qy >= r ## y >= 0 ## π: dual sol ## η: contribution to the objective of the second-level variables ## feasibility cut: πTx >= πr ## optimality cut: η + πTx >= πr struct TestBendersIterationContext <: Coluna.Benders.AbstractBendersContext context::ClA.BendersContext master ##Formulation{Benders...} sps ##Dict{Int16, Coluna.ColunaBase.AbstractModel} first_stage_sol::Dict{String, Float64} ## id of variable, value second_stage_sols::Dict{String, Float64} ##id of variable, value end Coluna.Benders.get_master(ctx::TestBendersIterationContext) = Coluna.Benders.get_master(ctx.context) Coluna.Benders.get_reform(ctx::TestBendersIterationContext) = Coluna.Benders.get_reform(ctx.context) Coluna.Benders.is_minimization(ctx::TestBendersIterationContext) = Coluna.Benders.is_minimization(ctx.context) Coluna.Benders.get_benders_subprobs(ctx::TestBendersIterationContext) = Coluna.Benders.get_benders_subprobs(ctx.context) ## where to check stop condition ? ## re-def if need to check something Coluna.Benders.optimize_master_problem!(master, ctx::TestBendersIterationContext, env) = Coluna.Benders.optimize_master_problem!(master, ctx.context, env) Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx::TestBendersIterationContext, env) = Coluna.Benders.treat_unbounded_master_problem_case!(master, ctx.context, env) Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx::TestBendersIterationContext, sp, mast_primal_sol) = Coluna.Benders.setup_separation_for_unbounded_master_case!(ctx.context, sp, mast_primal_sol) ## TODO: redef to check cuts Coluna.Benders.optimize_separation_problem!(ctx::TestBendersIterationContext, sp::Formulation{BendersSp}, env, unbounded_master) = Coluna.Benders.optimize_separation_problem!(ctx.context, sp, env, unbounded_master) Coluna.Benders.master_is_unbounded(ctx::TestBendersIterationContext, second_stage_cost, unbounded_master_case) = Coluna.Benders.master_is_unbounded(ctx.context, second_stage_cost, unbounded_master_case) ## same Coluna.Benders.treat_infeasible_separation_problem_case!(ctx::TestBendersIterationContext, sp::Formulation{BendersSp}, env, unbounded_master_case) = Coluna.Benders.treat_infeasible_separation_problem_case!(ctx.context, sp, env, unbounded_master_case) Coluna.Benders.push_in_set!(ctx::TestBendersIterationContext, set::Coluna.Algorithm.CutsSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.push_in_set!(ctx::TestBendersIterationContext, set::Coluna.Algorithm.SepSolSet, sep_result::Coluna.Algorithm.BendersSeparationResult) = Coluna.Benders.push_in_set!(ctx.context, set, sep_result) Coluna.Benders.insert_cuts!(reform, ctx::TestBendersIterationContext, cuts) = Coluna.Benders.insert_cuts!(reform, ctx.context, cuts) function Coluna.Benders.build_primal_solution(ctx::TestBendersIterationContext, mast_primal_sol, sep_sp_sols) output = Coluna.Benders.build_primal_solution(ctx.context, mast_primal_sol, sep_sp_sols) for (varid, val) in mast_primal_sol name = getname(ctx.master, varid) if haskey(ctx.first_stage_sol, name) @test ctx.first_stage_sol[name] ≈ val else @test 0.0 <= val <= 1.0e-4 end end for (_, sp) in ctx.sps for sp_sol in sep_sp_sols.sols for (varid, val) in sp_sol name = getname(sp, varid) if haskey(ctx.second_stage_sols, name) @test ctx.second_stage_sols[name] ≈ val else @test 0.0 <= val <= 1.0e-4 end end end end return output end Coluna.Benders.benders_iteration_output_type(ctx::TestBendersIterationContext) = Coluna.Benders.benders_iteration_output_type(ctx.context) Coluna.Benders.update_sp_rhs!(ctx::TestBendersIterationContext, sp, mast_primal_sol) = Coluna.Benders.update_sp_rhs!(ctx.context, sp, mast_primal_sol) Coluna.Benders.set_of_cuts(ctx::TestBendersIterationContext) = Coluna.Benders.set_of_cuts(ctx.context) Coluna.Benders.set_of_sep_sols(ctx::TestBendersIterationContext) = Coluna.Benders.set_of_sep_sols(ctx.context) ## checks cuts ## stop criterion because of opt. sol found is matched function benders_iter_opt_stop() env, reform = benders_form_location_routing_fixed_opt_continuous() master = ClMP.getmaster(reform) sps = ClMP.get_benders_sep_sps(reform) ClMP.relax_integrality!(master) ## sol fully fixed ##expected sol first_stage_sol = Dict( "y1" => 0.5, "y2" => 0.0, "y3" => 0.3333, "z" => 175.16666666666666 ) second_stage_sols = Dict( "x11" => 0.5, "x12" => 0.5, "x13" => 0.49999, "x14" => 0.5, "x31" => 0.33333, "x32" => 0.33333, "x33" => 0.16666, "x34" => 0.33333 ) alg = Coluna.Algorithm.BendersCutGeneration( max_nb_iterations = 10 ) ctx = TestBendersIterationContext( Coluna.Algorithm.BendersContext( reform, alg ), master, sps, first_stage_sol, second_stage_sols ) master = Coluna.MathProg.getmaster(reform) master.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) for (_, sp) in Coluna.MathProg.get_benders_sep_sps(reform) sp.optimizers = Coluna.MathProg.AbstractOptimizer[] # dirty ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end Coluna.set_optim_start_time!(env) result = Coluna.Benders.run_benders_iteration!(ctx, nothing, env, nothing) @test result.master ≈ 293.4956666 end register!(unit_tests, "benders_default", benders_iter_opt_stop)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

18933

## optimal solution is found with 1st level variables y equals to: ## y1 = 1.0, y2 = 0.0, y3 = 1.0 ## with 2nd level variables: ## x12 = x14 = x32 = x33 = 1.0 ## mlp = 385.0 ## a sub-optimal solution can be found with 1st level variables: ## y1 = 1.0, y2 = 1.0, y3 = 0.0 (fix y3 to zero) ## with 2nd level variables: ## x12 = x22 = x23 = 1.0 ## mlp = 517.0 ## if y1 is fixed to zero, the problem is infeasible ## [Relaxation] ## optimal solution is found with 1st level variables y equals to: ## y1 = 0.5, y2 = 0.0, y3 = 0.33333 ## with 2nd level variables: ## x11 = 0.5, x12 = 0.5, x13 = 0.49999, x14 = 0.5, x31 = 1/3, x32 = 1/3, x33 = 0.16666, x34 = 1/3 ## mlp = 293.5 ## a sub-optimal solution can be found with 1st level variables: ## y1 = 0.5, y2 = 0.5, y3 = 0.0 (fix y3 to zero) ## with 2nd level variables: ## x11 = 0.5, x12 = 0.5, x13 = 0.5, x21 = 0.5, x22 = 0.5, x23 = 0.5 ## mlp = 386.00 ## if y1 is fixed to zero, the problem is infeasible function benders_form_location_routing() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 integer first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds -Inf <= z <= Inf 0 <= x11 <= 1 0 <= x12 <= 1 0 <= x13 <= 1 0 <= x14 <= 1 0 <= x21 <= 1 0 <= x22 <= 1 0 <= x23 <= 1 0 <= x24 <= 1 0 <= x31 <= 1 0 <= x32 <= 1 0 <= x33 <= 1 0 <= x34 <= 1 0 <= y1 <= 1 0 <= y2 <= 1 0 <= y3 <= 1 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_form_location_routing_fixed_opt_continuous() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 continuous first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds 175.16666666666666 <= z <= 175.16666666666666 0.5 <= x11 <= 0.5 0.5 <= x12 <= 0.5 0.49999 <= x13 <= 0.49999 0.5 <= x14 <= 0.5 0 <= x21 <= 0 0 <= x22 <= 0 0 <= x23 <= 0 0 <= x24 <= 0 0.33333 <= x31 <= 0.33333 0.33333 <= x32 <= 0.33333 0.16666 <= x33 <= 0.16666 0.33333 <= x34 <= 0.33333 0.5 <= y1 <= 0.5 0.0 <= y2 <= 0.0 0.3333 <= y3 <= 0.3333 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_form_location_routing_infeasible() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 integer first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds -Inf <= z <= Inf 0 <= x11 <= 1 0 <= x12 <= 1 0 <= x13 <= 1 0 <= x14 <= 1 0 <= x21 <= 1 0 <= x22 <= 1 0 <= x23 <= 1 0 <= x24 <= 1 0 <= x31 <= 1 0 <= x32 <= 1 0 <= x33 <= 1 0 <= x34 <= 1 0 <= y1 <= 0 0 <= y2 <= 1 0 <= y3 <= 1 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end function benders_form_location_routing_subopt() form = """ master min 150y1 + 210y2 + 130y3 + z s.t. y1 + y2 + y3 >= 0 benders_sp min 0y1 + 0y2 + 0y3 + 100x11 + 50x12 + 75x13 + 15x14 + 80x21 + 40x22 + 67x23 + 24x24 + 70x31 + 5x32 + 35x33 + 73x34 + z + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 s.t. y1 - x11 + local_art_of_open1 >= 0 {BendTechConstr} y1 - x12 + local_art_of_open2 >= 0 {BendTechConstr} y1 - x13 + local_art_of_open3 >= 0 {BendTechConstr} y1 - x14 + local_art_of_open4 >= 0 {BendTechConstr} y2 - x21 + local_art_of_open5 >= 0 {BendTechConstr} y2 - x22 + local_art_of_open6 >= 0 {BendTechConstr} y2 - x23 + local_art_of_open7 >= 0 {BendTechConstr} y2 - x24 + local_art_of_open8 >= 0 {BendTechConstr} y3 - x31 + local_art_of_open9 >= 0 {BendTechConstr} y3 - x32 + local_art_of_open10 >= 0 {BendTechConstr} y3 - x33 + local_art_of_open11 >= 0 {BendTechConstr} y3 - x34 + local_art_of_open12 >= 0 {BendTechConstr} x11 + x12 + local_art_of_cov1 >= 1 x12 + x13 + x21 + x23 + x31 + x34 + local_art_of_cov2 >= 1 x13 + x22 + x33 + x34 + local_art_of_cov3 >= 1 x13 + x14 + x21 + x22 + x24 + local_art_of_cov4 >= 1 x21 + x23 + x31 + x32 + x34 + local_art_of_cov5 >= 1 x11 + x12 + x13 + x14 + local_art_of_limit_nb_routes1 <= 3 x21 + x22 + x23 + x24 + local_art_of_limit_nb_routes2 <= 3 x31 + x32 + x33 + x34 + local_art_of_limit_nb_routes3 <= 3 x11 + x12 + x13 + x14 + x21 + x22 + x23 + x24 + x31 + x32 + x33 + x34 + local_art_of_open1 + local_art_of_open2 + local_art_of_open3 + local_art_of_open4 + local_art_of_open5 + local_art_of_open6 + local_art_of_open7 + local_art_of_open8 + local_art_of_open9 + local_art_of_open10 + local_art_of_open11 + local_art_of_open12 + local_art_of_cov1 + local_art_of_cov2 + local_art_of_cov3 + local_art_of_cov4 + local_art_of_cov5 + local_art_of_limit_nb_routes1 + local_art_of_limit_nb_routes2 + local_art_of_limit_nb_routes3 integer first_stage y1, y2, y3 continuous second_stage_cost z second_stage x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33, x34 second_stage_artificial local_art_of_open1, local_art_of_open2, local_art_of_open3, local_art_of_open4, local_art_of_open5, local_art_of_open6, local_art_of_open7, local_art_of_open8, local_art_of_open9, local_art_of_open10, local_art_of_open11, local_art_of_open12, local_art_of_cov1, local_art_of_cov2, local_art_of_cov3, local_art_of_cov4, local_art_of_cov5, local_art_of_limit_nb_routes1, local_art_of_limit_nb_routes2, local_art_of_limit_nb_routes3 bounds -Inf <= z <= Inf 0 <= x11 <= 1 0 <= x12 <= 1 0 <= x13 <= 1 0 <= x14 <= 1 0 <= x21 <= 1 0 <= x22 <= 1 0 <= x23 <= 1 0 <= x24 <= 1 0 <= x31 <= 1 0 <= x32 <= 1 0 <= x33 <= 1 0 <= x34 <= 1 0 <= y1 <= 1 0 <= y2 <= 1 0 <= y3 <= 0 0 <= local_art_of_open1 <= Inf 0 <= local_art_of_open2 <= Inf 0 <= local_art_of_open3 <= Inf 0 <= local_art_of_open4 <= Inf 0 <= local_art_of_open5 <= Inf 0 <= local_art_of_open6 <= Inf 0 <= local_art_of_open7 <= Inf 0 <= local_art_of_open8 <= Inf 0 <= local_art_of_open9 <= Inf 0 <= local_art_of_open10 <= Inf 0 <= local_art_of_open11 <= Inf 0 <= local_art_of_open12 <= Inf 0 <= local_art_of_cov1 <= Inf 0 <= local_art_of_cov2 <= Inf 0 <= local_art_of_cov3 <= Inf 0 <= local_art_of_cov4 <= Inf 0 <= local_art_of_cov5 <= Inf 0 <= local_art_of_limit_nb_routes1 <= Inf 0 <= local_art_of_limit_nb_routes2 <= Inf 0 <= local_art_of_limit_nb_routes3 <= Inf """ env, _, _, _, reform = reformfromstring(form) return env, reform end

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

11226

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

2347

struct MockCandidate <: Coluna.Branching.AbstractBranchingCandidate local_id::Int64 lhs::Float64 end Coluna.Branching.get_local_id(c::MockCandidate) = c.local_id Coluna.Branching.get_lhs(c::MockCandidate) = c.lhs Coluna.Branching.getdescription(::MockCandidate) = "MockCandidate" function _mock_candidates(lhs::Vector{Float64}) return map(enumerate(lhs)) do (i, lhs) MockCandidate(i, lhs) end end function test_first_found_criterion1() candidates = _mock_candidates([0.1, 0.2, 0.3, 0.4, 0.5]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.FirstFoundCriterion(), 3 ) @test length(selected_candidates) == 3 @test selected_candidates[1].local_id == 1 @test selected_candidates[2].local_id == 2 @test selected_candidates[3].local_id == 3 end register!(unit_tests, "branching", test_first_found_criterion1) function test_most_fractional_criterion1() candidates = _mock_candidates([0.1, 0.2, 0.3, 0.4, 0.5]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.MostFractionalCriterion(), 3 ) @test length(selected_candidates) == 3 @test selected_candidates[1].local_id == 5 @test selected_candidates[2].local_id == 4 @test selected_candidates[3].local_id == 3 end register!(unit_tests, "branching", test_most_fractional_criterion1) function test_least_fractional_criterion1() candidates = _mock_candidates([0.4, 0.3, 0.45, -0.4, -0.8]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.LeastFractionalCriterion(), 2 ) @test length(selected_candidates) == 2 @test selected_candidates[1].local_id == 5 @test selected_candidates[2].local_id == 2 end register!(unit_tests, "branching", test_least_fractional_criterion1) function test_closest_to_non_zero_integer_criterion1() candidates = _mock_candidates([0.4, 0.3, 0.45, -0.4, -0.8]) selected_candidates = Coluna.Branching.select_candidates!( candidates, Coluna.Algorithm.ClosestToNonZeroIntegerCriterion(), 2 ) @test length(selected_candidates) == 2 @test selected_candidates[1].local_id == 5 @test selected_candidates[2].local_id == 3 end register!(unit_tests, "branching", test_closest_to_non_zero_integer_criterion1)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

65250

############################################################################################ # Test default implementation of an iteration of Column Generation ############################################################################################ # 1- minimize # 2- maximize # 3- minimize with pure master variables # 4- minimize with objective constant # # TODO: description of the tests. ############################################################################################ using Test # Minimization and test all constraint senses form1() = """ master min 3x1 + 2x2 + 5x3 + 4y1 + 3y2 + 5y3 + z s.t. x1 + x2 + x3 + y1 + y2 + y3 + 2z >= 10 x1 + 2x2 + y1 + 2y2 + z <= 100 x1 + 3x3 + y1 + + 3y3 == 100 z <= 5 dw_sp min x1 + x2 + x3 + y1 + y2 + y3 s.t. x1 + x2 + x3 + y1 + y2 + y3 >= 10 integer representatives x1, x2, x3, y1, y2, y3 pure z bounds x1 >= 0 x2 >= 0 x3 >= 0 y1 >= 0 y2 >= 0 y3 >= 0 z >= 0 """ function get_name_to_varids(form) d = Dict{String, ClMP.VarId}() for (varid, var) in ClMP.getvars(form) d[ClMP.getname(form, var)] = varid end return d end function get_name_to_constrids(form) d = Dict{String, ClMP.ConstrId}() for (constrid, constr) in ClMP.getconstrs(form) d[ClMP.getname(form, constr)] = constrid end return d end # Simple case with only subproblem representatives variables. function test_reduced_costs_calculation_helper() _, master, _, _, _ = reformfromstring(form1()) vids = get_name_to_varids(master) cids = get_name_to_constrids(master) helper = ClA.ReducedCostsCalculationHelper(master) @test helper.dw_subprob_c[vids["x1"]] == 3 @test helper.dw_subprob_c[vids["x2"]] == 2 @test helper.dw_subprob_c[vids["x3"]] == 5 @test helper.dw_subprob_c[vids["y1"]] == 4 @test helper.dw_subprob_c[vids["y2"]] == 3 @test helper.dw_subprob_c[vids["y3"]] == 5 @test helper.dw_subprob_c[vids["z"]] == 0 @test helper.dw_subprob_A[cids["c1"], vids["x1"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["x2"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["x3"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["y1"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["y2"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["y3"]] == 1 @test helper.dw_subprob_A[cids["c1"], vids["z"]] == 0 # z is not in the subproblem. @test helper.dw_subprob_A[cids["c2"], vids["x1"]] == 1 @test helper.dw_subprob_A[cids["c2"], vids["x2"]] == 2 @test helper.dw_subprob_A[cids["c2"], vids["y1"]] == 1 @test helper.dw_subprob_A[cids["c2"], vids["y2"]] == 2 @test helper.dw_subprob_A[cids["c2"], vids["z"]] == 0 # z is not in the subproblem. @test helper.dw_subprob_A[cids["c3"], vids["x1"]] == 1 @test helper.dw_subprob_A[cids["c3"], vids["x3"]] == 3 @test helper.dw_subprob_A[cids["c3"], vids["y1"]] == 1 @test helper.dw_subprob_A[cids["c3"], vids["y3"]] == 3 @test helper.dw_subprob_A[cids["c3"], vids["z"]] == 0 # z is not in the subproblem. @test helper.master_c[vids["x1"]] == 0 # x1 is not in the master. @test helper.master_c[vids["x2"]] == 0 # x2 is not in the master. @test helper.master_c[vids["x3"]] == 0 # x3 is not in the master. @test helper.master_c[vids["y1"]] == 0 # y1 is not in the master. @test helper.master_c[vids["y2"]] == 0 # y2 is not in the master. @test helper.master_c[vids["y3"]] == 0 # y3 is not in the master. @test helper.master_c[vids["z"]] == 1 @test helper.master_A[cids["c1"], vids["x1"]] == 0 # x1 is not in the master. @test helper.master_A[cids["c1"], vids["z"]] == 2 @test helper.master_A[cids["c2"], vids["z"]] == 1 @test helper.master_A[cids["c3"], vids["z"]] == 0 @test helper.master_A[cids["c4"], vids["z"]] == 1 end register!(unit_tests, "colgen_default", test_reduced_costs_calculation_helper) # Minimization and test all constraint senses form2() = """ master min 3x1 + 2x2 + 5x3 + 4y1 + 3y2 + 5y3 + z1 + z2 s.t. x1 + x2 + x3 + y1 + y2 + y3 + 2z1 + z2 >= 10 x1 + 2x2 + y1 + 2y2 + z1 <= 100 x1 + 3x3 + y1 + + 3y3 == 100 z1 + z2 <= 5 dw_sp min x1 + x2 + x3 + y1 + y2 + y3 s.t. x1 + x2 + x3 + y1 + y2 + y3 >= 10 integer representatives x1, x2, x3, y1, y2, y3 pure z1, z2 bounds x1 >= 0 x2 >= 0 x3 >= 0 y1 >= 0 y2 >= 0 y3 >= 0 z1 >= 0 z2 >= 3 """ function test_subgradient_calculation_helper() _, master, _, _, _ = reformfromstring(form2()) vids = get_name_to_varids(master) cids = get_name_to_constrids(master) helper = ClA.SubgradientCalculationHelper(master) @test helper.a[cids["c1"]] == 10 @test helper.a[cids["c2"]] == -100 @test helper.a[cids["c3"]] == 100 @test helper.a[cids["c4"]] == -5 @test helper.A[cids["c1"], vids["x1"]] == 1 @test helper.A[cids["c1"], vids["x2"]] == 1 @test helper.A[cids["c1"], vids["x3"]] == 1 @test helper.A[cids["c1"], vids["y1"]] == 1 @test helper.A[cids["c1"], vids["y2"]] == 1 @test helper.A[cids["c1"], vids["y3"]] == 1 @test helper.A[cids["c1"], vids["z1"]] == 2 @test helper.A[cids["c1"], vids["z2"]] == 1 @test helper.A[cids["c2"], vids["x1"]] == -1 @test helper.A[cids["c2"], vids["x2"]] == -2 @test helper.A[cids["c2"], vids["y1"]] == -1 @test helper.A[cids["c2"], vids["y2"]] == -2 @test helper.A[cids["c2"], vids["z1"]] == -1 @test helper.A[cids["c2"], vids["z2"]] == 0 @test helper.A[cids["c3"], vids["x1"]] == 1 @test helper.A[cids["c3"], vids["x3"]] == 3 @test helper.A[cids["c3"], vids["y1"]] == 1 @test helper.A[cids["c3"], vids["y3"]] == 3 @test helper.A[cids["c3"], vids["z1"]] == 0 @test helper.A[cids["c3"], vids["z2"]] == 0 @test helper.A[cids["c4"], vids["z1"]] == -1 @test helper.A[cids["c4"], vids["z2"]] == -1 end register!(unit_tests, "colgen_default", test_subgradient_calculation_helper) # All the tests are based on the Generalized Assignment problem. # x_mj = 1 if job j is assigned to machine m function min_toy_gap() # We introduce variables z1 & z2 to force dual value of constraint c7 to equal to 28. form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 51.0 MC_30 + 38.0 MC_31 + 31.0 MC_32 + 35.0 MC_33 + 48.0 MC_34 + 13.0 MC_35 + 53.0 MC_36 + 28.0 MC_37 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 28 z1 - 28 z2 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_32 + 1.0 MC_33 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_37 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_37 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_31 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_36 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_34 + 1.0 MC_36 + 1.0 MC_37 + z1 - z2 >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_30, MC_31, MC_32, MC_33, MC_34, MC_35, MC_36, MC_37 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var pure z1, z2 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_30 >= 0.0 MC_31 >= 0.0 MC_32 >= 0.0 MC_33 >= 0.0 MC_34 >= 0.0 MC_35 >= 0.0 MC_36 >= 0.0 MC_37 >= 0.0 z1 >= 0.0 z2 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function max_toy_gap() # We introduce variables (z1, z2), (z3, z4) and (z5, z6) to force dual value of constraint c2, c1 and c5 to be equal to 6.0, 3.0 and 22.0 respectively. form = """ master max - 10000.0 local_art_of_cov_5 - 10000.0 local_art_of_cov_4 - 10000.0 local_art_of_cov_6 - 10000.0 local_art_of_cov_7 - 10000.0 local_art_of_cov_2 - 10000.0 local_art_of_cov_3 - 10000.0 local_art_of_cov_1 - 10000.0 local_art_of_sp_lb_5 - 10000.0 local_art_of_sp_ub_5 - 10000.0 local_art_of_sp_lb_4 - 10000.0 local_art_of_sp_ub_4 - 100000.0 global_pos_art_var - 100000.0 global_neg_art_var + 53.0 MC_30 + 49.0 MC_31 + 35.0 MC_32 + 45.0 MC_33 + 27.0 MC_34 + 42.0 MC_35 + 45.0 MC_36 + 12.0 MC_37 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 6.0 z1 - 6.0 z2 + 3.0 z3 - 3.0 z4 + 22.0 z5 - 22.0 z6 s.t. 1.0 x_11 + 1.0 x_21 - 1.0 local_art_of_cov_1 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_34 + z3 - z4 <= 1.0 1.0 x_12 + 1.0 x_22 - 1.0 local_art_of_cov_2 - 1.0 global_neg_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_37 + z1 - z2 <= 1.0 1.0 x_13 + 1.0 x_23 - 1.0 local_art_of_cov_3 - 1.0 global_neg_art_var + 1.0 MC_31 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_35 <= 1.0 1.0 x_14 + 1.0 x_24 - 1.0 local_art_of_cov_4 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_31 + 1.0 MC_36 <= 1.0 1.0 x_15 + 1.0 x_25 - 1.0 local_art_of_cov_5 - 1.0 global_neg_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + z5 - z6 <= 1.0 1.0 x_16 + 1.0 x_26 - 1.0 local_art_of_cov_6 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_33 <= 1.0 1.0 x_17 + 1.0 x_27 - 1.0 local_art_of_cov_7 - 1.0 global_neg_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 <= 1.0 {MasterConvexityConstr} dw_sp max x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp max x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_30, MC_31, MC_32, MC_33, MC_34, MC_35, MC_36, MC_37 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var pure z1, z2, z3, z4, z5, z6 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_30 >= 0.0 MC_31 >= 0.0 MC_32 >= 0.0 MC_33 >= 0.0 MC_34 >= 0.0 MC_35 >= 0.0 MC_36 >= 0.0 MC_37 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_with_penalties() # We add variables z1 and z2 to fix the dual value of constraint c4 to 21. # We add variables z3 and z4 to fix the dual value of constraint c3 to 18.56666667. # We add variables z5 and z6 to fix the dual value of constraint c7 to 19.26666667. # We add variables z7 and z8 to fix the dual value of constraint c1 to 8.26666667. # We add variables z9 and z10 to fix the dual value of constraint c2 to 17.13333333 form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 21 z1 - 21 z2 + 18.56666667 z3 -18.56666667 z4 + 19.26666667 z5 - 19.26666667 z6 + 8.26666667 z7 - 8.26666667 z8 + 17.13333333 z9 - 17.13333333 z10 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + z7 - z8 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 + z9 - z10 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 + z3 - z4 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 + z1 - z2 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 + z5 - z6 >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7, z1, z2, z3, z4, z5, z6, z7, z8, z9, z10 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 0.0 <= y_1 <= 1.0 0.0 <= y_2 <= 1.0 0.0 <= y_3 <= 1.0 0.0 <= y_4 <= 1.0 0.0 <= y_5 <= 1.0 0.0 <= y_6 <= 1.0 0.0 <= y_7 <= 1.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_with_obj_const() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 51.0 MC_30 + 38.0 MC_31 + 31.0 MC_32 + 35.0 MC_33 + 48.0 MC_34 + 13.0 MC_35 + 53.0 MC_36 + 28.0 MC_37 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 + 700.0 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_32 + 1.0 MC_33 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_37 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_33 + 1.0 MC_34 + 1.0 MC_35 + 1.0 MC_36 + 1.0 MC_37 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_31 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_36 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_30 + 1.0 MC_34 + 1.0 MC_36 + 1.0 MC_37 >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_30 + 1.0 MC_32 + 1.0 MC_34 + 1.0 MC_36 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_31 + 1.0 MC_33 + 1.0 MC_35 + 1.0 MC_37 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_30, MC_31, MC_32, MC_33, MC_34, MC_35, MC_36, MC_37 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_30 >= 0.0 MC_31 >= 0.0 MC_32 >= 0.0 MC_33 >= 0.0 MC_34 >= 0.0 MC_35 >= 0.0 MC_36 >= 0.0 MC_37 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function check_identical_subproblems() # Used to check the output of identical_subproblem. The two formulations should be equivalent. # Subproblem 5 is introduced twice. form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 8.0 x_21 + 5.0 x_22 + 11.0 x_23 + 21.0 x_24 + 6.0 x_25 + 5.0 x_26 + 19.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 8.0 dw_sp min 8.0 x_21 + 5.0 x_22 + 11.0 x_23 + 21.0 x_24 + 6.0 x_25 + 5.0 x_26 + 19.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 2.0 x_21 + 3.0 x_22 + 3.0 x_23 + 1.0 x_24 + 2.0 x_25 + 1.0 x_26 + 1.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end ### Implementation of ColGen API to test and call the default implementation struct TestColGenIterationContext <: ColGen.AbstractColGenContext context::ClA.ColGenContext master_lp_primal_sol::Dict{String, Float64} master_lp_dual_sol::Dict{String, Float64} master_lp_obj_val::Float64 pricing_var_reduced_costs::Dict{String, Float64} end ColGen.get_reform(ctx::TestColGenIterationContext) = ColGen.get_reform(ctx.context) ColGen.get_master(ctx::TestColGenIterationContext) = ColGen.get_master(ctx.context) ColGen.is_minimization(ctx::TestColGenIterationContext) = ColGen.is_minimization(ctx.context) ColGen.get_pricing_subprobs(ctx::TestColGenIterationContext) = ColGen.get_pricing_subprobs(ctx.context) ColGen.colgen_iteration_output_type(::TestColGenIterationContext) = ClA.ColGenIterationOutput struct TestColGenStage <: ColGen.AbstractColGenStage end ColGen.get_pricing_subprob_optimizer(::TestColGenStage, _) = 1 function ColGen.optimize_master_lp_problem!(master, ctx::TestColGenIterationContext, env) output = ColGen.optimize_master_lp_problem!(master, ctx.context, env) primal_sol = ColGen.get_primal_sol(output) for (var_id, var) in ClMP.getvars(master) name = ClMP.getname(master, var) if !haskey(ctx.master_lp_primal_sol, name) @test primal_sol[var_id] ≈ 0.0 else @test primal_sol[var_id] ≈ ctx.master_lp_primal_sol[name] end end dual_sol = ColGen.get_dual_sol(output) for (constr_id, constr) in ClMP.getconstrs(master) name = ClMP.getname(master, constr) if !haskey(ctx.master_lp_dual_sol, name) @test dual_sol[constr_id] ≈ 0.0 else @test dual_sol[constr_id] ≈ ctx.master_lp_dual_sol[name] end end return output end ColGen.check_primal_ip_feasibility!(master_lp_primal_sol, ::TestColGenIterationContext, phase, env) = nothing, false ColGen.is_unbounded(ctx::TestColGenIterationContext) = ColGen.is_unbounded(ctx.context) ColGen.is_infeasible(ctx::TestColGenIterationContext) = ColGen.is_infeasible(ctx.context) ColGen.update_master_constrs_dual_vals!(ctx::TestColGenIterationContext, master_lp_dual_sol) = ColGen.update_master_constrs_dual_vals!(ctx.context, master_lp_dual_sol) ColGen.update_reduced_costs!(ctx::TestColGenIterationContext, phase, red_costs) = nothing ColGen.get_subprob_var_orig_costs(ctx::TestColGenIterationContext) = ColGen.get_subprob_var_orig_costs(ctx.context) ColGen.get_subprob_var_coef_matrix(ctx::TestColGenIterationContext) = ColGen.get_subprob_var_coef_matrix(ctx.context) function ColGen.update_sp_vars_red_costs!(ctx::TestColGenIterationContext, sp::Formulation{DwSp}, red_costs) ColGen.update_sp_vars_red_costs!(ctx.context, sp, red_costs) for (_, var) in ClMP.getvars(sp) name = ClMP.getname(sp, var) @test ctx.pricing_var_reduced_costs[name] ≈ ClMP.getcurcost(sp, var) end return end ColGen.compute_sp_init_pb(ctx::TestColGenIterationContext, sp::Formulation{DwSp}) = ColGen.compute_sp_init_pb(ctx.context, sp) ColGen.compute_sp_init_db(ctx::TestColGenIterationContext, sp::Formulation{DwSp}) = ColGen.compute_sp_init_db(ctx.context, sp) ColGen.set_of_columns(ctx::TestColGenIterationContext) = ColGen.set_of_columns(ctx.context) ColGen.push_in_set!(ctx::TestColGenIterationContext, set, col) = ColGen.push_in_set!(ctx.context, set, col) # Columns insertion function ColGen.insert_columns!(ctx::TestColGenIterationContext, phase, columns) return ColGen.insert_columns!(ctx.context, phase, columns) end function ColGen.optimize_pricing_problem!(ctx::TestColGenIterationContext, sp::Formulation{DwSp}, env, optimizer, master_dual_sol, stab_changes_mast_dual_sol) output = ColGen.optimize_pricing_problem!(ctx.context, sp, env, optimizer, master_dual_sol, stab_changes_mast_dual_sol) # test here return output end function ColGen.compute_dual_bound(ctx::TestColGenIterationContext, phase, sp_dbs, generated_columns, master_dual_sol) return ColGen.compute_dual_bound(ctx.context, phase, sp_dbs, generated_columns, master_dual_sol) end function test_colgen_iteration_min_gap() env, master, sps, reform = min_toy_gap() # vids = get_name_to_varids(master) # cids = get_name_to_constrids(master) master_lp_primal_sol = Dict( "MC_30" => 1/3, "MC_31" => 2/3, "MC_32" => 1/3, "MC_36" => 1/3, "MC_37" => 1/3, ) master_lp_dual_sol = Dict( "c1" => 11.33333333, "c2" => 17.33333333, "c5" => 9.33333333, "c6" => 13.66666667, "c7" => 28.0, ) master_obj_val = 79.67 pricing_var_reduced_costs = Dict( "x_11" => - 3.3333333300000003, "x_12" => - 12.333333329999999, "x_13" => 11.0, "x_14" => 21.0, "x_15" => - 3.3333333300000003, "x_16" => - 8.66666667, "x_17" => - 9.0, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 10.33333333, "x_22" => - 5.3333333299999985, "x_23" => 11.0, "x_24" => 12.0, "x_25" => 4.66666667, "x_26" => - 5.66666667, "x_27" => - 23.0, "PricingSetupVar_sp_4" => 0.0, ) # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 79.666666667 @test output.db ≈ 21.3333333333 @test output.nb_new_cols == 2 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_min_gap) function test_colgen_iteration_max_gap() env, master, sps, reform = max_toy_gap() master_lp_primal_sol = Dict( "MC_30" => 0.5, "MC_31" => 0.5, "MC_33" => 0.5, "MC_34" => 0.5, ) master_lp_dual_sol = Dict( "c1" => 3.0, # fixed "c2" => 6.0, # fixed "c4" => 15.0, "c5" => 22.0, # fixed "c6" => 11.0, "c7" => 8.0, "c9" => 16.0, "c11" => 6.0 ) master_obj_val = 87.00 pricing_var_reduced_costs = Dict( "x_11" => 5.0, "x_12" => - 1.0, "x_13" => 11.0, "x_14" => 6.0, "x_15" => - 16.0, "x_16" => - 6.0, "x_17" => 11.0, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 2.0, "x_22" => 6.0, "x_23" => 11.0, "x_24" => - 3.0, "x_25" => - 8.0, "x_26" => - 3.0, "x_27" => - 3.0, "PricingSetupVar_sp_4" => 0.0, ) ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 87.00 @test output.db ≈ 110.00 @test output.nb_new_cols == 2 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_max_gap) function test_colgen_iteration_pure_master_vars() env, master, sps, reform = toy_gap_with_penalties() master_lp_primal_sol = Dict( "MC_41" => 1, "MC_42" => 1, "y_2" => 1, ) master_lp_dual_sol = Dict( "c1" => 8.26666667, # fixed "c2" => 17.13333333, # fixed "c3" => 18.56666667, # fixed "c4" => 21.0, # fixed "c5" => 17.86666667, "c6" => 15.41666667, "c7" => 19.26666667, # fixed "c8" => -10.86666667, "c10" => -22.55, "c12" => -30.83333334 ) master_obj_val = 52.95 pricing_var_reduced_costs = Dict( "x_11" => - 0.26666666999999933, "x_12" => - 12.13333333, "x_13" => - 7.56666667, "x_14" => 0.0, "x_15" => - 11.86666667, "x_16" => - 10.41666667, "x_17" => - 0.26666666999999933, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 7.266666669999999, "x_22" => - 5.133333329999999, "x_23" => - 7.56666667, "x_24" => - 9.0, "x_25" => - 3.8666666700000007, "x_26" => - 7.41666667, "x_27" => - 14.26666667, "PricingSetupVar_sp_4" => 0.0, ) # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 52.9500 @test output.db ≈ 51.5 @test output.nb_new_cols == 1 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_pure_master_vars) function test_colgen_iteration_obj_const() env, master, sps, reform = toy_gap_with_obj_const() master_lp_primal_sol = Dict( "MC_30" => 1/3, "MC_31" => 2/3, "MC_32" => 1/3, "MC_36" => 1/3, "MC_37" => 1/3, ) master_lp_dual_sol = Dict( "c1" => 11.33333333, "c3" => 17.33333333, "c5" => 9.33333333, "c6" => 31.0, "c7" => 10.66666667, ) master_obj_val = 779.67 pricing_var_reduced_costs = Dict( "x_11" => - 3.3333333300000003, "x_12" => 5.0, "x_13" => - 6.3333333299999985, "x_14" => 21.0, "x_15" => - 3.3333333300000003, "x_16" => - 26.0, "x_17" => 8.33333333, "PricingSetupVar_sp_5" => 0.0, "x_21" => - 10.33333333, "x_22" => 12.0, "x_23" => - 6.3333333299999985, "x_24" => 12.0, "x_25" => 4.66666667, "x_26" => - 23.0, "x_27" => - 5.66666667, "PricingSetupVar_sp_4" => 0.0, ) # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = TestColGenIterationContext( ClA.ColGenContext(reform, ClA.ColumnGeneration()), master_lp_primal_sol, master_lp_dual_sol, master_obj_val, pricing_var_reduced_costs, ) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_iteration!(ctx, ClA.ColGenPhase0(), TestColGenStage(), env, input, Coluna.Algorithm.NoColGenStab()) @test output.mlp ≈ 779.6666666666667 @test output.db ≈ 717.6666666766668 @test output.nb_new_cols == 2 @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false end register!(unit_tests, "colgen_default", test_colgen_iteration_obj_const) ############################################################################################ # Test column insertion ############################################################################################ function insert_cols_form() # We introduce variables z1 & z2 to force dual value of constraint c7 to equal to 28. form = """ master min x1 + x2 + x3 + x4 + x5 s.t. x1 + x2 + x3 + x4 + x5 >= 0.0 dw_sp min x1 + x2 + x3 + x4 + x5 continuous representatives x1, x2, x3, x4, x5 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_two_identicals_cols_at_two_iterations_failure() env, master, sps, reform = insert_cols_form() spform = sps[1] spvarids = Dict(CL.getname(spform, var) => varid for (varid, var) in CL.getvars(spform)) phase = ClA.ColGenPhase0() ## Iteration 1 ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( throw_column_already_inserted_warning = true )) redcosts_spsols = [-2.0, 2.0] col1 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col2 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x2", "x3"]), [5.0, 2.0], 2.5, ClB.FEASIBLE_SOL ) # not inserted because positive reduced cost. columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col1, col2]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end new_cols = ColGen.insert_columns!(ctx, phase, columns) @test length(new_cols) == 1 ## Iteration 2 redcosts_spsols = [-1.0] col3 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 3.0, ClB.FEASIBLE_SOL ) columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col3]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end @test_throws ClA.ColumnAlreadyInsertedColGenWarning ColGen.insert_columns!(ctx, phase, columns) end register!(unit_tests, "colgen_default", test_two_identicals_cols_at_two_iterations_failure) function test_two_identicals_cols_at_same_iteration_ok() env, master, sps, reform = insert_cols_form() spform = sps[1] spvarids = Dict(CL.getname(spform, var) => varid for (varid, var) in CL.getvars(spform)) phase = ClA.ColGenPhase0() redcosts_spsols = [-2.0, -2.0, 2.0] col1 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col2 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 2.0, ClB.FEASIBLE_SOL ) col3 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x2", "x3"]), [5.0, 2.0], 3.5, ClB.FEASIBLE_SOL ) # not inserted because positive reduced cost. ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( throw_column_already_inserted_warning = true )) columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col1, col2, col3]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end new_cols = ColGen.insert_columns!(ctx, phase, columns) @test length(new_cols) == 2 end register!(unit_tests, "colgen_default", test_two_identicals_cols_at_same_iteration_ok) function test_deactivated_column_added_twice_at_same_iteration_ok() env, master, sps, reform = insert_cols_form() spform = sps[1] spvarids = Dict(CL.getname(spform, var) => varid for (varid, var) in CL.getvars(spform)) phase = ClA.ColGenPhase0() ## Add column. col1 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col_id = ClMP.insert_column!(master, col1, "MC") ## Deactivate column. ClMP.deactivate!(master, col_id) redcosts_spsols = [-2.0, -2.0] col2 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 1.0, ClB.FEASIBLE_SOL ) col3 = ClMP.PrimalSolution( spform, map(x -> spvarids[x], ["x1", "x3"]), [1.0, 2.0], 2.0, ClB.FEASIBLE_SOL ) ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( throw_column_already_inserted_warning = true )) columns = ColGen.set_of_columns(ctx) for (cost, sol) in Iterators.zip(redcosts_spsols, [col2, col3]) ColGen.push_in_set!(ctx, columns, ClA.GeneratedColumn(sol, cost)) end new_cols = ColGen.insert_columns!(ctx, phase, columns) @test length(new_cols) == 1 end register!(unit_tests, "colgen_default", test_deactivated_column_added_twice_at_same_iteration_ok) ############################################################################################ # Test the column generation loop ############################################################################################ function min_toy_gap_for_colgen_loop() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_colgen_loop() env, master, sps, reform = min_toy_gap_for_colgen_loop() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end phase = ClA.ColGenPhase0() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) ColGen.setup_reformulation!(reform, phase) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run_colgen_phase!(ctx, phase, ColGenIterationTestStage(), env, input, Coluna.Algorithm.NoColGenStab()) # EXPECTED: # """ # <it= 11> <et=19.92> <mst= 0.00> <sp= 0.00> <cols= 0> <al= 0.00> <DB= 70.3333> <mlp= 70.3333> <PB=89.0000> # [ Info: Column generation algorithm has converged. # """ @test output.mlp ≈ 70.33333333 @test output.db ≈ 70.33333333 @test Coluna.ColunaBase.getvalue(output.master_ip_primal_sol) ≈ 89.0 return end register!(unit_tests, "colgen_default", test_colgen_loop) function min_toy_gap_for_colgen() # We use very large costs to go through phase 1. form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_identical_subproblems() env, master, sps, reform = identical_subproblems() ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 75 @test output.mlp ≈ 75 end register!(unit_tests, "colgen_default", test_identical_subproblems) # Don't run this test because we use it to check the output of the previous test. function expected_output_identical_subproblems() env, master, sps, reform = check_identical_subproblems() ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 75 @test output.db ≈ 75 end register!(unit_tests, "colgen_default", expected_output_identical_subproblems) function test_colgen() env, master, sps, reform = min_toy_gap_for_colgen() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end Coluna.set_optim_start_time!(env) ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 7033.3333333 @test output.db ≈ 7033.3333333 end register!(unit_tests, "colgen", test_colgen) function identical_subproblems() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 1.0 x_11 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 2.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_5 binary representatives x_11, x_12, x_13, x_14, x_15, x_16, x_17 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_17 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function r1c_form() form = """ master min 1.0 MC_1 + 1.0 MC_2 + 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 + 8.0 x_1 + 1.0 x_2 + 3.0 x_3 + 11.0 x_4 + 7.0 x_5 s.t. 1.0 MC_1 + 1.0 MC_2 + 1.0 MC_4 + 1.0 x_1 >= 1.0 1.0 MC_1 + 1.0 MC_2 + 1.0 MC_4 + 1.0 MC_5 + 1.0 x_2 >= 1.0 1.0 MC_2 + 1.0 MC_3 + 1.0 MC_5 + 1.0 x_3 >= 1.0 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 + 1.0 x_4 >= 1.0 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 + 1.0 x_5 >= 1.0 0.0 MC_1 + 1.0 MC_2 + 1.0 MC_3 + 1.0 MC_4 + 1.0 MC_5 <= 1.0 dw_sp min 8.0 x_1 + 1.0 x_2 + 3.0 x_3 + 11.0 x_4 + 7.0 x_5 s.t. 2.0 x_1 + 3.0 x_2 + 3.0 x_3 <= 8.0 continuous columns MC_1, MC_2, MC_3, MC_4, MC_5 binary representatives x_1, x_2, x_3, x_4, x_5 bounds 0.0 <= x_1 <= 1.0 0.0 <= x_2 <= 1.0 0.0 <= x_3 <= 1.0 0.0 <= x_4 <= 1.0 0.0 <= x_5 <= 1.0 MC_1 >= 0.0 MC_2 >= 0.0 MC_3 >= 0.0 MC_4 >= 0.0 MC_5 >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_red_cost_calc_with_non_robust_cuts() var_costs = [ 8.0, 1.0, 3.0, 11.0, 7.0 ] A = [ 1 0 0 0 0; 0 1 0 0 0; 0 0 1 0 0; 0 0 0 1 0; 0 0 0 0 1; 0 0 0 0 0 ] constr_costs = [2.0, 8.0, 1.0, 3.0, 9.0, 4.0] expected_redcosts = var_costs - transpose(A) * constr_costs form = r1c_form() env, master, sps, reform = form constrids = Dict(getname(master, id) => id for (id,_) in ClA.getconstrs(master)) varids = Dict(getname(master, id) => id for (id,_) in ClA.getvars(master)) dual_sol = ClA.DualSolution( master, map(name -> constrids[name], ["c1", "c2", "c3", "c4", "c5", "c6"]), constr_costs, [], [], [], 0.0, FEASIBLE_SOL ) helper = ClA.ReducedCostsCalculationHelper(master) coeffs = transpose(helper.dw_subprob_A) * dual_sol redcosts = helper.dw_subprob_c - coeffs @test redcosts[varids["x_1"]] == expected_redcosts[1] @test redcosts[varids["x_2"]] == expected_redcosts[2] @test redcosts[varids["x_3"]] == expected_redcosts[3] @test redcosts[varids["x_4"]] == expected_redcosts[4] @test redcosts[varids["x_5"]] == expected_redcosts[5] end register!(unit_tests, "colgen", test_red_cost_calc_with_non_robust_cuts) function jet_report_colgen_loop() env, master, sps, reform = min_toy_gap_for_colgen_loop() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end phase = ClA.ColGenPhase0() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) ColGen.setup_reformulation!(reform, phase) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) stage = ColGenIterationTestStage() stab = Coluna.Algorithm.NoColGenStab() @test_call ColGen.run_colgen_phase!(ctx, phase, stage, env, input, stab) # @test output.mlp ≈ 70.33333333 # @test output.db ≈ 70.33333333 return end # Excluded at the moment because too many errors come from DynamicSparseArrays. register!(unit_tests, "colgen_default", jet_report_colgen_loop; x = true)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

15729

# Test using the column generation example for Integer Programming book by Wolsey # See page 191. # There is no so much logic to test in the generic implementation of an iteration of column # generation: # - calculation of reduced costs # - calculation of the master lp dual bound # - flow # - error handling # - output # This is the problems that we consider here: # master # min # 7x_12 + 2x_13 + x_14 + 5x_15 + 3x_23 + 6x_24 + 8x_25 + 4x_34 + 2x_35 + 9x_45 + 28λ1 + 25λ2 + 21λ3 + 19λ4 + 22λ5 + 18λ6 + 28λ7 # s.t. # x_12 + x_13 + x_14 + x_15 + 2λ1 + 2λ2 + 2λ3 + 2λ4 + 2λ5 + 2λ6 + 2λ7 == 2 # x_12 + x_23 + x_24 + x_25 + 2λ1 + 2λ2 + 2λ3 + 1λ4 + 1λ5 + 2λ6 + 3λ7 == 2 # x_13 + x_23 + x_34 + x_35 + 2λ1 + 3λ2 + 2λ3 + 3λ4 + 2λ5 + 3λ6 + 1λ7 == 2 # x_14 + x_24 + x_34 + x_45 + 2λ1 + 2λ2 + 3λ3 + 3λ4 + 3λ5 + 1λ6 + 1λ7 == 2 # x_15 + x_25 + x_35 + x_45 + 2λ1 + 1λ2 + 1λ3 + 1λ4 + 2λ5 + 2λ6 + 3λ7 == 2 # dw_sp # min # 7x_12 + 2x_13 + x_14 + 5x_15 + 3x_23 + 6x_24 + 8x_25 + 4x_34 + 2x_35 + 9x_45 # s.t. # x_12 + x_13 + x_14 + x_15 == 1 # continuous # columns # λ1, λ2, λ3, λ4, λ5, λ6, λ7 # integer # representatives # x_12, x_13, x_14, x_15, x_23, x_24, x_25, x_34, x_35, x_45 # bounds # λ1 >= 0 # λ2 >= 0 # λ3 >= 0 # λ4 >= 0 # λ5 >= 0 # λ6 >= 0 # λ7 >= 0 # x_12 >= 0 # x_13 >= 0 # x_14 >= 0 # x_15 >= 0 # x_23 >= 0 # x_24 >= 0 # x_25 >= 0 # x_34 >= 0 # x_35 >= 0 # x_45 >= 0 mutable struct GlobalTestColGenIterationPrimalHandler global_primal_sol::Union{Nothing, Vector{Float64}} end GlobalTestColGenIterationPrimalHandler() = GlobalTestColGenIterationPrimalHandler(nothing) struct ColGenIterationTestMaster end struct ColGenIterationTestPricing end struct ColGenIterationTestReform end # Column generation context # We have many flags here to test lots of different scenarios that might happens if # we there is a bug in the subsolver or MOI. Base.@kwdef struct ColGenIterationTestContext <: ColGen.AbstractColGenContext master_term_status::ClB.TerminationStatus = ClB.OPTIMAL master_solver_has_no_primal_solution::Bool = false master_solver_has_no_dual_solution::Bool = false pricing_term_status::ClB.TerminationStatus = ClB.OPTIMAL pricing_has_correct_dual_bound::Bool = true pricing_has_incorrect_dual_bound::Bool = false pricing_has_no_dual_bound::Bool = false pricing_solver_has_no_solution::Bool = false new_ip_primal_sol::Bool = false master_has_new_cuts::Bool = false reform = ColGenIterationTestReform() master = ColGenIterationTestMaster() pricing = ColGenIterationTestPricing() end ColGen.get_master(ctx::ColGenIterationTestContext) = ctx.master ColGen.get_reform(ctx::ColGenIterationTestContext) = ctx.reform ColGen.is_minimization(ctx::ColGenIterationTestContext) = true ColGen.get_pricing_subprobs(context) = [(1, context.pricing)] ColGen.colgen_iteration_output_type(::ColGenIterationTestContext) = ClA.ColGenIterationOutput ColGen.colgen_phase_output_type(::ColGenIterationTestContext) = ClA.ColGenPhaseOutput # Stage struct ColGenIterationTestStage <: ColGen.AbstractColGenStage end ColGen.get_pricing_subprob_optimizer(::ColGenIterationTestStage, _) = 1 ColGen.is_exact_stage(::ColGenIterationTestStage) = true # Pricing strategy struct ColGenIterationTestPricingStrategy <: ColGen.AbstractPricingStrategy subprobs::Vector{Tuple{Int, ColGenIterationTestPricing}} end ColGen.get_pricing_strategy(context::ColGenIterationTestContext, _) = ColGenIterationTestPricingStrategy(ColGen.get_pricing_subprobs(context)) ColGen.pricing_strategy_iterate(strategy::ColGenIterationTestPricingStrategy) = iterate(strategy.subprobs) ColGen.pricing_strategy_iterate(strategy::ColGenIterationTestPricingStrategy, state) = iterate(strategy.subprobs, state) # Column generation phase struct ColGenIterationTestPhase <: ColGen.AbstractColGenPhase end # Master struct ColGenIterationTestMasterResult term_status::ClB.TerminationStatus obj_val::Union{Nothing,Float64} primal_sol::Union{Nothing, Vector{Float64}} dual_sol::Union{Nothing, Vector{Float64}} end ColGen.get_primal_sol(res::ColGenIterationTestMasterResult) = res.primal_sol ColGen.get_dual_sol(res::ColGenIterationTestMasterResult) = res.dual_sol ColGen.get_obj_val(res::ColGenIterationTestMasterResult) = res.obj_val ColGen.is_infeasible(res::ColGenIterationTestMasterResult) = res.term_status == ClB.INFEASIBLE ColGen.is_unbounded(res::ColGenIterationTestMasterResult) = res.term_status == ClB.UNBOUNDED ## mock of the master lp solver function ColGen.optimize_master_lp_problem!(master, ctx::ColGenIterationTestContext, env) obj_val = nothing primal_sol = nothing dual_sol = nothing if ctx.master_term_status == ClB.OPTIMAL obj_val = 22.5 primal_sol = [0, 0, 1/4, 0, 1/4, 1/4, 1/4] dual_sol = [151/8, -1, -11/2, -5/4, 0] end return ColGenIterationTestMasterResult(ctx.master_term_status, obj_val, primal_sol, dual_sol) end # Pricing struct ColGenIterationTestPricingResult term_status::ClB.TerminationStatus primal_sols::Vector{Vector{Float64}} primal_bound::Union{Nothing, Float64} dual_bound::Union{Nothing, Float64} end ColGen.get_primal_sols(res::ColGenIterationTestPricingResult) = res.primal_sols ColGen.get_primal_bound(res::ColGenIterationTestPricingResult) = res.primal_bound ColGen.get_dual_bound(res::ColGenIterationTestPricingResult) = res.dual_bound ColGen.compute_sp_init_db(::ColGenIterationTestContext, sp) = -Inf ColGen.compute_sp_init_pb(::ColGenIterationTestContext, sp) = Inf ColGen.set_of_columns(::ColGenIterationTestContext) = Vector{Float64}[] ColGen.is_infeasible(res::ColGenIterationTestPricingResult) = res.term_status == ClB.INFEASIBLE ColGen.is_unbounded(res::ColGenIterationTestPricingResult) = res.term_status == ClB.UNBOUNDED function ColGen.push_in_set!(ctx::ColGenIterationTestContext, set::Vector{Vector{Float64}}, col::Vector) push!(set, col) return true end ## mock of the pricing solver function ColGen.optimize_pricing_problem!(ctx::ColGenIterationTestContext, form, env, optimizer, master_dual_sol, stab_changes_mast_dual_sol) primal_val = nothing dual_val = nothing sols = Vector{Float64}[] if !ctx.pricing_solver_has_no_solution push!(sols, [0, 1, 1, 0, 1, 1, 0, 0, 1, 0]) primal_val = -23/4 end if ctx.pricing_has_correct_dual_bound dual_val = -23/4 elseif ctx.pricing_has_incorrect_dual_bound dual_val = -47 # this value is lower than the correct dual bound (minimization problem!!) else @assert ctx.pricing_has_no_dual_bound end return ColGenIterationTestPricingResult(ctx.pricing_term_status, sols, primal_val, dual_val) end # Reduced costs ColGen.get_subprob_var_orig_costs(::ColGenIterationTestContext) = [7, 2, 1, 5, 3, 6, 8, 4, 2, 9] ColGen.get_subprob_var_coef_matrix(::ColGenIterationTestContext) = [ 1 1 1 1 0 0 0 0 0 0; 1 0 0 0 1 1 1 0 0 0; 0 1 0 0 1 0 0 1 1 0; 0 0 1 0 0 1 0 1 0 1; 0 0 0 1 0 0 1 0 1 1 ] function ColGen.update_sp_vars_red_costs!(::ColGenIterationTestContext, subprob, red_costs) # We check that reduced costs are correct. @test reduce(&, red_costs .== [-87/8, -91/8, -133/8, -111/8, 19/2, 33/4, 9, 43/4, 15/2, 41/4]) return end ColGen.update_reduced_costs!(::ColGenIterationTestContext, phase, red_costs) = nothing function ColGen.check_primal_ip_feasibility!(sol, ctx::ColGenIterationTestContext, ::ColGenIterationTestPhase, env) if ctx.new_ip_primal_sol @assert !ctx.master_has_new_cuts return [7.0, 7.0, 7.0], false end if ctx.master_has_new_cuts @assert !ctx.new_ip_primal_sol return nothing, true end return nothing, false end ColGen.is_better_primal_sol(::Vector{Float64}, p) = isnothing(p.global_primal_sol) function ColGen.update_inc_primal_sol!(::ColGenIterationTestContext, sol::GlobalTestColGenIterationPrimalHandler, new_sol) if isnothing(sol.global_primal_sol) sol.global_primal_sol = new_sol end @test sol.global_primal_sol == [7.0, 7.0, 7.0] end ColGen.update_master_constrs_dual_vals!(::ColGenIterationTestContext, dual_mast_sol) = nothing function ColGen.insert_columns!(::ColGenIterationTestContext, phase, generated_columns) @test length(generated_columns) == 1 @test generated_columns[1] == [0, 1, 1, 0, 1, 1, 0, 0, 1, 0] return [1] end function ColGen.compute_dual_bound(::ColGenIterationTestContext, ::ColGenIterationTestPhase, sp_dbs, generated_columns, mast_dual_sol) return 22.5 - 23/4 end ColGen.update_stabilization_after_pricing_optim!(::Coluna.Algorithm.NoColGenStab, ::ColGenIterationTestContext, _, _, _, _) = nothing struct TestColGenIterationOutput <: ColGen.AbstractColGenIterationOutput min_sense::Bool mlp::Union{Nothing, Float64} db::Union{Nothing, Float64} nb_new_cols::Int new_cut_in_master::Bool infeasible_master::Bool unbounded_master::Bool infeasible_subproblem::Bool unbounded_subproblem::Bool time_limit_reached::Bool master_lp_primal_sol::Union{Nothing, Vector{Float64}} master_ip_primal_sol::Union{Nothing, Vector{Float64}} master_lp_dual_sol::Union{Nothing, Vector{Float64}} end ColGen.colgen_iteration_output_type(::ColGenIterationTestContext) = TestColGenIterationOutput function ColGen.new_iteration_output(::Type{<:TestColGenIterationOutput}, min_sense, mlp, db, nb_new_cols, new_cut_in_master, infeasible_master, unbounded_master, infeasible_subproblem, unbounded_subproblem, time_limit_reached, master_lp_primal_sol, master_ip_primal_sol, master_lp_dual_sol ) master_ip_primal_sol = isnothing(master_ip_primal_sol) ? nothing : master_ip_primal_sol.global_primal_sol return TestColGenIterationOutput( min_sense, mlp, db, nb_new_cols, new_cut_in_master, infeasible_master, unbounded_master, infeasible_subproblem, unbounded_subproblem, time_limit_reached, master_lp_primal_sol, master_ip_primal_sol, master_lp_dual_sol ) end ColGen.update_stabilization_after_pricing_optim!(::Coluna.Algorithm.NoColGenStab, ::TestColGenIterationContext, _, _, _, _) = nothing function colgen_iteration_master_ok_pricing_ok() ctx = ColGenIterationTestContext() output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test output.mlp == 22.5 @test output.db == 22.5 - 23/4 @test output.nb_new_cols == 1 @test output.new_cut_in_master == false @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_iteration_master_ok_pricing_ok) function colgen_iteration_master_infeasible() ctx = ColGenIterationTestContext( master_term_status = ClB.INFEASIBLE, master_solver_has_no_primal_solution = true, master_solver_has_no_dual_solution = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test isnothing(output.mlp) @test output.db == Inf @test output.nb_new_cols == 0 @test output.new_cut_in_master == false @test output.infeasible_master == true @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_iteration_master_infeasible) function colgen_iteration_pricing_infeasible() ctx = ColGenIterationTestContext( pricing_term_status = ClB.INFEASIBLE, pricing_solver_has_no_solution = true, pricing_has_no_dual_bound = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test isnothing(output.mlp) @test output.db == Inf @test output.nb_new_cols == 0 @test output.new_cut_in_master == false @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == true @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_iteration_pricing_infeasible) function colgen_iteration_master_unbounded() ctx = ColGenIterationTestContext( master_term_status = ClB.UNBOUNDED, master_solver_has_no_primal_solution = true, master_solver_has_no_dual_solution = true ) @test_throws ColGen.UnboundedProblemError ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) end register!(unit_tests, "colgen_iteration", colgen_iteration_master_unbounded) function colgen_iteration_pricing_unbounded() ctx = ColGenIterationTestContext( pricing_term_status = ClB.UNBOUNDED, pricing_solver_has_no_solution = true, pricing_has_no_dual_bound = true ) @test_throws ColGen.UnboundedProblemError ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) end register!(unit_tests, "colgen_iteration", colgen_iteration_pricing_unbounded) function colgen_finds_ip_primal_sol() ctx = ColGenIterationTestContext( new_ip_primal_sol = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test output.mlp == 22.5 @test output.db == 22.5 - 23/4 @test output.nb_new_cols == 1 @test output.new_cut_in_master == false @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test output.master_ip_primal_sol == [7.0, 7.0, 7.0] end register!(unit_tests, "colgen_iteration", colgen_finds_ip_primal_sol) function colgen_new_cuts_in_master() ctx = ColGenIterationTestContext( master_has_new_cuts = true ) output = ColGen.run_colgen_iteration!(ctx, ColGenIterationTestPhase(), ColGenIterationTestStage(), nothing, GlobalTestColGenIterationPrimalHandler(), Coluna.Algorithm.NoColGenStab()) @test isnothing(output.mlp) @test isnothing(output.db) @test output.nb_new_cols == 0 @test output.new_cut_in_master == true @test output.infeasible_master == false @test output.unbounded_master == false @test output.infeasible_subproblem == false @test output.unbounded_subproblem == false @test isnothing(output.master_ip_primal_sol) end register!(unit_tests, "colgen_iteration", colgen_new_cuts_in_master)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

21645

struct TestColGenOutput <: ColGen.AbstractColGenPhaseOutput has_art_vars::Bool new_cuts_in_master::Bool exact_stage::Bool has_converged::Bool end ClA.colgen_master_has_new_cuts(ctx::TestColGenOutput) = ctx.new_cuts_in_master ClA.colgen_mast_lp_sol_has_art_vars(ctx::TestColGenOutput) = ctx.has_art_vars ClA.colgen_uses_exact_stage(ctx::TestColGenOutput) = ctx.exact_stage ClA.colgen_has_converged(ctx::TestColGenOutput) = ctx.has_converged # The two following tests are pretty straightforward. # They are just here to make sure nobody changes the behavior of the phases. function initial_phase_colgen_test() it = ClA.ColunaColGenPhaseIterator() @test ColGen.initial_phase(it) isa ClA.ColGenPhase0 end register!(unit_tests, "colgen_phase", initial_phase_colgen_test) function next_phase_colgen_test() # Classic case where we use exact phase and the algorithm has converged. it = ClA.ColunaColGenPhaseIterator() table = [ # Current phase | art vars | new cut | exact stage | converged | next expected phase | err | err_type ( ClA.ColGenPhase1() , false , false , false , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , false , false , true , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , false , true , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , false , true , true , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase1() , false , true , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , false , true , false , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , false , true , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , false , true , true , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , false , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , false , false , true , ClA.ColGenPhase1() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase1() , true , false , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , false , true , true , nothing , false , nothing ), # infeasible ( ClA.ColGenPhase1() , true , true , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , true , false , true , ClA.ColGenPhase1() , false , nothing ), # infeasible ( ClA.ColGenPhase1() , true , true , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase1() , true , true , true , true , nothing , false , nothing ), # infeasible # Current phase | art vars | new cut | exact stage | converged | next expected phase | err | err_type ( ClA.ColGenPhase2() , false , false , false , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase2() , false , false , false , true , ClA.ColGenPhase2() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase2() , false , false , true , false , ClA.ColGenPhase2() , false , nothing ), ( ClA.ColGenPhase2() , false , false , true , true , nothing , false , nothing ), # end of the column generation algorithm ( ClA.ColGenPhase2() , false , true , false , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , false , true , false , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , false , true , true , false , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , false , true , true , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase2() , true , false , false , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , false , false , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , false , true , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , false , true , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , false , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , false , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , true , false , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen ( ClA.ColGenPhase2() , true , true , true , true , nothing , true , ClA.UnexpectedEndOfColGenPhase ), # no artificial vars in phase 2 of colgen # Current phase | art vars | new cut | exact stage | converged | next expected phase | err | err_type ( ClA.ColGenPhase0() , false , false , false , false , ClA.ColGenPhase0() , false , nothing ), # you should have converged but you may have hit another limit ( ClA.ColGenPhase0() , false , false , false , true , ClA.ColGenPhase0() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase0() , false , false , true , false , ClA.ColGenPhase0() , false , nothing ), # you should have converged but you may have hit another limit ( ClA.ColGenPhase0() , false , false , true , true , nothing , false , nothing ), # end of the column generation algorithm ( ClA.ColGenPhase0() , false , true , false , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , false , true , false , true , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , false , true , true , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , false , true , true , true , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , false , false , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , false , false , true , ClA.ColGenPhase0() , false , nothing ), # converging with heuristic pricing means nothing ( ClA.ColGenPhase0() , true , false , true , false , ClA.ColGenPhase1() , false , nothing ), # you should have converged but you may have hit another limit. Let's try phase 1. ( ClA.ColGenPhase0() , true , false , true , true , ClA.ColGenPhase1() , false , nothing ), ( ClA.ColGenPhase0() , true , true , false , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , true , false , true , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , true , true , false , ClA.ColGenPhase0() , false , nothing ), ( ClA.ColGenPhase0() , true , true , true , true , ClA.ColGenPhase0() , false , nothing ), ] # Current phase | art vars | n dew cut | exact stage | converged | next expected phase | err | err_type for (cp, art, cut, exact, conv, exp, err, err_type) in table if !err @test ColGen.next_phase(it, cp, TestColGenOutput(art, cut, exact, conv)) isa typeof(exp) else @test_throws err_type ColGen.next_phase(it, cp, TestColGenOutput(art, cut, exact, conv)) end end end register!(unit_tests, "colgen_phase", next_phase_colgen_test) function get_reform_master_and_vars() form_string1 = """ master min 3x1 + 4x2 + 1000z s.t. x1 + x2 + z >= 1 dw_sp min x1 + x2 integer representatives x1, x2 continuous artificial z """ _, master, _, _, reform = reformfromstring(form_string1) vars_by_name = Dict{String, ClMP.Variable}(ClMP.getname(master, var) => var for (_, var) in ClMP.getvars(master)) return reform, master, vars_by_name end function setup_reformulation_colgen_test() reform, master, vars_by_name = get_reform_master_and_vars() @test ClMP.getcurcost(master, vars_by_name["x1"]) == 3 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 4 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test ClMP.iscuractive(master, vars_by_name["z"]) ColGen.setup_reformulation!(reform, ClA.ColGenPhase1()) @test ClMP.getcurcost(master, vars_by_name["x1"]) == 0 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 0 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test ClMP.iscuractive(master, vars_by_name["z"]) # To make sure that reduced costs will be well calculated: helper = ClA.ReducedCostsCalculationHelper(master) @test helper.master_c[ClMP.getid(vars_by_name["x1"])] == 0 @test helper.master_c[ClMP.getid(vars_by_name["x2"])] == 0 reform, master, vars_by_name = get_reform_master_and_vars() ColGen.setup_reformulation!(reform, ClA.ColGenPhase2()) @test ClMP.getcurcost(master, vars_by_name["x1"]) == 3 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 4 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test !ClMP.iscuractive(master, vars_by_name["z"]) reform, master, vars_by_name = get_reform_master_and_vars() ColGen.setup_reformulation!(reform, ClA.ColGenPhase0()) @test ClMP.getcurcost(master, vars_by_name["x1"]) == 3 @test ClMP.getcurcost(master, vars_by_name["x2"]) == 4 @test ClMP.getcurcost(master, vars_by_name["z"]) == 1000 @test ClMP.iscuractive(master, vars_by_name["z"]) end register!(unit_tests, "colgen_phase", setup_reformulation_colgen_test) function test_gap() mlp_db_sense_closed = [ # min sense (250, 10, true, false), (250, 255, true, true), (250.1, 250.1, true, true), (250.11111, 250.111110, true, true), # max sense (250, 10, false, true), (250, 255, false, false), (250.1, 250.1, false, true), (250.11111, 250.111112, false, true) ] for (mlp, db, sense, closed) in mlp_db_sense_closed coeff = sense ? 1 : -1 # minimization @test ClA._colgen_gap_closed(coeff * mlp, coeff * db, 0.001, 0.001) == closed end end register!(unit_tests, "colgen_phase", test_gap) function stop_colgen_phase_if_colgen_converged_eq() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( false, Inf, 99.9998, 99.9999, 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_colgen_converged_eq) function stop_colgen_phase_if_colgen_converged_min() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, # min sense Inf, 99.9998, # mlp 100.12, # greater than mlp means colgen has converged 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_colgen_converged_min) function stop_colgen_phase_if_colgen_converged_max() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( false, # max sense Inf, 99.9998, # mlp 99.9, # lower than mlp means colgen has converged 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_colgen_converged_max) function stop_colgen_phase_if_iterations_limit() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration(max_nb_iterations = 8)) colgen_iteration = 8 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 65.87759, 29.869, 6, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_iterations_limit) function stop_colgen_phase_if_time_limit() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 65.87759, 29.869, 6, false, false, false, false, false, true, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_time_limit) function stop_colgen_phase_if_subproblem_infeasible() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, false, true, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_subproblem_infeasible) function stop_colgen_phase_if_subproblem_unbounded() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, false, false, true, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_subproblem_unbounded) function stop_colgen_phase_if_master_unbounded() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, true, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_master_unbounded) function stop_colgen_phase_if_no_new_column() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 0, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_no_new_column) function stop_colgen_phase_if_new_cut_in_master() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, true, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase0(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_colgen_phase_if_new_cut_in_master) function continue_colgen_phase_otherwise() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 87859, 890, 1, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test !ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", continue_colgen_phase_otherwise) function stop_when_inf_db() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_iteration = 1 env = nothing colgen_iter_output = ClA.ColGenIterationOutput( true, Inf, 4578, Inf, 1, false, false, false, false, false, false, nothing, nothing, nothing ) ip_primal_sol = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) @test ColGen.stop_colgen_phase(ctx, ClA.ColGenPhase1(), env, colgen_iter_output, colgen_iter_output.db, ip_primal_sol, colgen_iteration) end register!(unit_tests, "colgen_phase", stop_when_inf_db) function infeasible_phase_output() reform, _, _ = get_reform_master_and_vars() ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration()) colgen_phase_output = ClA.ColGenPhaseOutput( nothing, nothing, nothing, nothing, 167673.9643, #mlp 162469.0291, #db false, true, true, #infeasible true, #exact_stage false, 6, true ) @test ColGen.stop_colgen(ctx, colgen_phase_output) colgen_output = ColGen.new_output(ClA.ColGenOutput, colgen_phase_output) @test colgen_output.infeasible == true @test isnothing(colgen_output.master_lp_primal_sol) @test isnothing(colgen_output.master_ip_primal_sol) @test isnothing(colgen_output.master_lp_dual_sol) @test_broken isnothing(colgen_output.mlp) @test_broken isnothing(colgen_output.db) master = ClA.getmaster(reform) optstate = ClA._colgen_optstate_output(colgen_output, master) @test optstate.termination_status == ClA.INFEASIBLE end register!(unit_tests, "colgen_phase", infeasible_phase_output)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

4177

function printer_colgen_iteration_master_ok_pricing_ok() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, 22.5, 22.5 - 23/4, 1, false, false, false, false, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> <mst= 1.23> <sp= 0.12> <cols= 1> <al= 0.12> <DB= 16.7500> <mlp= 22.5000> <PB=Inf>" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.12) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_master_ok_pricing_ok) function printer_colgen_iteration_master_infeasible() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, Inf, 0, false, true, false, false, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - infeasible master" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_master_infeasible) function printer_colgen_iteration_pricing_infeasible() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, Inf, 0, false, false, false, true, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - infeasible subproblem" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_pricing_infeasible) function printer_colgen_iteration_master_unbounded() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, -Inf, 0, false, false, true, false, false, false, nothing, nothing, nothing ) expected_str = "" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test_broken expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_master_unbounded) function printer_colgen_iteration_pricing_unbounded() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, nothing, 0, false, false, false, false, true, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - unbounded subproblem" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_iteration_pricing_unbounded) # function printer_colgen_finds_ip_primal_sol() # output = Coluna.Algorithm.ColGenIterationOutput( # true, # 22.5, # 22.5 - 23/4, # 1, # false, # false, # false, # false, # false, # false, # nothing, # [7.0, 7.0, 7.0] # ) # expected_str = "" # str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 0.12, 1.23, 2.34) # @show str # @test_broken expected_str == str # end # register!(unit_tests, "colgen_printer", printer_colgen_finds_ip_primal_sol) function printer_colgen_new_cuts_in_master() output = Coluna.Algorithm.ColGenIterationOutput( true, Inf, nothing, nothing, 0, true, false, false, false, false, false, nothing, nothing, nothing ) expected_str = " <st= 9> <it= 1> <et= 2.34> - new essential cut in master" str = Coluna.Algorithm._colgen_iter_str(1, output, 3, 9, 0.12, 1.23, 2.34, 0.0) @test expected_str == str end register!(unit_tests, "colgen_printer", printer_colgen_new_cuts_in_master)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

39429

################################################################################ # Test the implementation of the stabilization procedure. ################################################################################ # Make sure the value of α is updated correctly after each misprice. # The goal is to tend to 0.0 after a given number of misprices. function test_misprice_schedule() smooth_factor = 1 base_α = 0.8 prev_α = 0.8 for nb_misprices in 1:11 α = Coluna.Algorithm._misprice_schedule(smooth_factor, nb_misprices, base_α) @test (prev_α > α) || (iszero(α) && iszero(prev_α)) prev_α = α end return end register!(unit_tests, "colgen_stabilization", test_misprice_schedule) form_primal_solution() = """ master min 3x_11 + 2x_12 + 5x_13 + 2x_21 + x_22 + x_23 + 4y1 + 3y2 + z1 + z2 + 0s1 + 0s2 s.t. x_11 + x_12 + x_13 + x_21 + y1 + y2 + 2z1 + z2 >= 10 x_11 + 2x_12 + x_21 + 2x_22 + 3x_23 + y1 + 2y2 + z1 <= 100 x_11 + 3x_13 + x_22 + x_23 + y1 + == 100 y1 + + z1 + z2 <= 5 s1 >= 1 {MasterConvexityConstr} s1 <= 2 {MasterConvexityConstr} s2 >= 0 {MasterConvexityConstr} s2 <= 3 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + y1 + 0s1 s.t. x_11 + x_12 + x_13 + y1 >= 10 dw_sp min x_21 + x_22 + x_23 + y2 + 0s2 s.t. x_21 + x_22 + x_23 + y2 >= 10 integer representatives x_11, x_12, x_13, x_21, x_22, x_23, y1, y2 pure z1, z2 pricing_setup s1, s2 bounds x_11 >= 0 x_12 >= 0 x_13 >= 0 x_21 >= 0 x_22 >= 1 x_23 >= 0 1 <= y1 <= 2 3 <= y2 <= 6 z1 >= 0 z2 >= 3 """ function test_primal_solution() _, master, sps, _, _ = reformfromstring(form_primal_solution()) sp1, sp2 = sps[2], sps[1] vids = get_name_to_varids(master) cids = get_name_to_constrids(master) pool = Coluna.Algorithm.ColumnsSet() sol1 = Coluna.MathProg.PrimalSolution( sp1, [vids["x_11"], vids["x_12"], vids["x_13"], vids["y1"], vids["s1"]], [1.0, 2.0, 3.0, 7.0, 1.0], 11.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol1, false) # improving = true sol2 = Coluna.MathProg.PrimalSolution( sp2, [vids["x_21"], vids["x_22"], vids["x_23"], vids["y2"], vids["s2"]], [4.0, 4.0, 5.0, 10.0, 1.0], 13.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol2, true) primal_sol = Coluna.Algorithm._primal_solution(master, pool, true) sp1_lb = 1.0 sp2_ub = 3.0 @test primal_sol[vids["x_11"]] == 1.0 * sp1_lb @test primal_sol[vids["x_12"]] == 2.0 * sp1_lb @test primal_sol[vids["x_13"]] == 3.0 * sp1_lb @test primal_sol[vids["x_21"]] == 4.0 * sp2_ub @test primal_sol[vids["x_22"]] == 4.0 * sp2_ub @test primal_sol[vids["x_23"]] == 5.0 * sp2_ub @test primal_sol[vids["y1"]] == 7.0 * sp1_lb @test primal_sol[vids["y2"]] == 10.0 * sp2_ub @test primal_sol[vids["s1"]] == 1.0 * sp1_lb @test primal_sol[vids["s2"]] == 1.0 * sp2_ub @test primal_sol[vids["z1"]] == 0.0 # TODO: not sure about this test @test primal_sol[vids["z2"]] == 0.0 # TODO: not sure about this test return end register!(unit_tests, "colgen_stabilization", test_primal_solution) form_primal_solution2() = """ master min 3x_11 + 2x_12 + 2x_21 + x_22 + z1 + z2 + 0s1 + 0s2 s.t. x_11 + x_12 + x_21 + 2z1 + z2 >= 10 x_11 + 2x_12 + x_21 + 2x_22 + z1 <= 100 x_11 + x_22 + == 100 z1 + z2 >= 5 s1 >= 1 {MasterConvexityConstr} s1 <= 2 {MasterConvexityConstr} s2 >= 0 {MasterConvexityConstr} s2 <= 3 {MasterConvexityConstr} dw_sp min x_11 + x_12 + 0s1 s.t. x_11 + x_12 >= 10 dw_sp min x_21 + x_22 + 0s2 s.t. x_21 + x_22 >= 10 integer representatives x_11, x_12, x_21, x_22 pure z1, z2 pricing_setup s1, s2 bounds x_11 >= 0 x_12 >= 0 x_21 >= 0 x_22 >= 1 z1 >= 0 z2 >= 3 """ # We consider the master with the following coefficient matrix: # master_coeff_matrix = [ # 1 1 1 0 1 1; # -1 -2 -1 -2 -1 0; # 1 0 0 1 0 0; # is it correct to handle an "==" constraint like this in subgradient computation? # 0 0 0 0 1 1; # ] # the following rhs: rhs = [10, -100, 100, 5] # We consider the primal solution: primal = [1, 2, 12, 12, 0, 0] # The subgradient is therefore: rhs - master_coeff_matrix * primal = [-5, -59, 87, 5] # We use the following stability center: stab = [1, 2, 0, 1] function _test_angle_primal_sol(master, sp1, sp2) vids = get_name_to_varids(master) pool = Coluna.Algorithm.ColumnsSet() sol1 = Coluna.MathProg.PrimalSolution( sp1, [vids["x_11"], vids["x_12"]], [1.0, 2.0], 11.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol1, false) # improving = true sol2 = Coluna.MathProg.PrimalSolution( sp2, [vids["x_21"], vids["x_22"]], [4.0, 4.0], 13.0, Coluna.MathProg.FEASIBLE_SOL ) Coluna.Algorithm.add_primal_sol!(pool.subprob_primal_sols, sol2, true) is_minimization = true primal_sol = Coluna.Algorithm._primal_solution(master, pool, is_minimization) return primal_sol end function _test_angle_stab_center(master) cids = get_name_to_constrids(master) return Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [1.0, 2.0, 1.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) end function _data_for_dynamic_schedule_test() _, master, sps, _, _ = reformfromstring(form_primal_solution2()) sp1, sp2 = sps[2], sps[1] cids = get_name_to_constrids(master) cur_stab_center = _test_angle_stab_center(master) h = Coluna.Algorithm.SubgradientCalculationHelper(master) is_minimization = true primal_sol = _test_angle_primal_sol(master, sp1, sp2) return master, cur_stab_center, h, primal_sol, is_minimization end # Make sure the angle is well computed. # Here we test the can where the in and sep points are the same. # In that case, we should decrease the value of α. function test_angle_1() master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [1.0, 2.0, 1.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) increase = Coluna.Algorithm._increase(smooth_dual_sol, cur_stab_center, h, primal_sol, is_minimization) @test increase == false end register!(unit_tests, "colgen_stabilization", test_angle_1) # Let's consider the following sep point: sep = [5, 7, 0, 3] # The direction will be [4, 5, 0, 2] and should lead to a negative cosinus for the angle. # In that case, we need to increase the value of α. function test_angle_2() master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [5.0, 7.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) increase = Coluna.Algorithm._increase(smooth_dual_sol, cur_stab_center, h, primal_sol, is_minimization) @test increase == true end register!(unit_tests, "colgen_stabilization", test_angle_2) # Let's consider the following sep point: sep = [5, 1, 10, 3] # The direction will be [4, 1, 10, 2] and should lead to a positive cosinus for the angle. # In that case, we need to decrease the value of α. function test_angle_3() master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c3"], cids["c4"]], [5.0, 1.0, 10.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) increase = Coluna.Algorithm._increase(smooth_dual_sol, cur_stab_center, h, primal_sol, is_minimization) @test increase == false end register!(unit_tests, "colgen_stabilization", test_angle_3) function test_dynamic_alpha_schedule() for α in 0.1:0.1:0.9 @test Coluna.Algorithm.f_incr(α) > α @test Coluna.Algorithm.f_decr(α) < α end @test Coluna.Algorithm.f_incr(1.0) - 1.0 < 1e-3 @test Coluna.Algorithm.f_decr(0.0) < 1e-3 master, cur_stab_center, h, primal_sol, is_minimization = _data_for_dynamic_schedule_test() cids = get_name_to_constrids(master) smooth_dual_sol_for_decrease = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c3"], cids["c4"]], [5.0, 1.0, 10.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) smooth_dual_sol_for_increase = Coluna.MathProg.DualSolution( master, [cids["c1"], cids["c2"], cids["c4"]], [5.0, 7.0, 3.0], Coluna.MathProg.VarId[], Float64[], Coluna.MathProg.ActiveBound[], 0.0, Coluna.MathProg.FEASIBLE_SOL ) α = 0.8 @test α > Coluna.Algorithm._dynamic_alpha_schedule( α, smooth_dual_sol_for_decrease, cur_stab_center, h, primal_sol, is_minimization ) @test α < Coluna.Algorithm._dynamic_alpha_schedule( α, smooth_dual_sol_for_increase, cur_stab_center, h, primal_sol, is_minimization ) end register!(unit_tests, "colgen_stabilization", test_dynamic_alpha_schedule) ################################################################################ # Test to make sure the generic code works ################################################################################ # Mock implementation of the column generation to make sure the stabilization logic works # as expected. mutable struct ColGenStabFlowStab nb_misprice::Int64 nb_update_stab_after_master_done::Int64 nb_update_stab_after_pricing_done::Int64 nb_check_misprice::Int64 nb_misprices_done::Int64 nb_update_stab_after_iter_done::Int64 ColGenStabFlowStab(nb_misprice) = new(nb_misprice, 0, 0, 0, 0, 0) end struct ColGenStabFlowRes end struct ColGenStabFlowOutput end struct ColGenStabFlowDualSol end struct ColGenStabFlowPrimalSol end struct ColGenStabFlowPricingStrategy end mutable struct ColGenStabFlowCtx <: Coluna.ColGen.AbstractColGenContext nb_compute_dual_bound::Int64 ColGenStabFlowCtx() = new(0) end ColGen.get_master(::ColGenStabFlowCtx) = nothing ColGen.is_minimization(::ColGenStabFlowCtx) = true ColGen.optimize_master_lp_problem!(master, ctx::ColGenStabFlowCtx, env) = ColGenStabFlowRes() ColGen.colgen_iteration_output_type(::ColGenStabFlowCtx) = ColGenStabFlowOutput ColGen.is_infeasible(::ColGenStabFlowRes) = false ColGen.is_unbounded(::ColGenStabFlowRes) = false ColGen.get_dual_sol(::ColGenStabFlowRes) = ones(Float64, 3) ColGen.get_primal_sol(::ColGenStabFlowRes) = ColGenStabFlowPrimalSol() ColGen.get_obj_val(::ColGenStabFlowRes) = 0.0 ColGen.is_better_primal_sol(::ColGenStabFlowPrimalSol, p) = false ColGen.get_reform(::ColGenStabFlowCtx) = nothing ColGen.update_master_constrs_dual_vals!(::ColGenStabFlowCtx, dual_sol) = nothing ColGen.get_subprob_var_orig_costs(::ColGenStabFlowCtx) = ones(Float64, 3) ColGen.get_subprob_var_coef_matrix(::ColGenStabFlowCtx) = ones(Float64, 3, 3) ColGen.update_reduced_costs!(::ColGenStabFlowCtx, phase, red_costs) = nothing function ColGen.update_stabilization_after_master_optim!(stab::ColGenStabFlowStab, phase, mast_dual_sol) stab.nb_update_stab_after_master_done += 1 return true end ColGen.get_stab_dual_sol(stab::ColGenStabFlowStab, phase, mast_dual) = [0.5, 0.5, 0.5] ColGen.set_of_columns(::ColGenStabFlowCtx) = [] ColGen.get_pricing_subprobs(::ColGenStabFlowCtx) = [] ColGen.get_pricing_strategy(::ColGenStabFlowCtx, phase) = ColGenStabFlowPricingStrategy() ColGen.pricing_strategy_iterate(::ColGenStabFlowPricingStrategy) = nothing ColGen.compute_dual_bound(ctx::ColGenStabFlowCtx, phase, bounds, generated_columns, mast_dual_sol) = ctx.nb_compute_dual_bound += 1 function ColGen.update_stabilization_after_pricing_optim!(stab::ColGenStabFlowStab, ctx, generated_columns, master, pseudo_db, smooth_dual_sol) @test smooth_dual_sol == [0.5, 0.5, 0.5] # we need the out point in this method. stab.nb_update_stab_after_pricing_done += 1 return true end function ColGen.check_misprice(stab::ColGenStabFlowStab, cols, mast_dual_sol) @test mast_dual_sol == [1.0, 1.0, 1.0] # we need the out point in this method. stab.nb_check_misprice += 1 return stab.nb_check_misprice <= stab.nb_misprice end function ColGen.update_stabilization_after_misprice!(stab::ColGenStabFlowStab, mast_dual_sol) @test mast_dual_sol == [1.0, 1.0, 1.0] # we need the out point in this method. stab.nb_misprices_done += 1 end function ColGen.insert_columns!(context::ColGenStabFlowCtx, phase, generated_columns) return [] end function ColGen.update_stabilization_after_iter!(stab::ColGenStabFlowStab, mast_dual_sol) @test mast_dual_sol == [1.0, 1.0, 1.0] # we need the out point in this method. stab.nb_update_stab_after_iter_done += 1 return true end ColGen.new_iteration_output(::Type{<:ColGenStabFlowOutput}, args...) = nothing function test_stabilization_flow_no_misprice() ctx = ColGenStabFlowCtx() phase = nothing stage = nothing env = nothing ip_primal_sol = nothing stab = ColGenStabFlowStab(0) res = Coluna.ColGen.run_colgen_iteration!(ctx, phase, stage, env, ip_primal_sol, stab) @test stab.nb_check_misprice == 1 @test stab.nb_misprices_done == 0 @test stab.nb_update_stab_after_iter_done == 1 @test stab.nb_update_stab_after_master_done == 1 @test stab.nb_update_stab_after_pricing_done == 1 end register!(unit_tests, "colgen_stabilization", test_stabilization_flow_no_misprice) function test_stabilization_flow_with_misprice() ctx = ColGenStabFlowCtx() phase = nothing stage = nothing env = nothing ip_primal_sol = nothing stab = ColGenStabFlowStab(10) res = Coluna.ColGen.run_colgen_iteration!(ctx, phase, stage, env, ip_primal_sol, stab) @test stab.nb_check_misprice == 10 + 1 @test stab.nb_misprices_done == 10 @test stab.nb_update_stab_after_iter_done == 1 @test stab.nb_update_stab_after_master_done == 1 @test stab.nb_update_stab_after_pricing_done == 10 + 1 end register!(unit_tests, "colgen_stabilization", test_stabilization_flow_with_misprice) ################################################################################ # ################################################################################ function min_toy_gap_for_stab() form = """ master min 100.0 local_art_of_cov_5 + 100.0 local_art_of_cov_4 + 100.0 local_art_of_cov_6 + 100.0 local_art_of_cov_7 + 100.0 local_art_of_cov_2 + 100.0 local_art_of_cov_3 + 100.0 local_art_of_cov_1 + 100.0 local_art_of_sp_lb_5 + 100.0 local_art_of_sp_ub_5 + 100.0 local_art_of_sp_lb_4 + 100.0 local_art_of_sp_ub_4 + 1000.0 global_pos_art_var + 1000.0 global_neg_art_var + 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function max_toy_gap_for_stab() form = """ master max -100.0 local_art_of_cov_5 - 100.0 local_art_of_cov_4 - 100.0 local_art_of_cov_6 - 100.0 local_art_of_cov_7 - 100.0 local_art_of_cov_2 - 100.0 local_art_of_cov_3 - 100.0 local_art_of_cov_1 - 100.0 local_art_of_sp_lb_5 - 100.0 local_art_of_sp_ub_5 - 100.0 local_art_of_sp_lb_4 - 100.0 local_art_of_sp_ub_4 - 1000.0 global_pos_art_var - 1000.0 global_neg_art_var - 800.0 x_11 - 500.0 x_12 - 1100.0 x_13 - 2100.0 x_14 - 600.0 x_15 - 500.0 x_16 - 1900.0 x_17 - 100.0 x_21 - 1200.0 x_22 - 1100.0 x_23 - 1200.0 x_24 - 1400.0 x_25 - 800.0 x_26 - 500.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp max -800.0 x_11 - 500.0 x_12 - 1100.0 x_13 - 2100.0 x_14 - 600.0 x_15 - 500.0 x_16 - 1900.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp max -100.0 x_21 - 1200.0 x_22 - 1100.0 x_23 - 1200.0 x_24 - 1400.0 x_25 - 800.0 x_26 - 500.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_min_with_penalties_for_stab() form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp min 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 0.0 <= y_1 <= 1.0 0.0 <= y_2 <= 1.0 0.0 <= y_3 <= 1.0 0.0 <= y_4 <= 1.0 0.0 <= y_5 <= 1.0 0.0 <= y_6 <= 1.0 0.0 <= y_7 <= 1.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function toy_gap_max_with_penalties_for_stab() form = """ master max - 3.15 y_1 - 5.949999999999999 y_2 - 7.699999999999999 y_3 - 11.549999999999999 y_4 - 7.0 y_5 - 4.55 y_6 - 8.399999999999999 y_7 - 10000.0 local_art_of_cov_5 - 10000.0 local_art_of_cov_4 - 10000.0 local_art_of_cov_6 - 10000.0 local_art_of_cov_7 - 10000.0 local_art_of_cov_2 - 10000.0 local_art_of_limit_pen - 10000.0 local_art_of_cov_3 - 10000.0 local_art_of_cov_1 - 10000.0 local_art_of_sp_lb_5 - 10000.0 local_art_of_sp_ub_5 - 10000.0 local_art_of_sp_lb_4 - 10000.0 local_art_of_sp_ub_4 - 100000.0 global_pos_art_var - 100000.0 global_neg_art_var - 8.0 x_11 - 5.0 x_12 - 11.0 x_13 - 21.0 x_14 - 6.0 x_15 - 5.0 x_16 - 19.0 x_17 - 1.0 x_21 - 12.0 x_22 - 11.0 x_23 - 12.0 x_24 - 14.0 x_25 - 8.0 x_26 - 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 1.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 <= 1.0 {MasterConvexityConstr} dw_sp max - 8.0 x_11 - 5.0 x_12 - 11.0 x_13 - 21.0 x_14 - 6.0 x_15 - 5.0 x_16 - 19.0 x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp max - 1.0 x_21 - 12.0 x_22 - 11.0 x_23 - 12.0 x_24 - 14.0 x_25 - 8.0 x_26 - 5.0 x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 0.0 <= y_1 <= 1.0 0.0 <= y_2 <= 1.0 0.0 <= y_3 <= 1.0 0.0 <= y_4 <= 1.0 0.0 <= y_5 <= 1.0 0.0 <= y_6 <= 1.0 0.0 <= y_7 <= 1.0 """ env, master, sps, _, reform = reformfromstring(form) return env, master, sps, reform end function test_stabilization_min_automatic() env, master, sps, reform = min_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 1.0 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 7033.3333333 @test output.db ≈ 7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_min_automatic) function test_stabilization_max_automatic() env, master, sps, reform = max_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 1.0 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -7033.3333333 @test output.db ≈ -7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_max_automatic) function test_stabilization_min() env, master, sps, reform = min_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 7033.3333333 @test output.db ≈ 7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_min) function test_stabilization_max() env, master, sps, reform = max_toy_gap_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -7033.3333333 @test output.db ≈ -7033.3333333 end register!(unit_tests, "colgen_stabilization", test_stabilization_max) function test_stabilization_pure_master_vars_min() env, master, sps, reform = toy_gap_min_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 52.95 @test output.db ≈ 52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_min) function test_stabilization_pure_master_vars_min_automatic() env, master, sps, reform = toy_gap_min_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 1.0 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ 52.95 @test output.db ≈ 52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_min_automatic) function test_stabilization_pure_master_vars_max() env, master, sps, reform = toy_gap_max_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -52.95 @test output.db ≈ -52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_max) function test_stabilization_pure_master_vars_max_automatic() env, master, sps, reform = toy_gap_max_with_penalties_for_stab() # We need subsolvers to optimize the master and subproblems. # We relax the master formulation. ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start ClMP.relax_integrality!(master) for sp in sps ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer())) end ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration( smoothing_stabilization = 0.5 )) Coluna.set_optim_start_time!(env) input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform) output = ColGen.run!(ctx, env, input) @test output.mlp ≈ -52.95 @test output.db ≈ -52.95 end register!(unit_tests, "colgen_stabilization", test_stabilization_pure_master_vars_max_automatic)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

4506

ClMP.getuid(i::Int) = i # hack avoid creating formulations for the tests. function test_colgen_stage_iterator() nb_optimizers_per_pricing_prob = Dict(1 => [1, 2, 3], 2 => [1, 2], 3 => [1, 2, 3, 4]) it = Coluna.Algorithm.ColGenStageIterator(4, nb_optimizers_per_pricing_prob) stage = ColGen.initial_stage(it) @test ColGen.stage_id(stage) == 4 @test !ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 3 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 2 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 4 stage = ColGen.decrease_stage(it, stage) @test ColGen.stage_id(stage) == 3 @test !ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 2 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 3 stage = ColGen.decrease_stage(it, stage) @test ColGen.stage_id(stage) == 2 @test !ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 2 stage = ColGen.decrease_stage(it, stage) @test ColGen.stage_id(stage) == 1 @test ColGen.is_exact_stage(stage) @test ColGen.get_pricing_subprob_optimizer(stage, 1) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 2) == 1 @test ColGen.get_pricing_subprob_optimizer(stage, 3) == 1 stage = ColGen.decrease_stage(it, stage) @test isnothing(stage) end register!(unit_tests, "colgen_stage", test_colgen_stage_iterator) struct TestStageColGenPhaseOutput <: ColGen.AbstractColGenPhaseOutput new_cuts_in_master::Bool no_new_cols::Bool has_converged::Bool end ClA.colgen_master_has_new_cuts(output::TestStageColGenPhaseOutput) = output.new_cuts_in_master ClA.colgen_has_no_new_cols(output::TestStageColGenPhaseOutput) = output.no_new_cols ClA.colgen_has_converged(output::TestStageColGenPhaseOutput) = output.has_converged function test_colgen_next_stage() nb_optimizers_per_pricing_prob = Dict(1 => [1, 2, 3], 2 => [1, 2], 3 => [1, 2, 3, 4]) it = Coluna.Algorithm.ColGenStageIterator(4, nb_optimizers_per_pricing_prob) # ColGen.next_stage always returns the same stage, the next in the decreasing sequence, # or the initial stage. cur_stage = ColGen.initial_stage(it) # 4 heur_stage = ColGen.decrease_stage(it, cur_stage) # 3 cur_stage = ColGen.decrease_stage(it, heur_stage) # 2 exact_stage = ColGen.decrease_stage(it, cur_stage) # 1 @test ColGen.stage_id(heur_stage) == 3 @test !ColGen.is_exact_stage(heur_stage) @test ColGen.stage_id(exact_stage) == 1 @test ColGen.is_exact_stage(exact_stage) table = [ # stage_id | new cut | no more col| conv | next stage ( 3 , false , false , false , 3 ), # other limit ( 3 , false , false , true , 3 ), # impossible in theory ( 3 , false , true , false , 2 ), ( 3 , false , true , true , 3 ), ( 3 , true , false , false , 4 ), ( 3 , true , false , true , 4 ), # impossible in theory ( 3 , true , true , false , 4 ), ( 3 , true , true , true , 4 ), # stage_id | new cut | no more col| conv | next stage ( 1 , false , false , false , 1 ), # other limit ( 1 , false , false , true , 1 ), # impossible in theory ( 1 , false , true , false , nothing ), ( 1 , false , true , true , 1 ), ( 1 , true , false , false , 4 ), ( 1 , true , false , true , 4 ), # impossible in theory ( 1 , true , true , false , 4 ), ( 1 , true , true , true , 4 ), ] for (cur_st_id, cut, no_more_col, conv, next_st_id) in table stage = cur_st_id == 3 ? heur_stage : exact_stage next_stage = ColGen.next_stage(it, stage, TestStageColGenPhaseOutput(cut, no_more_col, conv)) if isnothing(next_st_id) @test isnothing(next_stage) else @test ColGen.stage_id(next_stage) == next_st_id end end end register!(unit_tests, "colgen_stage", test_colgen_next_stage)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

1886

function hash_table_1() x = 'A' y = 'B' z = 'C' col1 = 1 sol1 = [(x, 1.0), (z, 2.0)] col2 = 2 sol2 = [(x, 2.0), (y, -1.0)] col3 = 3 sol3 = [(y, -1.0), (z, 3.0)] col4 = 4 sol4 = [(x, 1.0)] col5 = 5 sol5 = [(z, -2.0), (y, 1.0)] ht = ClB.HashTable{Char,Int}() ClB.savesolid!(ht, col1, sol1) ClB.savesolid!(ht, col2, sol2) ClB.savesolid!(ht, col3, sol3) ClB.savesolid!(ht, col4, sol4) ClB.savesolid!(ht, col5, sol5) @test ClB.getsolids(ht, sol1) == [col1] @test ClB.getsolids(ht, sol2) == [col2] @test ClB.getsolids(ht, sol3) == [col3, col5] @test ClB.getsolids(ht, sol4) == [col4] @test ClB.getsolids(ht, sol5) == [col3, col5] end register!(unit_tests, "hashtable", hash_table_1) # Same test as "hash table 1" but we use VarIds from Coluna. function hash_table_2() x = ClMP.VarId(ClMP.OriginalVar, 1, 1) y = ClMP.VarId(ClMP.OriginalVar, 2, 1) z = ClMP.VarId(ClMP.OriginalVar, 3, 1) col1 = ClMP.VarId(ClMP.MasterCol, 4, 2) sol1 = [(x, 1.0), (z, 2.0)] col2 = ClMP.VarId(ClMP.MasterCol, 5, 2) sol2 = [(x, 2.0), (y, -1.0)] col3 = ClMP.VarId(ClMP.MasterCol, 6, 2) sol3 = [(y, -1.0), (z, 3.0)] col4 = ClMP.VarId(ClMP.MasterCol, 7, 2) sol4 = [(x, 1.0)] col5 = ClMP.VarId(ClMP.MasterCol, 8, 2) sol5 = [(z, -2.0), (y, 1.0)] ht = ClB.HashTable{ClMP.VarId, ClMP.VarId}() ClB.savesolid!(ht, col1, sol1) ClB.savesolid!(ht, col2, sol2) ClB.savesolid!(ht, col3, sol3) ClB.savesolid!(ht, col4, sol4) ClB.savesolid!(ht, col5, sol5) @test ClB.getsolids(ht, sol1) == [col1] @test ClB.getsolids(ht, sol2) == [col2] @test ClB.getsolids(ht, sol3) == [col3, col5] @test ClB.getsolids(ht, sol4) == [col4] @test ClB.getsolids(ht, sol5) == [col3, col5] end register!(unit_tests, "hashtable", hash_table_2)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

594

ClB.@nestedenum begin VarConstrDuty A <= VarConstrDuty A1 <= A A2 <= A A3 <= A B <= VarConstrDuty B1 <= B B2 <= B B3 <= B B3A <= B3 B3B <= B3 B3C <= B3 C <= VarConstrDuty D <= VarConstrDuty D1 <= D D1A <= D1 D1B <= D1 D2 <= D E <= VarConstrDuty end function nested_enum() @test <=(A1, A) @test A1 <= A @test !<=(A, B) @test !(A <= B) @test <=(B3A, B) @test B3A <= B end register!(unit_tests, "nestedenum", nested_enum)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

11413

struct FakeModel <: ClB.AbstractModel id::Int end FakeModel() = FakeModel(1) const Solution = ClB.Solution{FakeModel,Int,Float64} function constructor() # Make sure that Coluna initializes bounds to infinity. # Check that initial value of the bound is correct. primal = true min = true dual = false max = false pb = ClB.Bound(primal, min) @test pb == Inf @test ClB.getvalue(pb) == Inf pb = ClB.Bound(primal, max) @test pb == -Inf @test ClB.getvalue(pb) == -Inf db = ClB.Bound(dual, min) @test db == -Inf @test ClB.getvalue(db) == -Inf db = ClB.Bound(dual, max) @test db == Inf @test ClB.getvalue(db) == Inf pb = ClB.Bound(primal, min, 100) @test pb == 100 @test ClB.getvalue(pb) == 100 db = ClB.Bound(dual, min, -π) @test db == -π @test ClB.getvalue(db) == -π end register!(unit_tests, "bounds", constructor) function isbetter() primal = true min = true dual = false max = false # In minimization, pb with value 10 is better than pb with value 15 pb1 = ClB.Bound(primal, min, 10.0) pb2 = ClB.Bound(primal, min, 15.0) @test ClB.isbetter(pb1, pb2) == !ClB.isbetter(pb2, pb1) == true # In maximization, pb with value 15 is better than pb with value 10 pb1 = ClB.Bound(primal, max, 10.0) pb2 = ClB.Bound(primal, max, 15.0) @test ClB.isbetter(pb2, pb1) == !ClB.isbetter(pb1, pb2) == true # In minimization, db with value 15 is better than db with value 10 db1 = ClB.Bound(dual, min, 15.0) db2 = ClB.Bound(dual, min, 10.0) @test ClB.isbetter(db1, db2) == !ClB.isbetter(db2, db1) == true # In maximization, db with value 10 is better than db with value 15 db1 = ClB.Bound(dual, max, 15.0) db2 = ClB.Bound(dual, max, 10.0) @test ClB.isbetter(db2, db1) == !ClB.isbetter(db1, db2) == true # Cannot compare a primal & a dual bound db1 = ClB.Bound(dual, max, -10.0) @test_throws AssertionError ClB.isbetter(db1, pb1) # Cannot compare a bound from maximization & a bound from minimization db2 = ClB.Bound(dual, min, 10.0) @test_throws AssertionError ClB.isbetter(db1, db2) end register!(unit_tests, "bounds", isbetter) function diff() primal = true min = true dual = false max = false # Compute distance between primal bound and dual bound # In minimization, if pb = 10 & db = 5, distance is 5 pb = ClB.Bound(min, primal, 10) db = ClB.Bound(min, dual, 5) @test ClB.diff(pb, db) == ClB.diff(db, pb) == 5 # In maximisation if pb = 10 & db = 5, distance is -5 pb = ClB.Bound(max, primal, 10) db = ClB.Bound(max, dual, 5) @test ClB.diff(pb, db) == ClB.diff(db, pb) == -5 # Cannot compute the distance between two primal bounds pb1 = ClB.Bound(max, primal, 10) pb2 = ClB.Bound(max, primal, 15) @test_throws AssertionError ClB.diff(pb1, pb2) # Cannot compute the distance between two dual bounds db1 = ClB.Bound(max, dual, 5) db2 = ClB.Bound(max, dual, 50) @test_throws AssertionError ClB.diff(db1, db2) # Cannot compute the distance between two bounds from different sense pb = ClB.Bound(max, primal, 10) db = ClB.Bound(min, dual, 5) @test_throws AssertionError ClB.diff(pb, db) end register!(unit_tests, "bounds", diff) function gap() primal = true min = true dual = false max = false # In minimisation, gap = (pb - db)/db pb = ClB.Bound(min, primal, 10.0) db = ClB.Bound(min, dual, 5.0) @test ClB.gap(pb, db) == ClB.gap(db, pb) == (10.0-5.0)/5.0 # In maximisation, gap = (db - pb)/pb pb = ClB.Bound(max, primal, 5.0) db = ClB.Bound(max, dual, 10.0) @test ClB.gap(pb, db) == ClB.gap(db, pb) == (10.0-5.0)/5.0 pb = ClB.Bound(min, primal, 10.0) db = ClB.Bound(min, dual, -5.0) @test ClB.gap(pb, db) == ClB.gap(db, pb) == (10.0+5.0)/5.0 # Cannot compute the gap between 2 primal bounds pb1 = ClB.Bound(max, primal, 10) pb2 = ClB.Bound(max, primal, 15) @test_throws AssertionError ClB.gap(pb1, pb2) # Cannot compute the gap between 2 dual bounds db1 = ClB.Bound(max, dual, 5) db2 = ClB.Bound(max, dual, 50) @test_throws AssertionError ClB.gap(db1, db2) # Cannot compute the gap between 2 bounds with different sense pb = ClB.Bound(max, primal, 10) db = ClB.Bound(min, dual, 5) @test_throws AssertionError ClB.gap(pb, db) end register!(unit_tests, "bounds", gap) function printbounds() primal = true min = true dual = false max = false # In minimisation sense pb1 = ClB.Bound(min, primal, 100) db1 = ClB.Bound(min, dual, -100) io = IOBuffer() ClB.printbounds(db1, pb1, io) @test String(take!(io)) == "[ -100.0000 , 100.0000 ]" # In maximisation sense pb2 = ClB.Bound(max, primal, -100) db2 = ClB.Bound(max, dual, 100) io = IOBuffer() ClB.printbounds(db2, pb2, io) @test String(take!(io)) == "[ -100.0000 , 100.0000 ]" end register!(unit_tests, "bounds", printbounds) function show_test() primal = true max = false pb = ClB.Bound(max, primal, 4) io = IOBuffer() show(io, pb) @test String(take!(io)) == "4.0" end register!(unit_tests, "bounds", show_test) function promotions_and_conversion() primal = true max = false pb = ClB.Bound(max, primal, 4.0) @test eltype(promote(pb, 1)) == Real @test eltype(promote(pb, 2.0)) == typeof(2.0) @test eltype(promote(pb, π)) == Float64 @test eltype(promote(pb, 1, 2.0, π)) == Float64 @test promote_rule(eltype(pb), Integer) == Integer @test promote_rule(eltype(pb), Float64) == Float64 @test promote_rule(eltype(pb), Irrational) == Irrational @test typeof(pb + 1) == Float64 # check that promotion works @test convert(Float64, pb) == pb.value @test convert(Integer, pb) == pb.value @test convert(Irrational, pb) == pb.value end register!(unit_tests, "bounds", promotions_and_conversion) function convert_MOI_Coluna_termination_statuses() statuses_bijection = [ (MOI.OPTIMIZE_NOT_CALLED, ClB.OPTIMIZE_NOT_CALLED), (MOI.OPTIMAL, ClB.OPTIMAL), (MOI.INFEASIBLE, ClB.INFEASIBLE), (MOI.DUAL_INFEASIBLE, ClB.UNBOUNDED), (MOI.INFEASIBLE_OR_UNBOUNDED, ClB.UNBOUNDED), (MOI.TIME_LIMIT, ClB.TIME_LIMIT), (MOI.NODE_LIMIT, ClB.NODE_LIMIT), (MOI.OTHER_LIMIT, ClB.OTHER_LIMIT), ] statuses_surjection = [ (MOI.ALMOST_OPTIMAL, ClB.UNCOVERED_TERMINATION_STATUS), (MOI.SLOW_PROGRESS, ClB.UNCOVERED_TERMINATION_STATUS), (MOI.MEMORY_LIMIT, ClB.UNCOVERED_TERMINATION_STATUS), (MOI.ALMOST_OPTIMAL, ClB.UNCOVERED_TERMINATION_STATUS) ] for (moi_status, coluna_status) in statuses_bijection @test ClB.convert_status(moi_status) == coluna_status @test ClB.convert_status(coluna_status) == moi_status end for (moi_status, coluna_status) in statuses_surjection @test ClB.convert_status(moi_status) == coluna_status @test ClB.convert_status(coluna_status) == MOI.OTHER_LIMIT end end register!(unit_tests, "convert_MOI_Coluna", convert_MOI_Coluna_termination_statuses) function convert_MOI_Coluna_result_statuses() @test ClB.convert_status(MOI.NO_SOLUTION) == ClB.UNKNOWN_SOLUTION_STATUS @test ClB.convert_status(MOI.FEASIBLE_POINT) == ClB.FEASIBLE_SOL @test ClB.convert_status(MOI.INFEASIBLE_POINT) == ClB.INFEASIBLE_SOL @test ClB.convert_status(MOI.NEARLY_FEASIBLE_POINT) == ClB.UNCOVERED_SOLUTION_STATUS end register!(unit_tests, "convert_MOI_Coluna", convert_MOI_Coluna_result_statuses) function convert_MOI_Coluna_termination_statuses() @test ClB.convert_status(ClB.FEASIBLE_SOL) == MOI.FEASIBLE_POINT @test ClB.convert_status(ClB.INFEASIBLE_SOL) == MOI.INFEASIBLE_POINT @test ClB.convert_status(ClB.UNCOVERED_SOLUTION_STATUS) == MOI.OTHER_RESULT_STATUS end register!(unit_tests, "convert_MOI_Coluna", convert_MOI_Coluna_termination_statuses) function solution_factory(nbdecisions) decisions = Set{Int}() i = 0 while i < nbdecisions v = rand(rng, 1:100) if v ∉ decisions push!(decisions, v) i += 1 end end dict = Dict{Int, Float64}() soldecisions = Vector{Int}() solvals = Vector{Float64}() for d in decisions val = rand(rng, 0:0.0001:1000) dict[d] = val push!(soldecisions, d) push!(solvals, val) end return dict, soldecisions, solvals end function test_solution_iterations(solution::ClB.Solution, dict::Dict) prev_decision = nothing for (decision, value) in solution if prev_decision !== nothing @test prev_decision < decision end @test solution[decision] == dict[decision] solution[decision] += 1 @test solution[decision] == dict[decision] + 1 end return end function solution_constructor_iterate_print() model = FakeModel() dict_sol, soldecs, solvals = solution_factory(100) primal_sol = Solution(model, soldecs, solvals, 12.3, ClB.FEASIBLE_SOL) test_solution_iterations(primal_sol, dict_sol) @test ClB.getvalue(primal_sol) == 12.3 @test ClB.getstatus(primal_sol) == ClB.FEASIBLE_SOL dict_sol = Dict(1 => 2.0, 2 => 3.0, 3 => 4.0) primal_sol = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 0.0, ClB.FEASIBLE_SOL) @test length(primal_sol) == typemax(Coluna.MAX_NB_ELEMS) @test nnz(primal_sol) == 3 @test primal_sol[1] == 2.0 primal_sol[1] = 5.0 # change the value @test primal_sol[1] == 5.0 io = IOBuffer() show(io, primal_sol) @test String(take!(io)) == "Solution\n| 1 = 5.0\n| 2 = 3.0\n| 3 = 4.0\n└ value = 0.00 \n" end register!(unit_tests, "solution", solution_constructor_iterate_print) function solution_isequal() model = FakeModel() model2 = FakeModel(2) dict_sol = Dict(1 => 2.0, 2 => 5.0, 3 => 8.0, 9 => 15.0) dict_sol2 = Dict(1 => 2.0, 2 => 5.0, 3 => 7.0, 9 => 15.0) # key 3 has different value dict_sol3 = Dict(1 => 2.0, 2 => 5.0, 3 => 8.0, 9 => 15.0, 10 => 11.0) # new key 10 dict_sol4 = Dict(1 => 2.0, 2 => 5.0, 3 => 8.0) # missing key 9 sol1 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.FEASIBLE_SOL) sol2 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.FEASIBLE_SOL) sol3 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 15.0, ClB.FEASIBLE_SOL) sol4 = Solution(model, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.INFEASIBLE_SOL) sol5 = Solution(model2, collect(keys(dict_sol)), collect(values(dict_sol)), 12.0, ClB.FEASIBLE_SOL) sol6 = Solution(model, collect(keys(dict_sol2)), collect(values(dict_sol2)), 12.0, ClB.FEASIBLE_SOL) sol7 = Solution(model, collect(keys(dict_sol3)), collect(values(dict_sol3)), 12.0, ClB.FEASIBLE_SOL) sol8 = Solution(model, collect(keys(dict_sol4)), collect(values(dict_sol4)), 12.0, ClB.FEASIBLE_SOL) @test sol1 == sol2 @test sol1 != sol3 # different cost @test sol1 != sol4 # different solution status @test sol1 != sol5 # different model @test sol1 != sol6 # different solution @test sol1 != sol7 @test sol1 != sol8 end register!(unit_tests, "solution", solution_isequal)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

1744

# Storage units keeps records of a given data structure. const NB_VARS_CS1 = 6 struct ModelCs1 <: ClB.AbstractModel char_values::Vector{Char} tracked_char_pos::Vector{Int} end function Base.show(io::IO, model::ModelCs1) # TODO remove print(io, model.char_values) end struct CharStorageUnitCs1 <: ClB.AbstractRecordUnit end ClB.storage_unit(::Type{CharStorageUnitCs1}, _) = CharStorageUnitCs1() struct CharRecordCs1 <: ClB.AbstractRecord id::Int char_values::Dict{Int, Char} end ClB.get_id(r::CharRecordCs1) = r.id ClB.record_type(::Type{CharStorageUnitCs1}) = CharRecordCs1 ClB.storage_unit_type(::Type{CharRecordCs1}) = CharStorageUnitCs1 function ClB.record(::Type{CharRecordCs1}, id::Int, model::ModelCs1, ::CharStorageUnitCs1) entries_it = Iterators.filter( t -> t[1] ∈ model.tracked_char_pos, Iterators.map(t -> (t[1] => t[2]), Iterators.enumerate(model.char_values)) ) return CharRecordCs1(id, Dict{Int,Char}(collect(entries_it))) end function ClB.restore_from_record!( model::ModelCs1, ::CharStorageUnitCs1, record::CharRecordCs1 ) for (pos, char) in record.char_values model.char_values[pos] = char end return end function storage() model = ModelCs1(fill('A', NB_VARS_CS1), [3,4,5]) storage = ClB.Storage(model) r1 = ClB.create_record(storage, CharStorageUnitCs1) # create_record -> save_current_state a = ClB.restore_from_record!(storage, r1) model.char_values[3] = 'B' r2 = ClB.create_record(storage, CharStorageUnitCs1) ClB.restore_from_record!(storage, r1) @test model.char_values[3] == 'A' ClB.restore_from_record!(storage, r2) @test model.char_values[3] == 'B' end register!(unit_tests, "storage", storage)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

8343

function benders_decomposition() """ original min x1 + 4x2 + 2y1 + 3y2 s.t. x1 + x2 >= 0 - x1 + 3x2 - y1 + 2y2 >= 2 x1 + 3x2 + y1 + y2 >= 3 y1 + y2 >= 0 continuous original y1, y2 integer original x1, x2 bounds x1 >= 0 x2 >= 0 y1 >= 0 y2 >= 0 """ env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) origform = Coluna.MathProg.create_formulation!( env, Coluna.MathProg.Original() ) # Variables vars = Dict{String, Coluna.MathProg.VarId}() variables_infos = [ ("x1", 1.0, Coluna.MathProg.Integ), ("x2", 4.0, Coluna.MathProg.Integ), ("y1", 2.0, Coluna.MathProg.Continuous), ("y2", 3.0, Coluna.MathProg.Continuous) ] for (name, cost, kind) in variables_infos vars[name] = Coluna.MathProg.getid(Coluna.MathProg.setvar!( origform, name, Coluna.MathProg.OriginalVar; cost = cost, lb = 0.0, kind = kind )) end # Constraints constrs = Dict{String, Coluna.MathProg.ConstrId}() constraints_infos = [ ("c1", 2.0, Coluna.MathProg.Greater, Dict(vars["x1"] => -1.0, vars["x2"] => 4.0, vars["y1"] => 2.0, vars["y2"] => 3.0)), ("c2", 3.0, Coluna.MathProg.Greater, Dict(vars["x1"] => 1.0, vars["x2"] => 3.0, vars["y1"] => 1.0, vars["y2"] => 1.0)), ("c3", 0.0, Coluna.MathProg.Greater, Dict(vars["x1"] => 1.0, vars["x2"] => 1.0)), ("c4", 0.0, Coluna.MathProg.Greater, Dict(vars["y1"] => 1.0, vars["y2"] => 1.0)) ] for (name, rhs, sense, members) in constraints_infos constrs[name] = Coluna.MathProg.getid(Coluna.MathProg.setconstr!( origform, name, Coluna.MathProg.OriginalConstr; rhs = rhs, sense = sense, members = members )) end # Decomposition tree m = JuMP.Model() BlockDecomposition.@axis(axis, [1]) tree = BlockDecomposition.Tree(m, BlockDecomposition.Benders, axis) mast_ann = tree.root.master sp_ann = BlockDecomposition.Annotation(tree, BlockDecomposition.BendersSepSp, BlockDecomposition.Benders, []) BlockDecomposition.create_leaf!(BlockDecomposition.getroot(tree), axis[1], sp_ann) # Benders annotations ann = Coluna.Annotations() ann.tree = tree Coluna.store!(ann, mast_ann, Coluna.MathProg.getvar(origform, vars["x1"])) Coluna.store!(ann, mast_ann, Coluna.MathProg.getvar(origform, vars["x2"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getvar(origform, vars["y1"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getvar(origform, vars["y2"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getconstr(origform, constrs["c1"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getconstr(origform, constrs["c2"])) Coluna.store!(ann, mast_ann, Coluna.MathProg.getconstr(origform, constrs["c3"])) Coluna.store!(ann, sp_ann, Coluna.MathProg.getconstr(origform, constrs["c4"])) problem = Coluna.MathProg.Problem(env) Coluna.MathProg.set_original_formulation!(problem, origform) Coluna.reformulate!(problem, ann, env) reform = Coluna.MathProg.get_reformulation(problem) # Test first stage variables & constraints # Coluna.MathProg.MinSense + 1.0 x1 + 4.0 x2 + 1.0 η[4] # c3 : + 1.0 x1 + 1.0 x2 >= 0.0 (MasterPureConstrConstraintu3 | true) # 0.0 <= x1 <= Inf (Continuous | MasterPureVar | true) # 0.0 <= x2 <= Inf (Continuous | MasterPureVar | true) # 0.0 <= η[4] <= Inf (Continuous | MasterBendSecondStageCostVar | true) master = Coluna.MathProg.getmaster(reform) fs_vars = Dict(getname(master, varid) => var for (varid, var) in Coluna.MathProg.getvars(master)) fs_constrs = Dict(getname(master, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(master)) @test length(fs_vars) == 3 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_vars["x1"])) <= Coluna.MathProg.MasterPureVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_vars["x2"])) <= Coluna.MathProg.MasterPureVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_vars["η[4]"])) <= Coluna.MathProg.MasterBendSecondStageCostVar @test Coluna.MathProg.getcurlb(master, fs_vars["x1"]) == 0.0 @test Coluna.MathProg.getcurlb(master, fs_vars["x2"]) == 0.0 @test Coluna.MathProg.getcurlb(master, fs_vars["η[4]"]) == -Inf @test Coluna.MathProg.getcurub(master, fs_vars["x1"]) == Inf @test Coluna.MathProg.getcurub(master, fs_vars["x2"]) == Inf @test Coluna.MathProg.getcurub(master, fs_vars["η[4]"]) == Inf @test Coluna.MathProg.getcurcost(master, fs_vars["x1"]) == 1.0 @test Coluna.MathProg.getcurcost(master, fs_vars["x2"]) == 4.0 @test Coluna.MathProg.getcurcost(master, fs_vars["η[4]"]) == 1.0 @test length(fs_constrs) == 1 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(fs_constrs["c3"])) <= Coluna.MathProg.MasterPureConstr @test Coluna.MathProg.getcurrhs(master, fs_constrs["c3"]) == 0.0 # Test second stage variables & Constraints # Coluna.MathProg.MinSense + 2.0 y1 + 3.0 y2 + 1.0 μ⁺[x1] + 1.0 μ⁻[x1] + 4.0 μ⁺[x2] + 4.0 μ⁻[x2] # c1 : - 1.0 x1 + 4.0 x2 + 2.0 y1 + 3.0 y2 >= 2.0 (BendSpTechnologicalConstrConstraintu1 | true) # c2 : + 1.0 x1 + 3.0 x2 + 1.0 y1 + 1.0 y2 >= 3.0 (BendSpTechnologicalConstrConstraintu2 | true) # c4 : + 1.0 y1 + 1.0 y2 >= 0.0 (BendSpPureConstrConstraintu4 | true) # 0.0 <= y1 <= Inf (Continuous | BendSpSepVar | true) # 0.0 <= y2 <= Inf (Continuous | BendSpSepVar | true) # 0.0 <= x1 <= Inf (Continuous | BendFirstStageRepVar | true) # 0.0 <= x2 <= Inf (Continuous | BendFirstStageRepVar | true) subprob = first(values(Coluna.MathProg.get_benders_sep_sps(reform))) ss_vars = Dict(getname(subprob, varid) => var for (varid, var) in Coluna.MathProg.getvars(subprob)) ss_constrs = Dict(getname(subprob, constrid) => constr for (constrid, constr) in Coluna.MathProg.getconstrs(subprob)) @test length(ss_vars) == 7 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["y1"])) <= Coluna.MathProg.BendSpSepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["y2"])) <= Coluna.MathProg.BendSpSepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["x1"])) <= Coluna.MathProg.BendSpFirstStageRepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["x2"])) <= Coluna.MathProg.BendSpFirstStageRepVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["local_art_of_c1"])) <= Coluna.MathProg.BendSpSecondStageArtVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["local_art_of_c2"])) <= Coluna.MathProg.BendSpSecondStageArtVar @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_vars["local_art_of_c4"])) <= Coluna.MathProg.BendSpSecondStageArtVar @test Coluna.MathProg.getcurlb(subprob, ss_vars["y1"]) == 0.0 @test Coluna.MathProg.getcurlb(subprob, ss_vars["y2"]) == 0.0 @test Coluna.MathProg.getcurlb(subprob, ss_vars["x1"]) == 0.0 @test Coluna.MathProg.getcurlb(subprob, ss_vars["x2"]) == 0.0 @test Coluna.MathProg.getcurub(subprob, ss_vars["y1"]) == Inf @test Coluna.MathProg.getcurub(subprob, ss_vars["y2"]) == Inf @test Coluna.MathProg.getcurub(subprob, ss_vars["x1"]) == Inf @test Coluna.MathProg.getcurub(subprob, ss_vars["x2"]) == Inf @test Coluna.MathProg.getcurcost(subprob, ss_vars["y1"]) == 2.0 @test Coluna.MathProg.getcurcost(subprob, ss_vars["y2"]) == 3.0 @test Coluna.MathProg.getcurcost(subprob, ss_vars["x1"]) == 1.0 @test Coluna.MathProg.getcurcost(subprob, ss_vars["x2"]) == 4.0 @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_constrs["c1"])) <= Coluna.MathProg.BendSpTechnologicalConstr @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_constrs["c2"])) <= Coluna.MathProg.BendSpTechnologicalConstr @test Coluna.MathProg.getduty(Coluna.MathProg.getid(ss_constrs["c4"])) <= Coluna.MathProg.BendSpPureConstr @test Coluna.MathProg.getcurrhs(subprob, ss_constrs["c1"]) == 2.0 @test Coluna.MathProg.getcurrhs(subprob, ss_constrs["c2"]) == 3.0 @test Coluna.MathProg.getcurrhs(subprob, ss_constrs["c4"]) == 0.0 @test length(ss_constrs) == 3 return end register!(unit_tests, "benders_decomposition", benders_decomposition)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

2703

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

10787

function model_factory_for_buffer() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) push!(form.optimizers, ClMP.MoiOptimizer(MOI._instantiate_and_check(GLPK.Optimizer))) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost=2.0, lb=-1.0, ub=1.0, kind=ClMP.Integ, inc_val=4.0 ) constr = ClMP.setconstr!( form, "constr1", ClMP.MasterBranchOnOrigVarConstr, rhs=-13.0, members = Dict(ClMP.getid(var) => 2.0) ) CL.closefillmode!(ClMP.getcoefmatrix(form)) return form, var, constr end function _test_buffer(current::ClMP.FormulationBuffer, expected::ClMP.FormulationBuffer) @test isequal(current.changed_obj_sense, expected.changed_obj_sense) @test isequal(current.changed_cost, expected.changed_cost) @test isequal(current.changed_bound, expected.changed_bound) @test isequal(current.changed_var_kind, expected.changed_var_kind) @test isequal(current.changed_rhs, expected.changed_rhs) @test isequal(current.var_buffer, expected.var_buffer) @test isequal(current.constr_buffer, expected.constr_buffer) @test isequal(current.reset_coeffs, expected.reset_coeffs) return end _empty_buffer() = ClMP.FormulationBuffer{ClMP.VarId,ClMP.Variable,ClMP.ConstrId,ClMP.Constraint}() function model_factory_buffer_initial_state() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.changed_obj_sense = false # minimization by default expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.added = Set([varid]) expected_buffer.constr_buffer = ClMP.VarConstrBuffer{ClMP.ConstrId, ClMP.Constraint}() expected_buffer.constr_buffer.added = Set([constrid]) _test_buffer(form.buffer, expected_buffer) end register!(unit_tests, "buffer", model_factory_buffer_initial_state) function setcurcost!_and_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurcost!(form, var, 3.0) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # should not throw any error @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurcost!_and_deactivate_variable) function setcurkind!_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurkind!(form, var, ClMP.Integ) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # should not throw any error @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurkind!_deactivate_variable) function setcurlb!_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurlb!(form, var, 0.0) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurlb!_deactivate_variable) function setcurub!_deactivate_variable() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurub!(form, var, 0.0) ClMP.deactivate!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, var) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurub!_deactivate_variable) function setcurrhs!_deactivate_constraint() form, var, constr = model_factory_for_buffer() constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.constr_buffer = ClMP.VarConstrBuffer{ClMP.ConstrId, ClMP.Constraint}() expected_buffer.constr_buffer.removed = Set([constrid]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurrhs!(form, constr, 0.0) ClMP.deactivate!(form, constr) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, constr) == false _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", setcurrhs!_deactivate_constraint) function set_matrix_coeff() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.reset_coeffs = Dict((constrid => varid) => 5.0) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] = 5.0 _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] == 5.0 _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", set_matrix_coeff) function add_variable_and_set_matrix_coeff() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) var2 = ClMP.setvar!( form, "var2", ClMP.OriginalVar, cost=3.0, lb=-2.0, ub=2.0, kind=ClMP.Integ, inc_val=4.0 ) ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var2)] = 8.0 expected_buffer = _empty_buffer() # change of the matrix is not buffered because it is a new variable and Coluna has # to create the whole column in the subsolver. expected_buffer.var_buffer.added = Set([ClMP.getid(var2)]) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", add_variable_and_set_matrix_coeff) function set_matrix_coeff_and_deactivate_var_and_constr() form, var, constr = model_factory_for_buffer() varid = ClMP.getid(var) constrid = ClMP.getid(constr) expected_buffer = _empty_buffer() expected_buffer.var_buffer = ClMP.VarConstrBuffer{ClMP.VarId, ClMP.Variable}() expected_buffer.var_buffer.removed = Set([varid]) expected_buffer.constr_buffer = ClMP.VarConstrBuffer{ClMP.ConstrId, ClMP.Constraint}() expected_buffer.constr_buffer.removed = Set([constrid]) # matrix coefficient change is kept because it's too expensive to propagate # variable or column deletion in the matrix coeff buffer expected_buffer.reset_coeffs = Dict((constrid => varid) => 3.0) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] = 3.0 ClMP.deactivate!(form, var) ClMP.deactivate!(form, constr) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) @test ClMP.iscuractive(form, var) == false @test ClMP.iscuractive(form, constr) == false @test ClMP.getcoefmatrix(form)[ClMP.getid(constr), ClMP.getid(var)] == 3.0 _test_buffer(form.buffer, _empty_buffer()) # empty buffer after sync_solver! end register!(unit_tests, "buffer", set_matrix_coeff_and_deactivate_var_and_constr) function change_objective_sense() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.changed_obj_sense = true ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.set_objective_sense!(form, false) # maximization _test_buffer(form.buffer, expected_buffer) @test ClMP.getobjsense(form) == ClMP.MaxSense ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) _test_buffer(form.buffer, _empty_buffer()) end register!(unit_tests, "buffer", change_objective_sense) function set_peren_lb_and_ub() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.changed_bound = Set{ClMP.VarId}([ClMP.getid(var)]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setperenlb!(form, var, 0.0) ClMP.setperenub!(form, var, 1.0) _test_buffer(form.buffer, expected_buffer) end register!(unit_tests, "buffer", set_peren_lb_and_ub) function remove_variable() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.var_buffer.removed = Set([ClMP.getid(var)]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurcost!(form, var, 2.0) # make sure we delete buffered changes delete!(form, var) _test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # make sure exception thrown end register!(unit_tests, "buffer", remove_variable) function remove_constraint() form, var, constr = model_factory_for_buffer() expected_buffer = _empty_buffer() expected_buffer.constr_buffer.removed = Set([ClMP.getid(constr)]) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) ClMP.setcurrhs!(form, constr, 3.0) # make sure we delete buffered changes delete!(form, constr) #_test_buffer(form.buffer, expected_buffer) ClMP.sync_solver!(ClMP.getoptimizer(form, 1), form) # make sure exception thrown end register!(unit_tests, "buffer", remove_constraint)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

1765

function getters_and_setters() form = ClMP.create_formulation!(Coluna.Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) constr = ClMP.setconstr!(form, "fake_constr", ClMP.MasterBranchOnOrigVarConstr, rhs = -13.0, kind = ClMP.Facultative, sense = ClMP.Less, inc_val = -12.0, is_active = false, is_explicit = false ) cid = ClMP.getid(constr) # rhs @test ClMP.getcurrhs(form, cid) == -13.0 ClMP.setcurrhs!(form, cid, 10.0) @test ClMP.getperenrhs(form, cid) == -13.0 @test ClMP.getcurrhs(form, cid) == 10.0 ClMP.setperenrhs!(form, cid, 45.0) @test ClMP.getperenrhs(form, cid) == 45.0 @test ClMP.getcurrhs(form, cid) == 45.0 ClMP.setcurrhs!(form, cid, 12.0) # change cur before reset! # sense @test ClMP.getcursense(form, cid) == ClMP.Less ClMP.setcursense!(form, cid, ClMP.Greater) @test ClMP.getperensense(form, cid) == ClMP.Less @test ClMP.getcursense(form, cid) == ClMP.Greater ClMP.setperensense!(form, cid, ClMP.Equal) @test ClMP.getperensense(form, cid) == ClMP.Equal @test ClMP.getcursense(form, cid) == ClMP.Equal ClMP.reset!(form, cid) @test ClMP.getcurrhs(form, cid) == 45.0 @test ClMP.getperenrhs(form, cid) == 45.0 end register!(unit_tests, "constraints", getters_and_setters) function records() c_rec = ClMP.MoiConstrRecord( ; index = ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-15) ) @test ClMP.getmoiindex(c_rec) == ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-15) ClMP.setmoiindex!(c_rec, ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-20)) @test ClMP.getmoiindex(c_rec) == ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}(-20) end register!(unit_tests, "constraints", records)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

14221

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

2507

vid(uid) = ClMP.VarId(ClMP.OriginalVar, uid, 1) # Dantzig-wolfe solution pool struct DummyFormulation <: ClMP.AbstractFormulation end # optimizers struct DummyOptimizer <: ClMP.AbstractOptimizer end function dantzig_wolfe_pool() pool_sols = dynamicsparse(ClMP.VarId, ClMP.VarId, Float64; fill_mode = false) pool_ht = ClB.HashTable{ClMP.VarId,ClMP.VarId}() form = DummyFormulation() sol1_id = vid(1) sol1_ids = [vid(4), vid(5), vid(8)] sol1_vals = [1.0, 2.0, 5.0] sol1_repr = ClMP.PrimalSolution(form, sol1_ids, sol1_vals, 2.0, ClMP.FEASIBLE_SOL) sol2_id = vid(2) sol2_ids = [vid(4), vid(7), vid(9)] sol2_vals = [2.0, 2.0, 3.0] sol2_repr = ClMP.PrimalSolution(form, sol2_ids, sol2_vals, 4.0, ClMP.FEASIBLE_SOL) sol3_ids = [vid(4), vid(7), vid(9)] sol3_vals = [1.0, 2.0, 3.0] sol3_repr = ClMP.PrimalSolution(form, sol3_ids, sol3_vals, 5.0, ClMP.FEASIBLE_SOL) addrow!(pool_sols, sol1_id, sol1_ids, sol1_vals) ClB.savesolid!(pool_ht, sol1_id, sol1_repr) addrow!(pool_sols, sol2_id, sol2_ids, sol2_vals) ClB.savesolid!(pool_ht, sol2_id, sol2_repr) a = ClMP._get_same_sol_in_pool(pool_sols, pool_ht, sol1_repr) @test a == sol1_id a = ClMP._get_same_sol_in_pool(pool_sols, pool_ht, sol2_repr) @test a == sol2_id a = ClMP._get_same_sol_in_pool(pool_sols, pool_ht, sol3_repr) @test a === nothing end register!(unit_tests, "formulations", dantzig_wolfe_pool) function optimizers() env = CL.Env{ClMP.VarId}(CL.Params()) form = ClMP.create_formulation!(env, ClMP.DwMaster()) @test ClMP.getoptimizer(form, 1) isa ClMP.NoOptimizer ClMP.push_optimizer!(form, () -> DummyOptimizer()) @test ClMP.getoptimizer(form, 1) isa DummyOptimizer @test ClMP.getoptimizer(form, 2) isa ClMP.NoOptimizer end register!(unit_tests, "formulations", optimizers) # TODO : move this test outside unit tests. # function max_nb_form_unit() # coluna = optimizer_with_attributes( # Coluna.Optimizer, # "params" => Coluna.Params( # solver = Coluna.Algorithm.TreeSearchAlgorithm() # default BCP # ), # "default_optimizer" => GLPK.Optimizer # GLPK for the master & the subproblems # ) # @axis(M, 1:typemax(Int16)+1) # model = BlockModel(coluna) # @variable(model, x[m in M], Bin) # @dantzig_wolfe_decomposition(model, decomposition, M) # @test_throws ErrorException("Maximum number of formulations reached.") optimize!(model) # return # end

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

1588

DynamicSparseArrays.semaphore_key(::Type{Char}) = ' ' Base.zero(::Type{Char}) = ' ' DynamicSparseArrays.semaphore_key(::Type{Int}) = 0 function coefmatrix_factory() rows = ['a', 'a', 'b', 'b', 'd', 'f'] cols = [1, 2, 3, 1, 7, 1] vals = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] buffer = ClMP.FormulationBuffer{Int,Nothing,Char,Nothing}() matrix = ClMP.CoefficientMatrix{Char,Int,Float64}(buffer) for (i,j,v) in Iterators.zip(rows, cols, vals) matrix[i,j] = v end return rows, cols, vals, matrix end function close_fill_mode() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) for (i,j,v) in Iterators.zip(rows, cols, vals) @test matrix[i,j] == v end end register!(unit_tests, "formulations", close_fill_mode) function view_col() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) for (row, val) in @view matrix[:, 1] @test val == matrix[row,1] end end register!(unit_tests, "formulations", view_col) function view_row() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) for (col, val) in @view matrix['a', :] @test val == matrix['a', col] end end register!(unit_tests, "formulations", view_row) function transpose_test() rows, cols, vals, matrix = coefmatrix_factory() closefillmode!(matrix) transposed_matrix = transpose(matrix) for (i,j,v) in Iterators.zip(rows, cols, vals) @test transposed_matrix[j,i] == matrix[i,j] == v end end register!(unit_tests, "formulations", transpose_test)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

8643

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

7578

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

1716

function convert_MOI_Coluna() @test ClMP.MoiConstrIndex{MOI.VariableIndex,MOI.EqualTo}() == MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo}(-1) @test ClMP.MoiConstrIndex() == MOI.ConstraintIndex{MOI.ScalarAffineFunction{Float64},MOI.LessThan{Float64}}(-1) @test ClMP.MoiVarIndex() == MOI.VariableIndex(-1) @test ClMP.MoiVarKind() == MOI.ConstraintIndex{MOI.VariableIndex,MOI.Integer}(-1) @test ClMP.convert_moi_sense_to_coluna(MOI.LessThan{Float64}(0.0)) == ClMP.Less @test ClMP.convert_moi_sense_to_coluna(MOI.GreaterThan{Float64}(0.0)) == ClMP.Greater @test ClMP.convert_moi_sense_to_coluna(MOI.EqualTo{Float64}(0.0)) == ClMP.Equal @test ClMP.convert_moi_rhs_to_coluna(MOI.LessThan{Float64}(-12.3)) == -12.3 @test ClMP.convert_moi_rhs_to_coluna(MOI.GreaterThan{Float64}(-12.3)) == -12.3 @test ClMP.convert_moi_rhs_to_coluna(MOI.EqualTo{Float64}(-12.3)) == -12.3 @test ClMP.convert_moi_bounds_to_coluna(MOI.LessThan{Float64}(3.0)) == (-Inf, 3.0) @test ClMP.convert_moi_bounds_to_coluna(MOI.GreaterThan{Float64}(4.0)) == (4.0, Inf) @test ClMP.convert_moi_bounds_to_coluna(MOI.EqualTo{Float64}(5.0)) == (5.0, 5.0) @test ClMP.convert_moi_bounds_to_coluna(MOI.Interval{Float64}(1.0, 2.0)) == (1.0, 2.0) @test ClMP.convert_moi_kind_to_coluna(MOI.ZeroOne()) == ClMP.Binary @test ClMP.convert_moi_kind_to_coluna(MOI.Integer()) == ClMP.Integ @test ClMP.convert_coluna_sense_to_moi(ClMP.Less) == MOI.LessThan{Float64} @test ClMP.convert_coluna_sense_to_moi(ClMP.Greater) == MOI.GreaterThan{Float64} @test ClMP.convert_coluna_sense_to_moi(ClMP.Equal) == MOI.EqualTo{Float64} return end register!(unit_tests, "types", convert_MOI_Coluna)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

8768

function getters_and_setters() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -1.0, ub = 1.0, kind = ClMP.Integ, inc_val = 4.0 ) varid = ClMP.getid(var) @test ClMP.getperencost(form, varid) == 2.0 @test ClMP.getperenlb(form, varid) == -1.0 @test ClMP.getperenub(form, varid) == 1.0 @test ClMP.getperensense(form, varid) == ClMP.Free @test ClMP.getperenkind(form, varid) == ClMP.Integ @test ClMP.getperenincval(form, varid) == 4.0 @test ClMP.getcurcost(form, varid) == 2.0 @test ClMP.getcurlb(form, varid) == -1.0 @test ClMP.getcurub(form, varid) == 1.0 @test ClMP.getcursense(form, varid) == ClMP.Free @test ClMP.getcurkind(form, varid) == ClMP.Integ @test ClMP.getcurincval(form, varid) == 4.0 ClMP.setcurcost!(form, varid, 3.0) ClMP.setcurlb!(form, varid, -2.0) ClMP.setcurub!(form, varid, 2.0) ClMP.setcurkind!(form, varid, ClMP.Continuous) ClMP.setcurincval!(form, varid, 3.0) @test ClMP.getcurcost(form, varid) == 3.0 @test ClMP.getcurlb(form, varid) == -2.0 @test ClMP.getcurub(form, varid) == 2.0 @test ClMP.getcursense(form, varid) == ClMP.Free @test ClMP.getcurkind(form, varid) == ClMP.Continuous @test ClMP.getcurincval(form, varid) == 3.0 ClMP.reset!(form, varid) @test ClMP.getcurcost(form, varid) == 2.0 @test ClMP.getcurlb(form, varid) == -1.0 @test ClMP.getcurub(form, varid) == 1.0 @test ClMP.getcursense(form, varid) == ClMP.Free @test ClMP.getcurkind(form, varid) == ClMP.Integ @test ClMP.getcurincval(form, varid) == 4.0 end register!(unit_tests, "variables", getters_and_setters) function bounds_of_binary_variable_1() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, kind = ClMP.Binary ) @test ClMP.getperenlb(form, var) == 0.0 @test ClMP.getperenub(form, var) == 1.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 ClMP.setperenlb!(form, var, -1.0) ClMP.setperenub!(form, var, 2.0) @test ClMP.getperenlb(form, var) == 0.0 @test ClMP.getperenub(form, var) == 1.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 ClMP.setcurlb!(form, var, -1.1) ClMP.setcurub!(form, var, 2.1) @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 ClMP.setperenlb!(form, var, 0.1) ClMP.setperenub!(form, var, 0.9) @test ClMP.getperenlb(form, var) == 0.1 @test ClMP.getperenub(form, var) == 0.9 @test ClMP.getcurlb(form, var) == 0.1 @test ClMP.getcurub(form, var) == 0.9 ClMP.setcurlb!(form, var, 0.2) ClMP.setcurub!(form, var, 0.8) @test ClMP.getperenlb(form, var) == 0.1 @test ClMP.getperenub(form, var) == 0.9 @test ClMP.getcurlb(form, var) == 0.2 @test ClMP.getcurub(form, var) == 0.8 end register!(unit_tests, "variables", bounds_of_binary_variable_1) function bounds_of_binary_variable_2() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, kind = ClMP.Continuous, lb = -10.0, ub = 10.0 ) @test ClMP.getperenlb(form, var) == -10.0 @test ClMP.getperenub(form, var) == 10.0 @test ClMP.getcurlb(form, var) == -10.0 @test ClMP.getcurub(form, var) == 10.0 ClMP.setperenkind!(form, var, ClMP.Binary) @test ClMP.getperenlb(form, var) == 0.0 @test ClMP.getperenub(form, var) == 1.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 end register!(unit_tests, "variables", bounds_of_binary_variable_2) function bounds_of_binary_variable_3() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, kind = ClMP.Continuous, lb = -10.0, ub = 10.0 ) @test ClMP.getperenlb(form, var) == -10.0 @test ClMP.getperenub(form, var) == 10.0 @test ClMP.getcurlb(form, var) == -10.0 @test ClMP.getcurub(form, var) == 10.0 ClMP.setcurkind!(form, var, ClMP.Binary) @test ClMP.getperenlb(form, var) == -10.0 @test ClMP.getperenub(form, var) == 10.0 @test ClMP.getcurlb(form, var) == 0.0 @test ClMP.getcurub(form, var) == 1.0 end register!(unit_tests, "variables", bounds_of_binary_variable_3) function record() v_rec = ClMP.MoiVarRecord(; index = ClMP.MoiVarIndex(-15)) @test ClMP.getmoiindex(v_rec) == ClMP.MoiVarIndex(-15) @test ClMP.getlowerbound(v_rec) == ClMP.MoiVarLowerBound(-1) @test ClMP.getupperbound(v_rec) == ClMP.MoiVarUpperBound(-1) ClMP.setmoiindex!(v_rec, ClMP.MoiVarIndex(-20)) ClMP.setlowerbound!(v_rec, ClMP.MoiVarLowerBound(10)) ClMP.setupperbound!(v_rec, ClMP.MoiVarUpperBound(20)) @test ClMP.getmoiindex(v_rec) == ClMP.MoiVarIndex(-20) @test ClMP.getlowerbound(v_rec) == ClMP.MoiVarLowerBound(10) @test ClMP.getupperbound(v_rec) == ClMP.MoiVarUpperBound(20) end register!(unit_tests, "variables", record) function add_in_partial_sol_variable_1() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -3.0, ub = 3.0, kind = ClMP.Integ, inc_val = 4.0 ) DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) varid = ClMP.getid(var) @test ClMP.iscuractive(form, var) @test ClMP.isexplicit(form, var) @test !ClMP.in_partial_sol(form, var) @test ClMP.get_value_in_partial_sol(form, var) == 0 ClMP.add_to_partial_solution!(form, var, -1.0, true) @test ClMP.getcurub(form, var) == 0 @test ClMP.getcurlb(form, var) == -2 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.in_partial_sol(form, var) @test ClMP.get_value_in_partial_sol(form, var) == -1 @test ClMP.iscuractive(form, var) end register!(unit_tests, "variables", add_in_partial_sol_variable_1) function add_in_partial_sol_variable_2() form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -3.0, ub = 3.0, kind = ClMP.Integ, inc_val = 4.0 ) DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) varid = ClMP.getid(var) ClMP.deactivate!(form, varid) @test !ClMP.iscuractive(form, varid) ClMP.add_to_partial_solution!(form, var, -1.0, true) # try an unactive variable -> should work. @test ClMP.getcurub(form, var) == 0 @test ClMP.getcurlb(form, var) == -2 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == -1 end register!(unit_tests, "variables", add_in_partial_sol_variable_2) function add_in_partial_sol_variable_3() # sequential fix form = ClMP.create_formulation!(Env{ClMP.VarId}(Coluna.Params()), ClMP.Original()) var = ClMP.setvar!( form, "var1", ClMP.OriginalVar, cost = 2.0, lb = -3.0, ub = 3.0, kind = ClMP.Integ, inc_val = 4.0 ) DynamicSparseArrays.closefillmode!(ClMP.getcoefmatrix(form)) varid = ClMP.getid(var) ClMP.add_to_partial_solution!(form, var, 0.0, true) @test ClMP.getcurub(form, var) == 3 @test ClMP.getcurlb(form, var) == -3 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == 0 @test ClMP.iscuractive(form, var) ClMP.add_to_partial_solution!(form, var, 1.0, true) @test ClMP.getcurub(form, var) == 2 @test ClMP.getcurlb(form, var) == 0 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == 1 @test ClMP.iscuractive(form, var) ClMP.add_to_partial_solution!(form, var, -1.0, true) @test ClMP.getcurub(form, var) == 3 @test ClMP.getcurlb(form, var) == -3 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == 0 @test ClMP.iscuractive(form, var) ClMP.add_to_partial_solution!(form, var, -1.0, true) @test ClMP.getcurub(form, var) == 0 @test ClMP.getcurlb(form, var) == -2 @test ClMP.getperenub(form, var) == 3 @test ClMP.getperenlb(form, var) == -3 @test ClMP.get_value_in_partial_sol(form, var) == -1 @test ClMP.iscuractive(form, var) end register!(unit_tests, "variables", add_in_partial_sol_variable_3)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

1338

function id_equality() # vid1 & vid2 have same uid 1. vid3 has uid 2. vid1 = ClMP.VarId(ClMP.OriginalVar, 1, 1) vid2 = ClMP.VarId(ClMP.DwSpPricingVar, 1, 2) vid3 = ClMP.VarId(ClMP.OriginalVar, 2, 1) @test vid1 == vid2 @test vid1 != vid3 dict = Dict{ClMP.VarId, Float64}() dict[vid1] = 1.0 dict[vid3] = 2.0 @test dict[vid1] == 1.0 @test dict[vid2] == 1.0 @test dict[vid3] == 2.0 dict[vid2] = 3.0 @test dict[vid1] == 3.0 @test dict[vid2] == 3.0 @test dict[vid3] == 2.0 @test haskey(dict, vid1) @test haskey(dict, vid2) @test haskey(dict, vid3) end register!(unit_tests, "vcids", id_equality) function math_operations() vid1 = ClMP.VarId(ClMP.OriginalVar, 1, 1) @test vid1 + 1 == 2 @test vid1 - 1 == 0 @test vid1 < 2 @test vid1 <= 2 @test 2 >= vid1 @test 2 > vid1 @test vid1 == 1 @test isequal(vid1, 1) vid2 = ClMP.VarId(ClMP.OriginalVar, 2, 1) @test vid1 < vid2 @test vid1 <= vid2 @test vid2 >= vid1 @test vid2 > vid1 vid3 = ClMP.VarId(ClMP.OriginalVar, 2, 2) @test vid2 <= vid3 @test vid2 >= vid3 @test vid2 == vid3 @test isequal(vid2, vid3) @test vid1 + vid2 == 3 @test vid1 - vid2 == -1 @test vid1 * vid2 == 2 end register!(unit_tests, "vcids", math_operations)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

413

Coluna.MustImplement.@mustimplement "API" mi_f1() = nothing Coluna.MustImplement.@mustimplement "API" mi_f2(a, b) = nothing mi_f2(a::Int, b::Int) = a+b function must_implement() @test_throws Coluna.MustImplement.IncompleteInterfaceError mi_f1() @test_throws Coluna.MustImplement.IncompleteInterfaceError mi_f2("a", "b") @test mi_f2(1,2) == 3 end register!(unit_tests, "MustImplement", must_implement)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

3462

function parser_strip_indentation() s = "1.1*x_1" @test Parser._strip_identation(s) == "1.1*x_1" s = " 1.1*x_1" @test Parser._strip_identation(s) == "1.1*x_1" s = " 1.1*x_1" @test Parser._strip_identation(s) == "1.1*x_1" end register!(unit_tests, "parser", parser_strip_indentation) function parser_strip_line() s = "1.1*x_1,2.2*y_1,3.3*z_1" @test Parser._strip_line(s) == "1.1*x_1,2.2*y_1,3.3*z_1" s = " 1.1*x_1, 2.2*y_1, 3.3*z_1 " @test Parser._strip_line(s) == "1.1*x_1,2.2*y_1,3.3*z_1" s = " 1.1*x_1 , 2.2*y_1 , 3.3*z_1 " @test Parser._strip_line(s) == "1.1*x_1,2.2*y_1,3.3*z_1" end register!(unit_tests, "parser", parser_strip_line) function parser_get_vars_list() s = "x_1,y_1,z_1" @test Parser._get_vars_list(s) == ["x_1", "y_1", "z_1"] s = " x_1, y_1, z_1 " @test Parser._get_vars_list(s) == ["x_1", "y_1", "z_1"] s = " x_1 , y_1 , z_1 " @test Parser._get_vars_list(s) == ["x_1", "y_1", "z_1"] end register!(unit_tests, "parser", parser_get_vars_list) function parser_read_expression() s = "x_1 + y_1 - z_1" @test Parser._read_expression(s).vars == Dict("y_1" => 1.0, "x_1" => 1.0, "z_1" => -1.0) s = "- x_1 + 2.5*y_1 - 3z_1" @test Parser._read_expression(s).vars == Dict("y_1" => 2.5, "x_1" => -1.0, "z_1" => -3.0) s = "2*x_1 + 6*y_1 - 1.1z_1" @test Parser._read_expression(s).vars == Dict("y_1" => 6.0, "x_1" => 2.0, "z_1" => -1.1) end register!(unit_tests, "parser", parser_read_expression) function parser_read_constraint() s = "==" @test Parser._read_constraint(s) === nothing s = "x_1 + y_1" @test Parser._read_constraint(s) === nothing s = "x_1 + y_1 <=" @test Parser._read_constraint(s) === nothing s = "x_1 + y_1 >= z_1" @test Parser._read_constraint(s) === nothing s = "x_1 + 1.2y_1 == 5" c = Parser._read_constraint(s) @test c.lhs.vars == Dict("y_1" => 1.2, "x_1" => 1.0) @test c.sense == ClMP.Equal @test c.rhs == 5.0 s = "-4x_1 + 1.2*y_1 <= 5" c = Parser._read_constraint(s) @test c.lhs.vars == Dict("y_1" => 1.2, "x_1" => -4.0) @test c.sense == ClMP.Less @test c.rhs == 5.0 s = "-4.25*x_1 + 2y_1 >= 5" c = Parser._read_constraint(s) @test c.lhs.vars == Dict("y_1" => 2.0, "x_1" => -4.25) @test c.sense == ClMP.Greater @test c.rhs == 5.0 end register!(unit_tests, "parser", parser_read_constraint) function parser_read_bounds() less_r = Regex("(($(Parser.coeff_re))<=)?([\\w,]+)(<=($(Parser.coeff_re)))?") greater_r = Regex("(($(Parser.coeff_re))>=)?([\\w,]+)(>=($(Parser.coeff_re)))?") s = "" @test Parser._read_bounds(s, less_r) == ([], "", "") s = "y == 10" @test Parser._read_bounds(s, less_r) == (["y"], "", "") @test Parser._read_bounds(s, greater_r) == (["y"], "", "") s = "20 <= x" @test Parser._read_bounds(s, less_r) == (["x"], "20", "") s = "20 <= x <= 21.5" @test Parser._read_bounds(s, less_r) == (["x"], "20", "21.5") s = "20 <= x1, x2 <= 21.5" @test Parser._read_bounds(s, less_r) == (["x1", "x2"], "20", "21.5") s = "21.5 >= x" @test Parser._read_bounds(s, greater_r) == (["x"], "21.5", "") s = "21.5 >= x >= 20" @test Parser._read_bounds(s, greater_r) == (["x"], "21.5", "20") s = "21.5 >= x1, x2 >= 20" @test Parser._read_bounds(s, greater_r) == (["x1", "x2"], "21.5", "20") end register!(unit_tests, "parser", parser_read_bounds)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

41187

function presolve_toy_gap_with_penalties() form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 2.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 <= 1.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 binary representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 global_bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_21 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_22 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_23 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_24 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_25 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_26 <= 1.0 0.0 <= x_17 <= 1.0 0.0 <= x_27 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 0.1 <= y_1 <= 1.1 0.2 <= y_2 <= 1.2 0.3 <= y_3 <= 1.3 0.4 <= y_4 <= 1.4 0.5 <= y_5 <= 1.5 0.6 <= y_6 <= 1.6 0.7 <= y_7 <= 1.7 """ env, master, sps, _, reform = reformfromstring(form) return reform, master, sps end function build_dw_presolve_reformulation() reform, master, sps = presolve_toy_gap_with_penalties() presolve_reform = Coluna.Algorithm.create_presolve_reform(reform) presolve_original_master = presolve_reform.representative_master mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_original_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["y_2"], 0.2, 1.2), (mast_var_ids["y_3"], 0.3, 1.3), (mast_var_ids["y_4"], 0.4, 1.4), (mast_var_ids["y_5"], 0.5, 1.5), (mast_var_ids["y_6"], 0.6, 1.6), (mast_var_ids["y_7"], 0.7, 1.7), (mast_var_ids["x_11"], 0.0, 1.0), (mast_var_ids["x_12"], 0.0, 1.0), (mast_var_ids["x_13"], 0.0, 1.0), (mast_var_ids["x_14"], 0.0, 1.0), (mast_var_ids["x_15"], 0.0, 1.0), (mast_var_ids["x_16"], 0.0, 1.0), (mast_var_ids["x_17"], 0.0, 1.0), (mast_var_ids["x_21"], 0.0, 1.0), (mast_var_ids["x_22"], 0.0, 1.0), (mast_var_ids["x_23"], 0.0, 1.0), (mast_var_ids["x_24"], 0.0, 1.0), (mast_var_ids["x_25"], 0.0, 1.0), (mast_var_ids["x_26"], 0.0, 1.0), (mast_var_ids["x_27"], 0.0, 1.0) ] @test presolve_original_master.form.lower_multiplicity == 1 @test presolve_original_master.form.upper_multiplicity == 1 for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_original_master.form.lbs[varuid] == lb @test presolve_original_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_original_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_original_master.form.rhs[construid] == rhs @test presolve_original_master.form.sense[construid] == sense end presolve_restricted_master = presolve_reform.restricted_master @test presolve_restricted_master.form.lower_multiplicity == 1 @test presolve_restricted_master.form.upper_multiplicity == 1 mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_restricted_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["MC_38"], 0.0, Inf), (mast_var_ids["MC_39"], 0.0, Inf), (mast_var_ids["MC_40"], 0.0, Inf), (mast_var_ids["MC_41"], 0.0, Inf), (mast_var_ids["MC_42"], 0.0, Inf), (mast_var_ids["MC_43"], 0.0, Inf), (mast_var_ids["MC_44"], 0.0, Inf), (mast_var_ids["MC_45"], 0.0, Inf), (mast_var_ids["local_art_of_cov_5"], 0.0, Inf), (mast_var_ids["local_art_of_cov_4"], 0.0, Inf), (mast_var_ids["local_art_of_cov_6"], 0.0, Inf), (mast_var_ids["local_art_of_cov_7"], 0.0, Inf), (mast_var_ids["local_art_of_cov_2"], 0.0, Inf), (mast_var_ids["local_art_of_cov_3"], 0.0, Inf), (mast_var_ids["local_art_of_cov_1"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_4"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_4"], 0.0, Inf), (mast_var_ids["global_pos_art_var"], 0.0, Inf), (mast_var_ids["global_neg_art_var"], 0.0, Inf), (mast_var_ids["local_art_of_limit_pen"], 0.0, Inf), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_restricted_master.form.lbs[varuid] == lb @test presolve_restricted_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_restricted_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), (mast_constr_ids["c9"], 0.0, ClMP.Greater), (mast_constr_ids["c10"], 1.0, ClMP.Less), (mast_constr_ids["c11"], 0.0, ClMP.Greater), (mast_constr_ids["c12"], 1.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_restricted_master.form.rhs[construid] == rhs @test presolve_restricted_master.form.sense[construid] == sense end # Test coefficient matrix restricted_coef_matrix = [ (mast_constr_ids["c1"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c1"], mast_var_ids["local_art_of_cov_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c2"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c2"], mast_var_ids["local_art_of_cov_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c3"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c3"], mast_var_ids["local_art_of_cov_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c4"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c4"], mast_var_ids["local_art_of_cov_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c5"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c5"], mast_var_ids["local_art_of_cov_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c6"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c6"], mast_var_ids["local_art_of_cov_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c7"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c7"], mast_var_ids["local_art_of_cov_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c8"], mast_var_ids["local_art_of_limit_pen"], -1.0), (mast_constr_ids["c8"], mast_var_ids["global_neg_art_var"], -1.0), (mast_constr_ids["c9"], mast_var_ids["local_art_of_sp_lb_5"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c10"], mast_var_ids["local_art_of_sp_ub_5"], -1.0), (mast_constr_ids["c10"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c11"], mast_var_ids["local_art_of_sp_lb_4"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c12"], mast_var_ids["local_art_of_sp_ub_4"], -1.0), (mast_constr_ids["c12"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_45"], 1.0), ] for (c, v, val) in restricted_coef_matrix @test presolve_restricted_master.form.col_major_coef_matrix[c, v] == val end dw_sp = ClMP.get_dw_pricing_sps(reform)[5] presolve_dw_sp = presolve_reform.dw_sps[5] @test presolve_dw_sp.form.lower_multiplicity == 0 @test presolve_dw_sp.form.upper_multiplicity == 1 sp_var_ids = Dict{String, Int}(ClMP.getname(dw_sp, var) => k for (k,var) in enumerate(presolve_dw_sp.col_to_var)) var_ids_lbs_ubs = [ (sp_var_ids["x_11"], 0.0, 1.0), (sp_var_ids["x_12"], 0.0, 1.0), (sp_var_ids["x_13"], 0.0, 1.0), (sp_var_ids["x_14"], 0.0, 1.0), (sp_var_ids["x_15"], 0.0, 1.0), (sp_var_ids["x_16"], 0.0, 1.0), (sp_var_ids["x_17"], 0.0, 1.0), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_dw_sp.form.lbs[varuid] == lb @test presolve_dw_sp.form.ubs[varuid] == ub end sp_constr_ids = Dict{String, Int}(ClMP.getname(dw_sp, constr) => k for (k, constr) in enumerate(presolve_dw_sp.row_to_constr)) constr_ids = [ sp_constr_ids["sp_c2"], ] constr_rhs = [ 5.0, ] constr_sense = [ ClMP.Less, ] for (k, construid) in enumerate(constr_ids) @test presolve_dw_sp.form.rhs[construid] == constr_rhs[k] @test presolve_dw_sp.form.sense[construid] == constr_sense[k] end # Test coefficient matrix @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_11"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_12"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_13"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_14"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_15"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_16"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_17"]] == 1.0 end register!(unit_tests, "presolve_reformulation", build_dw_presolve_reformulation) function presolve_toy_gap_with_penalties2() form = """ master min 3.15 y_1 + 5.949999999999999 y_2 + 7.699999999999999 y_3 + 11.549999999999999 y_4 + 7.0 y_5 + 4.55 y_6 + 8.399999999999999 y_7 + 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_limit_pen + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_5 + 10000.0 local_art_of_sp_ub_5 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 1.0 x_21 + 12.0 x_22 + 11.0 x_23 + 12.0 x_24 + 14.0 x_25 + 8.0 x_26 + 5.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4 s.t. 1.0 x_11 + 1.0 x_21 + 1.0 y_1 + 1.0 local_art_of_cov_1 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 >= 1.0 1.0 x_12 + 1.0 x_22 + 1.0 y_2 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 >= 1.0 1.0 x_13 + 1.0 x_23 + 1.0 y_3 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_14 + 1.0 x_24 + 1.0 y_4 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 >= 1.0 1.0 x_15 + 1.0 x_25 + 1.0 y_5 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 x_26 + 1.0 y_6 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 x_27 + 1.0 y_7 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 >= 1.0 1.0 y_1 + 1.0 y_2 + 1.0 y_3 + 1.0 y_4 + 1.0 y_5 + 1.0 y_6 + 1.0 y_7 - 1.0 local_art_of_limit_pen - 1.0 global_neg_art_var <= 2.0 1.0 PricingSetupVar_sp_5 + 1.0 local_art_of_sp_lb_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 3.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_5 - 1.0 local_art_of_sp_ub_5 + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 <= 5.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 2.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_5 s.t. 2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0 dw_sp min x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_5, local_art_of_sp_ub_5, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var, local_art_of_limit_pen pure y_1, y_2, y_3, y_4, y_5, y_6, y_7 integer pricing_setup PricingSetupVar_sp_4, PricingSetupVar_sp_5 integer representatives x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27 bounds 0.1 <= x_11 <= 1.0 0.2 <= x_12 <= 1.0 0.3 <= x_13 <= 1.0 0.4 <= x_14 <= 1.0 0.5 <= x_15 <= 1.0 0.6 <= x_16 <= 1.0 0.7 <= x_17 <= 1.0 0.8 <= x_21 <= 1.0 0.9 <= x_22 <= 1.0 1.0 <= x_23 <= 2.0 1.1 <= x_24 <= 2.0 1.2 <= x_25 <= 2.0 1.3 <= x_26 <= 2.0 1.4 <= x_27 <= 2.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 1.0 <= PricingSetupVar_sp_5 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_5 >= 0.0 local_art_of_sp_ub_5 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 local_art_of_limit_pen >= 0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 0.1 <= y_1 <= 1.1 0.2 <= y_2 <= 1.2 0.3 <= y_3 <= 1.3 0.4 <= y_4 <= 1.4 0.5 <= y_5 <= 1.5 0.6 <= y_6 <= 1.6 0.7 <= y_7 <= 1.7 """ env, master, sps, _, reform = reformfromstring(form) return reform, master, sps end function build_dw_presolve_reformulation2() reform, master, sps = presolve_toy_gap_with_penalties2() presolve_reform = Coluna.Algorithm.create_presolve_reform(reform) presolve_original_master = presolve_reform.representative_master mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_original_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["y_2"], 0.2, 1.2), (mast_var_ids["y_3"], 0.3, 1.3), (mast_var_ids["y_4"], 0.4, 1.4), (mast_var_ids["y_5"], 0.5, 1.5), (mast_var_ids["y_6"], 0.6, 1.6), (mast_var_ids["y_7"], 0.7, 1.7), ] @test presolve_original_master.form.lower_multiplicity == 1 @test presolve_original_master.form.upper_multiplicity == 1 for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_original_master.form.lbs[varuid] == lb @test presolve_original_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_original_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_original_master.form.rhs[construid] == rhs @test presolve_original_master.form.sense[construid] == sense end presolve_restricted_master = presolve_reform.restricted_master @test presolve_restricted_master.form.lower_multiplicity == 1 @test presolve_restricted_master.form.upper_multiplicity == 1 mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_restricted_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["y_1"], 0.1, 1.1), (mast_var_ids["MC_38"], 0.0, Inf), (mast_var_ids["MC_39"], 0.0, Inf), (mast_var_ids["MC_40"], 0.0, Inf), (mast_var_ids["MC_41"], 0.0, Inf), (mast_var_ids["MC_42"], 0.0, Inf), (mast_var_ids["MC_43"], 0.0, Inf), (mast_var_ids["MC_44"], 0.0, Inf), (mast_var_ids["MC_45"], 0.0, Inf), (mast_var_ids["local_art_of_cov_5"], 0.0, Inf), (mast_var_ids["local_art_of_cov_4"], 0.0, Inf), (mast_var_ids["local_art_of_cov_6"], 0.0, Inf), (mast_var_ids["local_art_of_cov_7"], 0.0, Inf), (mast_var_ids["local_art_of_cov_2"], 0.0, Inf), (mast_var_ids["local_art_of_cov_3"], 0.0, Inf), (mast_var_ids["local_art_of_cov_1"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_5"], 0.0, Inf), (mast_var_ids["local_art_of_sp_lb_4"], 0.0, Inf), (mast_var_ids["local_art_of_sp_ub_4"], 0.0, Inf), (mast_var_ids["global_pos_art_var"], 0.0, Inf), (mast_var_ids["global_neg_art_var"], 0.0, Inf), (mast_var_ids["local_art_of_limit_pen"], 0.0, Inf), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_restricted_master.form.lbs[varuid] == lb @test presolve_restricted_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_restricted_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), (mast_constr_ids["c7"], 1.0, ClMP.Greater), (mast_constr_ids["c8"], 2.0, ClMP.Less), (mast_constr_ids["c9"], 3.0, ClMP.Greater), (mast_constr_ids["c10"], 5.0, ClMP.Less), (mast_constr_ids["c11"], 0.0, ClMP.Greater), (mast_constr_ids["c12"], 2.0, ClMP.Less), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_restricted_master.form.rhs[construid] == rhs @test presolve_restricted_master.form.sense[construid] == sense end # Test coefficient matrix restricted_coef_matrix = [ (mast_constr_ids["c1"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c1"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c1"], mast_var_ids["local_art_of_cov_1"], 1.0), (mast_constr_ids["c1"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c2"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c2"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c2"], mast_var_ids["local_art_of_cov_2"], 1.0), (mast_constr_ids["c2"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c3"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c3"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c3"], mast_var_ids["local_art_of_cov_3"], 1.0), (mast_constr_ids["c3"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c4"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c4"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c4"], mast_var_ids["local_art_of_cov_4"], 1.0), (mast_constr_ids["c4"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c5"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c5"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c5"], mast_var_ids["local_art_of_cov_5"], 1.0), (mast_constr_ids["c5"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c6"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c6"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c6"], mast_var_ids["local_art_of_cov_6"], 1.0), (mast_constr_ids["c6"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c7"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c7"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c7"], mast_var_ids["local_art_of_cov_7"], 1.0), (mast_constr_ids["c7"], mast_var_ids["global_pos_art_var"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_1"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_2"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_3"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_4"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_5"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_6"], 1.0), (mast_constr_ids["c8"], mast_var_ids["y_7"], 1.0), (mast_constr_ids["c8"], mast_var_ids["local_art_of_limit_pen"], -1.0), (mast_constr_ids["c8"], mast_var_ids["global_neg_art_var"], -1.0), (mast_constr_ids["c9"], mast_var_ids["local_art_of_sp_lb_5"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c9"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c10"], mast_var_ids["local_art_of_sp_ub_5"], -1.0), (mast_constr_ids["c10"], mast_var_ids["MC_38"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_40"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_42"], 1.0), (mast_constr_ids["c10"], mast_var_ids["MC_44"], 1.0), (mast_constr_ids["c11"], mast_var_ids["local_art_of_sp_lb_4"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c11"], mast_var_ids["MC_45"], 1.0), (mast_constr_ids["c12"], mast_var_ids["local_art_of_sp_ub_4"], -1.0), (mast_constr_ids["c12"], mast_var_ids["MC_39"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_41"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_43"], 1.0), (mast_constr_ids["c12"], mast_var_ids["MC_45"], 1.0), ] for (c, v, val) in restricted_coef_matrix @test presolve_restricted_master.form.col_major_coef_matrix[c, v] == val end dw_sp = ClMP.get_dw_pricing_sps(reform)[5] presolve_dw_sp = presolve_reform.dw_sps[5] @test presolve_dw_sp.form.lower_multiplicity == 3 @test presolve_dw_sp.form.upper_multiplicity == 5 sp_var_ids = Dict{String, Int}(ClMP.getname(dw_sp, var) => k for (k,var) in enumerate(presolve_dw_sp.col_to_var)) var_ids_lbs_ubs = [ (sp_var_ids["x_11"], 0.1, 1.0), (sp_var_ids["x_12"], 0.2, 1.0), (sp_var_ids["x_13"], 0.3, 1.0), (sp_var_ids["x_14"], 0.4, 1.0), (sp_var_ids["x_15"], 0.5, 1.0), (sp_var_ids["x_16"], 0.6, 1.0), (sp_var_ids["x_17"], 0.7, 1.0), ] for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_dw_sp.form.lbs[varuid] == lb @test presolve_dw_sp.form.ubs[varuid] == ub end sp_constr_ids = Dict{String, Int}(ClMP.getname(dw_sp, constr) => k for (k, constr) in enumerate(presolve_dw_sp.row_to_constr)) constr_ids = [ sp_constr_ids["sp_c2"], ] constr_rhs = [ 5.0, ] constr_sense = [ ClMP.Less, ] for (k, construid) in enumerate(constr_ids) @test presolve_dw_sp.form.rhs[construid] == constr_rhs[k] @test presolve_dw_sp.form.sense[construid] == constr_sense[k] end # Test coefficient matrix @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_11"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_12"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_13"]] == 3.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_14"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_15"]] == 2.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_16"]] == 1.0 @test presolve_dw_sp.form.col_major_coef_matrix[sp_constr_ids["sp_c2"], sp_var_ids["x_17"]] == 1.0 end register!(unit_tests, "presolve_reformulation", build_dw_presolve_reformulation2) function presolve_reformulation_with_var_not_in_coeff_matrix() form = """ master min 10000.0 local_art_of_cov_5 + 10000.0 local_art_of_cov_4 + 10000.0 local_art_of_cov_6 + 10000.0 local_art_of_cov_7 + 10000.0 local_art_of_cov_2 + 10000.0 local_art_of_cov_3 + 10000.0 local_art_of_cov_1 + 10000.0 local_art_of_sp_lb_4 + 10000.0 local_art_of_sp_ub_4 + 100000.0 global_pos_art_var + 100000.0 global_neg_art_var + 51.0 MC_38 + 38.0 MC_39 + 10.0 MC_40 + 28.0 MC_41 + 19.0 MC_42 + 26.0 MC_43 + 31.0 MC_44 + 42.0 MC_45 + 8.0 x_11 + 5.0 x_12 + 11.0 x_13 + 21.0 x_14 + 6.0 x_15 + 5.0 x_16 + 19.0 x_17 + 0.0 PricingSetupVar_sp_4 s.t. 0.0 x_11 + 1.0 x_12 + 1.0 local_art_of_cov_2 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_40 + 1.0 MC_44 + 1.0 MC_45 >= 1.0 1.0 x_13 + 1.0 local_art_of_cov_3 + 1.0 global_pos_art_var + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_14 + 1.0 local_art_of_cov_4 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_44 >= 1.0 1.0 x_15 + 1.0 local_art_of_cov_5 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_39 + 1.0 MC_42 + 1.0 MC_43 + 1.0 MC_45 >= 1.0 1.0 x_16 + 1.0 local_art_of_cov_6 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_40 + 1.0 MC_42 + 1.0 MC_44 >= 1.0 1.0 x_17 + 1.0 local_art_of_cov_7 + 1.0 global_pos_art_var + 1.0 MC_38 + 1.0 MC_41 + 1.0 MC_45 >= 1.0 1.0 PricingSetupVar_sp_4 + 1.0 local_art_of_sp_lb_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 >= 0.0 {MasterConvexityConstr} 1.0 PricingSetupVar_sp_4 - 1.0 local_art_of_sp_ub_4 + 1.0 MC_39 + 1.0 MC_41 + 1.0 MC_43 + 1.0 MC_45 <= 1.0 {MasterConvexityConstr} dw_sp min x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + 0.0 PricingSetupVar_sp_4 s.t. 5.0 x_11 + 1.0 x_12 + 1.0 x_13 + 3.0 x_14 + 1.0 x_15 + 5.0 x_16 + 4.0 x_17 <= 8.0 continuous columns MC_38, MC_39, MC_40, MC_41, MC_42, MC_43, MC_44, MC_45 artificial local_art_of_cov_5, local_art_of_cov_4, local_art_of_cov_6, local_art_of_cov_7, local_art_of_cov_2, local_art_of_cov_3, local_art_of_cov_1, local_art_of_sp_lb_4, local_art_of_sp_ub_4, global_pos_art_var, global_neg_art_var integer pricing_setup PricingSetupVar_sp_4 binary representatives x_11, x_12, x_13, x_14, x_15, x_16, x_17 global_bounds 0.0 <= x_11 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_17 <= 1.0 bounds 0.0 <= x_11 <= 1.0 0.0 <= x_12 <= 1.0 0.0 <= x_13 <= 1.0 0.0 <= x_14 <= 1.0 0.0 <= x_15 <= 1.0 0.0 <= x_16 <= 1.0 0.0 <= x_17 <= 1.0 1.0 <= PricingSetupVar_sp_4 <= 1.0 local_art_of_cov_5 >= 0.0 local_art_of_cov_4 >= 0.0 local_art_of_cov_6 >= 0.0 local_art_of_cov_7 >= 0.0 local_art_of_cov_2 >= 0.0 local_art_of_cov_3 >= 0.0 local_art_of_cov_1 >= 0.0 local_art_of_sp_lb_4 >= 0.0 local_art_of_sp_ub_4 >= 0.0 global_pos_art_var >= 0.0 global_neg_art_var >= 0.0 MC_38 >= 0 MC_39 >= 0 MC_40 >= 0 MC_41 >= 0 MC_42 >= 0 MC_43 >= 0 MC_44 >= 0 MC_45 >= 0 """ env, master, sps, _, reform = reformfromstring(form) return reform, master, sps end function build_dw_presolve_reformulation_with_var_not_in_coeff_matrix() # We create a reformulation several subproblem variables does not appear in the master problem. # x11 does not appear in the coefficient matrix reform, master, sps = presolve_reformulation_with_var_not_in_coeff_matrix() presolve_reform = Coluna.Algorithm.create_presolve_reform(reform) presolve_original_master = presolve_reform.representative_master mast_var_ids = Dict{String, Int}(ClMP.getname(master, var) => k for (k, var) in enumerate(presolve_original_master.col_to_var)) var_ids_lbs_ubs = [ (mast_var_ids["x_11"], 0, 1), (mast_var_ids["x_12"], 0, 1), (mast_var_ids["x_13"], 0, 1), (mast_var_ids["x_14"], 0, 1), (mast_var_ids["x_15"], 0, 1), (mast_var_ids["x_16"], 0, 1), (mast_var_ids["x_17"], 0, 1) ] @test presolve_original_master.form.lower_multiplicity == 1 @test presolve_original_master.form.upper_multiplicity == 1 for (varuid, lb, ub) in var_ids_lbs_ubs @test presolve_original_master.form.lbs[varuid] == lb @test presolve_original_master.form.ubs[varuid] == ub end mast_constr_ids = Dict{String, Int}(ClMP.getname(master, constr) => k for (k, constr) in enumerate(presolve_original_master.row_to_constr)) constr_ids_rhs_sense = [ (mast_constr_ids["c1"], 1.0, ClMP.Greater), (mast_constr_ids["c2"], 1.0, ClMP.Greater), (mast_constr_ids["c3"], 1.0, ClMP.Greater), (mast_constr_ids["c4"], 1.0, ClMP.Greater), (mast_constr_ids["c5"], 1.0, ClMP.Greater), (mast_constr_ids["c6"], 1.0, ClMP.Greater), ] for (construid, rhs, sense) in constr_ids_rhs_sense @test presolve_original_master.form.rhs[construid] == rhs @test presolve_original_master.form.sense[construid] == sense end restricted_coef_matrix = [ (mast_constr_ids["c1"], mast_var_ids["x_12"], 1.0), (mast_constr_ids["c2"], mast_var_ids["x_13"], 1.0), (mast_constr_ids["c3"], mast_var_ids["x_14"], 1.0), (mast_constr_ids["c4"], mast_var_ids["x_15"], 1.0), (mast_constr_ids["c5"], mast_var_ids["x_16"], 1.0), (mast_constr_ids["c6"], mast_var_ids["x_17"], 1.0), (mast_constr_ids["c1"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c2"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c3"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c4"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c5"], mast_var_ids["x_11"], 0.0), (mast_constr_ids["c6"], mast_var_ids["x_11"], 0.0), ] for (c, v, val) in restricted_coef_matrix @test presolve_original_master.form.col_major_coef_matrix[c, v] == val end end register!(unit_tests, "presolve_reformulation", build_dw_presolve_reformulation_with_var_not_in_coeff_matrix)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

3402

function test_non_proper_column1() # min x1 + x2 + 1MC1 + 2MC2 + 4MC3 # s.t. x1 + x2 + MC1 + MC2 + MC3 >= 2 # 0 <= x1 <= 1 # 0 <= x2 <= 2 # with # MC1 = [x1 = 1] # MC2 = [x2 = 2] # MC3 = [x1 = 2, x2 = 2] # non-proper! env = Coluna.Env{Coluna.MathProg.VarId}(Coluna.Params()) master_form, master_name_to_var, master_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwMaster(), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 1.0, nothing, nothing), ("x2", Coluna.MathProg.MasterRepPricingVar, 1.0, 0.0, 2.0, nothing, nothing), ("MC1", Coluna.MathProg.MasterCol, 1.0, 0.0, 1.0, nothing, 2), ("MC2", Coluna.MathProg.MasterCol, 2.0, 0.0, 1.0, nothing, 2), ("MC3", Coluna.MathProg.MasterCol, 4.0, 0.0, 1.0, nothing, 2), ], [ # name, duty, rhs, sense , id ("c1", Coluna.MathProg.MasterMixedConstr, 2.0, ClMP.Greater, nothing), ] ) coeffs = [ # var, constr, coeff ("c1", "x1", 1.0), ("c1", "x2", 1.0), ("c1", "MC1", 1.0), ("c1", "MC2", 1.0), ("c1", "MC3", 1.0), ] master_form_coef_matrix = Coluna.MathProg.getcoefmatrix(master_form) for (constr_name, var_name, coef) in coeffs constr = master_name_to_constr[constr_name] var = master_name_to_var[var_name] master_form_coef_matrix[ClMP.getid(constr), ClMP.getid(var)] = coef end DynamicSparseArrays.closefillmode!(master_form_coef_matrix) sp_form, sp_name_to_var, sp_name_to_constr = _mathprog_formulation!( env, Coluna.MathProg.DwSp( nothing, nothing, nothing, ClMP.Continuous, 1.0, Coluna.MathProg.Pool() ), [ # name, duty, cost, lb, ub, id ("x1", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 1.0, Coluna.Algorithm.getid(master_name_to_var["x1"]), nothing), ("x2", Coluna.MathProg.DwSpPricingVar, 1.0, 0.0, 2.0, Coluna.Algorithm.getid(master_name_to_var["x2"]), nothing) ], [ # name, duty, rhs, sense, id ("c2", Coluna.MathProg.DwSpPureConstr, 2.0, ClMP.Less, nothing) ], ) var_ids = [Coluna.MathProg.getid(sp_name_to_var["x1"]), Coluna.MathProg.getid(sp_name_to_var["x2"])] pool = Coluna.MathProg.get_primal_sol_pool(sp_form) for (name, vals) in Iterators.zip( ["MC1", "MC2", "MC3"], [ #x1, x2, Float64[1.0, 0.0], Float64[0.0, 1.0], Float64[2.0, 2.0] ] ) col_id = Coluna.MathProg.VarId(Coluna.MathProg.getid(master_name_to_var[name]); duty = Coluna.MathProg.DwSpPrimalSol) Coluna.MathProg.push_in_pool!( pool, Coluna.MathProg.PrimalSolution(sp_form, var_ids, vals, 1.0, Coluna.MathProg.FEASIBLE_SOL), col_id, 1.0 ) end @test Coluna.Algorithm._column_is_proper(Coluna.getid(master_name_to_var["MC1"]), sp_form) === true @test Coluna.Algorithm._column_is_proper(Coluna.getid(master_name_to_var["MC2"]), sp_form) === true @test Coluna.Algorithm._column_is_proper(Coluna.getid(master_name_to_var["MC3"]), sp_form) === false return end register!(unit_tests, "columns", test_non_proper_column1)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

31715

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

5604

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

22529

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

4026

# # Propagation between formulations of Dantzig-Wolf reformulation. # # In the following tests, we consider that the variables have the possible following duties # # Original formulation: # # variable: # # - OriginalVar # # constraint: # # - OriginalConstr # # Master: # # variable: # # - MasterRepPricingVar # # - MasterPureVar # # - MasterCol # # - MasterArtVar # # constraint: # # - MasterPureConstr # # - MasterMixedConstr # # - MasterConvexityConstr # # Pricing subproblems: # # variable: # # - DwSpPricingVar # # - DwSpSetupVar # # constraint: # # - DwSpPureConstr # ## Helpers function _presolve_propagation_vars(form, var_descriptions) vars = Tuple{String, Coluna.MathProg.Variable}[] for (name, duty, cost, lb, ub, id, origin_form_id) in var_descriptions if isnothing(id) var = if isnothing(origin_form_id) Coluna.MathProg.setvar!(form, name, duty, cost = cost, lb = lb, ub = ub) else Coluna.MathProg.setvar!( form, name, duty, cost = cost, lb = lb, ub = ub, id = Coluna.MathProg.VarId( duty, form.env.var_counter += 1, origin_form_id; ) ) end else id_of_clone = if isnothing(origin_form_id) ClMP.VarId(id; duty = duty) else ClMP.VarId(id; duty = duty, origin_form_uid = origin_form_id) end var = Coluna.MathProg.setvar!(form, name, duty; id = id_of_clone, cost = cost, lb = lb, ub = ub) end push!(vars, (name, var)) end return vars end function _presolve_propagation_constrs(form, constr_descriptions) constrs = Tuple{String, Coluna.MathProg.Constraint}[] for (name, duty, rhs, sense, id) in constr_descriptions if isnothing(id) constr = Coluna.MathProg.setconstr!(form, name, duty, rhs = rhs, sense = sense) else id_of_clone = ClMP.ConstrId(id; duty = duty) constr = Coluna.MathProg.setconstr!(form, name, duty; id = id_of_clone, rhs = rhs, sense = sense) end push!(constrs, (name, constr)) end return constrs end function _mathprog_formulation!(env, form_duty, var_descriptions, constr_descriptions) form = Coluna.MathProg.create_formulation!(env, form_duty) vars = _presolve_propagation_vars(form, var_descriptions) constrs = _presolve_propagation_constrs(form, constr_descriptions) name_to_vars = Dict(name => var for (name, var) in vars) name_to_constrs = Dict(name => constr for (name, constr) in constrs) return form, name_to_vars, name_to_constrs end function _presolve_formulation(var_names, constr_names, matrix, form, name_to_vars, name_to_constrs; lm=1, um=1) rhs = [Coluna.MathProg.getcurrhs(form, name_to_constrs[name]) for name in constr_names] sense = [Coluna.MathProg.getcursense(form, name_to_constrs[name]) for name in constr_names] lbs = [Coluna.MathProg.getcurlb(form, name_to_vars[name]) for name in var_names] ubs = [Coluna.MathProg.getcurub(form, name_to_vars[name]) for name in var_names] partial_solution = zeros(Float64, length(lbs)) form_repr = Coluna.Algorithm.PresolveFormRepr( matrix, rhs, sense, lbs, ubs, partial_solution, lm, um ) col_to_var = [name_to_vars[name] for name in var_names] row_to_constr = [name_to_constrs[name] for name in constr_names] var_to_col = Dict(ClMP.getid(name_to_vars[name]) => i for (i, name) in enumerate(var_names)) constr_to_row = Dict(ClMP.getid(name_to_constrs[name]) => i for (i, name) in enumerate(constr_names)) presolve_form = Coluna.Algorithm.PresolveFormulation( col_to_var, row_to_constr, var_to_col, constr_to_row, form_repr, Coluna.MathProg.ConstrId[], Dict{Coluna.MathProg.VarId, Float64}() ) return presolve_form end

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

code

36613

# To be able to properly test the tree search implemented in Coluna, we write a redefinition of the tree search interface with a customized search space TestBaBSearchSpace. # The goal is to simplify the tests writings by giving the possibility to "build" a specific branch and bound tree. # To do so, we use customized conquer and divide algorithms, together with node ids. To be more precise, each test corresponds to the construction of a branch and bound tree where the nodes are built thanks to a deterministic divide algorithm where we specify the nodes ids, and a deterministic conquer which matches each node id to the optimization state we want for the given node. # Flags are added to the conquer and divide algorithms in order to be able to check which nodes have been explored during the execution. The expected behaviour of each test is specified at its beginning. # WARNING: the re-implementation is generic, but the tests have been implemented keeping in mind a DFS exploration strategy. The expected results (and therefore the tests) are no longer valid if we change the explore strategy. mutable struct TestBaBSearchSpace <: Coluna.Algorithm.AbstractColunaSearchSpace inner::Coluna.Algorithm.BaBSearchSpace end # We create a TestBaBNode which wraps a "real" branch and bound Node and carries the node id. mutable struct TestBaBNode <: Coluna.TreeSearch.AbstractNode inner::Coluna.Algorithm.Node id::Int end # LightNode are used to contains the minimal information needed to create real nodes. In the tests only LightNode are defined, the re-implementation of the interface is then responsible to create real nodes when it is necessary. mutable struct LightNode id::Int depth::Int parent_ip_dual_bound::Coluna.Algorithm.Bound end # Deterministic conquer, a map with all the nodes ids matched to their optimization state struct DeterministicConquer <: Coluna.Algorithm.AbstractConquerAlgorithm conquer::Dict{Int, Coluna.OptimizationState} new_primal_sol::Dict{Int, Coluna.PrimalSolution} run_conquer_on_nodes::Vector{Int} ## FLAG, node ids of the nodes on which we have run conquer during the execution end ## the only information the deterministic conquer needs is the node id but we need to embedded it in a TestBaBConquerInput to match the implementation of children in treesearch struct TestBaBConquerInput node_id::Int inner::Coluna.Algorithm.ConquerInputFromBaB end Coluna.Algorithm.get_global_primal_handler(input::TestBaBConquerInput) = Coluna.Algorithm.get_global_primal_handler(input.inner) Coluna.Algorithm.get_conquer_input_ip_dual_bound(input::TestBaBConquerInput) = Coluna.Algorithm.get_conquer_input_ip_dual_bound(input.inner) Coluna.Algorithm.get_node_depth(input::TestBaBConquerInput) = Coluna.Algorithm.get_node_depth(input.inner) Coluna.Algorithm.get_units_to_restore(input::TestBaBConquerInput) = Coluna.Algorithm.get_units_to_restore(input.inner) struct TestBaBConquerOutput node_id::Int inner::Coluna.Algorithm.OptimizationState ## to update if we create a "real" BaBConquerOutput in the branch and bound end # Deterministic divide, match each node id to the nodes that should be generated as children from this node. struct DeterministicDivide <: Coluna.AlgoAPI.AbstractDivideAlgorithm divide::Dict{Int, Vector{LightNode}} ## the children are creating using the minimal information, they will turned into real nodes later in the algorithm run nodes_created_by_divide::Vector{Int} ## FLAG, node ids of the nodes that have been created by divide during the run of the branch-and-bound run_divide_on_nodes::Vector{Int} ## FLAG, node ids of the nodes on which we have run divide end ## the only information the deterministic divide needs is the node id but we need to embedded it in a TestBaBDivideInput to match the implementation of children in treesearch struct TestBaBDivideInput <: Coluna.Branching.AbstractDivideInput node_id::Int parent_conquer_output::Coluna.OptimizationState ## to update if we create a "real" BaBDivideInput in branch and bound end Coluna.Branching.get_conquer_opt_state(divide_input::TestBaBDivideInput) = return divide_input.parent_conquer_output # We redefine the interface for TestBaBSearchSpace: function Coluna.TreeSearch.new_space(::Type{TestBaBSearchSpace}, alg, model, input) inner_space = Coluna.TreeSearch.new_space( Coluna.Algorithm.BaBSearchSpace, alg, model, input) return TestBaBSearchSpace(inner_space) end # new_root returns a TestBaBNode with id 1 by default. The stack in treesearch (in explore.jl) will therefore contains nodes of type TestBaBNode. function Coluna.TreeSearch.new_root(space::TestBaBSearchSpace, input) inner = Coluna.TreeSearch.new_root(space.inner, input) return TestBaBNode(inner, 1) ## root id is set to 1 by default end function Coluna.TreeSearch.stop(space::TestBaBSearchSpace, untreated_nodes) inner_untreated_nodes = map(node->node.inner, untreated_nodes) return Coluna.TreeSearch.stop(space.inner, inner_untreated_nodes) end Coluna.TreeSearch.tree_search_output(space::TestBaBSearchSpace) = Coluna.TreeSearch.tree_search_output(space.inner) # methods called by native method children (in branch_and_bound.jl) Coluna.Algorithm.get_previous(space::TestBaBSearchSpace) = Coluna.Algorithm.get_previous(space.inner) Coluna.Algorithm.set_previous!(space::TestBaBSearchSpace, previous::TestBaBNode) = Coluna.Algorithm.set_previous!(space.inner, previous.inner) Coluna.Algorithm.get_reformulation(space::TestBaBSearchSpace) = Coluna.Algorithm.get_reformulation(space.inner) # extra methods needed by the new implementation of treesearch with the storage of the leaves state: Coluna.Algorithm.node_is_leaf(space::TestBaBSearchSpace, current::TestBaBNode, conquer_output::TestBaBConquerOutput) = Coluna.Algorithm.node_is_leaf(space.inner, current.inner, conquer_output.inner) Coluna.Algorithm.is_pruned(space::TestBaBSearchSpace, current::TestBaBNode) = Coluna.Algorithm.is_pruned(space.inner, current.inner) Coluna.Algorithm.node_is_pruned(space::TestBaBSearchSpace, current::TestBaBNode) = Coluna.Algorithm.node_is_pruned(space.inner, current.inner) # ************* redefinition of the methods to implement the deterministic conquer: ************* Coluna.Algorithm.get_conquer(space::TestBaBSearchSpace) = Coluna.Algorithm.get_conquer(space.inner) ## the only information the deterministic conquer needs is the node id, but we need to compute a BaBConquerInput as inner: function Coluna.Algorithm.get_input(alg::DeterministicConquer, space::TestBaBSearchSpace, node::TestBaBNode) inner = Coluna.Algorithm.get_input(alg, space.inner, node.inner) return TestBaBConquerInput(node.id, inner) end ## given the node id in the input, retrieves the corresponding optimization state in the dict, returns it together with the node id to pass them to the divide function Coluna.Algorithm.run!(alg::DeterministicConquer, env, reform, input) push!(alg.run_conquer_on_nodes, input.node_id) ## update flag new_primal_sol = get(alg.new_primal_sol, input.node_id, nothing) if !isnothing(new_primal_sol) # Tell the global primal bound handler that we found a new primal solution. Coluna.Algorithm.store_ip_primal_sol!(Coluna.Algorithm.get_global_primal_handler(input), new_primal_sol) end conquer_output = alg.conquer[input.node_id] return TestBaBConquerOutput(input.node_id, conquer_output) ## pass node id as a conquer output end function Coluna.Algorithm.after_conquer!(space::TestBaBSearchSpace, current, conquer_output) return Coluna.Algorithm.after_conquer!(space.inner, current.inner, conquer_output.inner) end # ************* redefinition of the methods to implement the deterministic divide: ************* Coluna.Algorithm.get_divide(space::TestBaBSearchSpace) = Coluna.Algorithm.get_divide(space.inner) ## call to the native routine to check if divide should be run Coluna.Algorithm.run_divide(space::TestBaBSearchSpace, input::TestBaBDivideInput) = Coluna.Algorithm.run_divide(space.inner, input) ## the only information the deterministic divide needs is the node id but we need to embedded it in a TestBaBDivideInput to match the implementation of children in treesearch function Coluna.Algorithm.get_input(::Coluna.AlgoAPI.AbstractDivideAlgorithm, ::TestBaBSearchSpace, ::TestBaBNode, conquer_output) return TestBaBDivideInput(conquer_output.node_id, conquer_output.inner) end ## takes the node id as the input, retrieve the list of (LightNode) children of the corresponding node and returns a DivideOutput made up of these (LightNode) children. function Coluna.Algorithm.run!(alg::DeterministicDivide, ::Coluna.Env, ::Coluna.MathProg.Reformulation, input::TestBaBDivideInput) push!(alg.run_divide_on_nodes, input.node_id) ##update flag children = alg.divide[input.node_id] for c in children ## update flag push!(alg.nodes_created_by_divide, c.id) end return Coluna.Algorithm.DivideOutput(children) end # constructs a real node from a LightNode, used in new_children to built real children from the minimal information contained in LightNode function Coluna.Algorithm.Node(node::LightNode) return Coluna.Algorithm.Node(node.depth, " ", nothing, node.parent_ip_dual_bound, Coluna.Algorithm.Records()) end ## The candidates are passed as LightNodes and the current node is passed as a TestBaBNode. The method retrieves the inner nodes to run the native method new_children of branch_and_bound.jl, gets the result as a vector of Nodes and then re-built a solution as a vector of TestBaBNodes using the nodes ids contained in LightNode structures. # branches input is a divide output with children of type LightNode. In the native method new_children in branch_and_bound.jl, those children are retrieved via get_children and then real nodes are created with a direct call to the constructor Node so it is sufficient to re-write a Node(child) method with child a LightNode (as above) to make the method works. function Coluna.Algorithm.new_children(space::TestBaBSearchSpace, branches::Coluna.Algorithm.DivideOutput{LightNode}, node::TestBaBNode) new_children_inner = Coluna.Algorithm.new_children(space.inner, branches, node.inner) ## vector of Nodes ids = map(node -> node.id, branches.children) ## vector of ids children = map( (n, id) -> TestBaBNode(n, id), new_children_inner, ids)## build the list of TestBaBNode return children end Coluna.Algorithm.node_change!(previous::Coluna.Algorithm.Node, current::TestBaBNode, space::TestBaBSearchSpace) = Coluna.Algorithm.node_change!(previous, current.inner, space.inner) # end of the interface's redefinition # Helper function _tree_search_reformulation() param = Coluna.Params() env = Coluna.Env{Coluna.MathProg.VarId}(param) origform = Coluna.MathProg.create_formulation!(env, Coluna.MathProg.Original()) master = Coluna.MathProg.create_formulation!(env, Coluna.MathProg.DwMaster()) dws = Dict{Coluna.MathProg.FormId, Coluna.MathProg.Formulation{Coluna.MathProg.DwSp}}() benders = Dict{Coluna.MathProg.FormId, Coluna.MathProg.Formulation{Coluna.MathProg.BendersSp}}() reform = Coluna.MathProg.Reformulation(env, origform, master, dws, benders) return reform, env end # Tests: #```mermaid #graph TD # 0( ) --> |lp_dual_bound = 20, \n ip_primal_sol = 40| 1 # 1((1)) --> |lp_dual_bound = 20, \n ip_primal_sol = 20| 2((2)) # 1 --> |should not be explored \n because gap is closed \n at node 2| 3((3)) #``` # exploration should stop at node 2 because gap is gap_closed # conquer and divide should not be run on node 3 # divide should not be run on node 2 # status: OPTIMAL with prima solution = 20.0 function test_stop_gap_closed() ## create an empty formulation reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) ## empty input optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) ## should not be explored primalsol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 40.0, Coluna.ColunaBase.FEASIBLE_SOL) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 20.0, Coluna.ColunaBase.FEASIBLE_SOL) ## set up the conquer and the divide (and thus the shape of the branch-and-bound tree, see the mermaid diagram below) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => primalsol1, 2 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 20.0)), LightNode(2, 1, Coluna.DualBound(master, 20.0))], ##remark: should pass first the right child, and second the left child (a bit contre intuitive ?) ##TODO see and fix code 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test !(2 in dividealg.run_divide_on_nodes) ## we converge at node 2, we should not enter divide @test !(3 in conqueralg.run_conquer_on_nodes) @test !(3 in dividealg.run_divide_on_nodes) end register!(unit_tests, "treesearch", test_stop_gap_closed) #```mermaid #graph TD # 0( ) --> |lp_dual_bound = 55, \n ip_primal_sol = _| 1((1)) # 1 --> |INFEASIBLE| 2((2)) # 1 --> |INFEASIBLE| 3((3)) #``` # exploration should stop at node 3 # divide should not be called on node 2 and 3 # should return status = INFEASIBLE function test_infeasible_pb() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) optstate3 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict(), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 55.0)), LightNode(2, 1, Coluna.DualBound(master, 55.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.INFEASIBLE @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test !(2 in dividealg.run_divide_on_nodes) @test !(2 in dividealg.run_divide_on_nodes) end register!(unit_tests, "treesearch", test_infeasible_pb) #```mermaid #graph TD # 0( ) --> |lp_dual_bound = 55, \n ip_primal_sol = 60| 1((1)) # 1 --> |INFEASIBLE| 2((2)) # 1 --> |lp_dual_bound = 60, \n ip_primal_sol = 60| 3((3)) #``` # the exploration should stop on node 3 because the gap is closed # divide should not be run on node 2 because the subproblem is infeasible function test_infeasible_sp() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 60.0)) optimalsol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => optimalsol, 3 => optimalsol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 55.0)), LightNode(2, 1, Coluna.DualBound(master, 55.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == optimalsol @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test !(2 in dividealg.run_divide_on_nodes) @test !(3 in dividealg.run_divide_on_nodes) @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_infeasible_sp) #```mermaid #graph TD # 0( ) --> |lp_dual_bound = 53, \n ip_primal_sol = 60| 1((1)) # 1 --> |lp_dual_bound = 56, \n ip_primal_sol = 60| 2((2)) # 1 --> |lp_dual_bound = 57, \n ip_primal_sol = 60| 3((3)) #``` # not enough information to branch on both leaves 2 and 3 and the gap is not closed at any node # should return OTHER_LIMIT with dual_bound = 56 (worst among the leaves) and primal bound = 60, all nodes should be explored function test_all_explored_with_pb() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 53.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 53.0)), LightNode(2, 1, Coluna.DualBound(master, 53.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test Coluna.get_ip_dual_bound(algstate).value ≈ 56.0 @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 1 in dividealg.run_divide_on_nodes @test 2 in dividealg.run_divide_on_nodes @test 3 in dividealg.run_divide_on_nodes @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_all_explored_with_pb) #```mermaid #graph TD # 0( ) --> |lp_dual_bound = 53, \n ip_primal_sol = 60| 1((1)) # 1 --> |lp_dual_bound = 56, \n ip_primal_sol = 60| 2((2)) # 1 --> |lp_dual_bound = 57, \n ip_primal_sol = 58| 3((3)) #``` # not enough information to branch on leaves, should return OTHER_LIMIT with dual_bound = 56 and primal_bound = 58 function test_all_explored_with_pb_2() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 53.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) primal_sol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 58.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict( 1 => primal_sol1, 3 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 53.0)), LightNode(2, 1, Coluna.DualBound(master, 53.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test Coluna.get_ip_dual_bound(algstate).value ≈ 56.0 @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 1 in dividealg.run_divide_on_nodes @test 2 in dividealg.run_divide_on_nodes @test 3 in dividealg.run_divide_on_nodes @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_all_explored_with_pb_2) #```mermaid #graph TD # 0( ) --> |lp_dual_bound = 55, \n ip_primal_bound = _| 1((1)) # 1 --> |lp_dual_bound = 56, \n ip_primal_bound = _| 2((2)) # 1 --> |lp_dual_bound = 57, \n ip_primal_bound = _| 3((3)) # #``` # not enough information to branch on both leaves # no primal bound # should return OTHER_LIMIT with dual_bound = 56 function test_all_explored_without_pb() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 ), Dict(), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(3, 1, Coluna.DualBound(master, 53.0)), LightNode(2, 1, Coluna.DualBound(master, 53.0))], 2 => [], 3 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @test Coluna.get_ip_dual_bound(algstate).value ≈ 56.0 @test isnothing(Coluna.get_best_ip_primal_sol(algstate)) @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 1 in dividealg.run_divide_on_nodes @test 2 in dividealg.run_divide_on_nodes @test 3 in dividealg.run_divide_on_nodes @test 1 in conqueralg.run_conquer_on_nodes @test 2 in conqueralg.run_conquer_on_nodes @test 3 in conqueralg.run_conquer_on_nodes end register!(unit_tests, "treesearch", test_all_explored_without_pb) # ```mermaid #graph TD # 0( ) --> |lp_dual_bound = 20, \n ip_primal_sol = 40| 1 # 1((1)) --> |lp_dual_bound = 20, \n ip_primal_sol = 40| 2((2)) # 1 --> |lp_dual_bound = 30, \n ip_primal_sol = 30| 5((5)) # 2 --> |lp_dual_bound = 45, \n ip_primal_sol = 40| 3((3)) # 2 --> |lp_dual_bound = 45, \n ip_primal_sol = 40| 4((4)) # 5 --> |STOP| stop( ) # ``` # At nodes 3 and 4, the local lp_dual_bound > the primal bound but the global dual bound < primal bound so the algorithm should continue and stop at node 5 when gap is closed # status: OPTIMAL with primal solution = 30.0 function test_local_db() reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, ip_primal_bound = Coluna.MathProg.PrimalBound(master, 40.0), lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 20.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 45.0)) optstate4 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 45.0)) optstate5 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 30.0)) primalsol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 40.0, Coluna.ColunaBase.FEASIBLE_SOL) optimalsol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 30.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3, 4 => optstate4, 5 => optstate5 ), Dict( 1 => primalsol1, 5 => optimalsol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(5, 1, Coluna.DualBound(master, 20.0)), LightNode(2, 1, Coluna.DualBound(master, 20.0))], 2 => [LightNode(4, 2, Coluna.DualBound(master, 20.0)), LightNode(3, 2, Coluna.DualBound(master, 20.0))], 3 => [], 4 => [], 5 => [] ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == optimalsol @test 2 in dividealg.nodes_created_by_divide @test 3 in dividealg.nodes_created_by_divide @test 4 in dividealg.nodes_created_by_divide @test 5 in dividealg.nodes_created_by_divide @test !(3 in dividealg.run_divide_on_nodes) ## 3 and 4 should not be in run_divide_on_nodes ; they are pruned because their local db is worst than the current best primal sol @test !(4 in dividealg.run_divide_on_nodes) @test !(5 in dividealg.run_divide_on_nodes) end register!(unit_tests, "treesearch", test_local_db) #```mermaid #graph TD # 0( ) --> |lp_dual_bound = 55, \n ip_primal_sol = 60| 1 # 1((1)) --> |lp_dual_bound = 55, \n ip_primal_sol = 56| 2((2)) # 2 --> |lp_dual_bound = 56, \n ip_primal_sol = 56| 3((3)) # 2 --> |lp_dual_bound = 56, \n ip_primal_sol = 56| 4((4)) # 1 --> |lp_dual_bound = 57, \n ip_primal_sol = 60| 5((5)) # 5 --> |STOP \n because primal sol found at 2\n is better than current db| 6( ) #``` # exploration should not stop at nodes 3 and 4 because the gap between the local lp_dual_bound and the ip_primal_bound is closed, but not with the global dual bound. However, it should be stopped at node 5 because the primal bound found at node 2 is better than the local dual bound of node 5. # status: OPTIMAL with primal solution = 56.0 function test_pruning() ## create an empty formulation reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate4 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 56.0)) optstate5 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 57.0)) primal_sol1 = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 60.0, Coluna.ColunaBase.FEASIBLE_SOL) opt_sol = Coluna.PrimalSolution(master, Vector{Coluna.MathProg.VarId}(), Vector{Float64}(), 56.0, Coluna.ColunaBase.FEASIBLE_SOL) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3, 4 => optstate4, 5 => optstate5, 6 => Coluna.OptimizationState(master), ##should not be called 7 => Coluna.OptimizationState(master) ##should not be called ), Dict( 1 => primal_sol1, 2 => opt_sol ), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(5, 1, Coluna.DualBound(master, 55.0)), LightNode(2, 1, Coluna.DualBound(master, 55.0))], 2 => [LightNode(4, 2, Coluna.DualBound(master, 55.0)), LightNode(3, 2, Coluna.DualBound(master, 55.0))], 3 => [], 4 => [], 5 => [LightNode(7, 2, Coluna.DualBound(master, 57.0)), LightNode(6, 2, Coluna.DualBound(master, 57.0))], ##should not be called 6 => [],##should not be called 7 => [] ##should not be called, ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OPTIMAL @test Coluna.get_best_ip_primal_sol(algstate) == opt_sol @test !(6 in dividealg.nodes_created_by_divide) # 6 and 7 should not be created as 5 is pruned @test !(7 in dividealg.nodes_created_by_divide) @test !(3 in dividealg.run_divide_on_nodes) ## 3 and 4 should not be in run_divide_on_nodes ; they are pruned because their local db is equal to the current best primal sol @test !(4 in dividealg.run_divide_on_nodes) @test !(5 in dividealg.run_divide_on_nodes) ## 5 is not in run_divide_on_nodes either ; it is pruned because best primal bound found at node 2 is better than its db @test 5 in conqueralg.run_conquer_on_nodes ## however, 5 is in run_conquer_on_nodes because when it inherites the db from its parent, this db is better than the best primal solution @test !(6 in conqueralg.run_conquer_on_nodes) @test !(7 in conqueralg.run_conquer_on_nodes) end register!(unit_tests, "treesearch", test_pruning) function test_one_leaf_infeasible_and_then_node_limit() ## create an empty formulation reform, env = _tree_search_reformulation() master = Coluna.MathProg.getmaster(reform) input = Coluna.OptimizationState(master) optstate1 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 55.0)) optstate2 = Coluna.OptimizationState(termination_status = Coluna.INFEASIBLE, master) optstate3 = Coluna.OptimizationState(termination_status = Coluna.OPTIMAL, master, lp_dual_bound = Coluna.MathProg.DualBound(master, 65.0)) conqueralg = DeterministicConquer( Dict( 1 => optstate1, 2 => optstate2, 3 => optstate3 # should not be called ), Dict(), [] ) dividealg = DeterministicDivide( Dict( 1 => [LightNode(2, 1, Coluna.DualBound(master, 65.0)), LightNode(3, 1, Coluna.DualBound(master, 65.0))], 2 => [], ##should not be called 3 => [], ##should not be called ), [], [] ) algo = Coluna.Algorithm.TreeSearchAlgorithm( conqueralg = conqueralg, dividealg = dividealg, explorestrategy = Coluna.TreeSearch.DepthFirstStrategy(), maxnumnodes = 2 ) Coluna.set_optim_start_time!(env) search_space = Coluna.TreeSearch.new_space(TestBaBSearchSpace, algo, reform, input) algstate = Coluna.TreeSearch.tree_search(algo.explorestrategy, search_space, env, input) @test Coluna.getterminationstatus(algstate) == Coluna.OTHER_LIMIT @show Coluna.getterminationstatus(algstate) end register!(unit_tests, "treesearch", test_one_leaf_infeasible_and_then_node_limit)

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

828c61e9434b6af5f7908e42aacd17de35f08482

docs

5474

# Coluna 0.8.1 This is a minor update Features: - A new parameter whether to presolve the DW reformulation or not - A new parameter whether to do strong integrality check in column generation Fixed bugs: - A bug in the presolve algorithm when the partial solution to fix contains deactivated variables # Coluna 0.8.0 This is a major update which implements the presolve algorithm. Other features: - Possibility for the user to get the custom data of a column (i.e., SP solution) in the global solution. - Print the master and DW subproblem with only user-defined variables and constraints. - One can now specify the branching priority of columns, either through branching priority of DW sub-problems, or directly in the CustomData of the SP solution It also resolves some bugs: - Correction in dual price smoothing stabilization - Correction in integrality check inside column generation. - Correction in calculating initial (global) bounds of the master representative (implicit) variables. - Corrected the "Sleeping bug" related to the Id type promotion, which appeared in Julia 1.10 - Removed superfluous Heuristics module. - Global dual bound printer is corrected - Strong branching printer is corrected. # Coluna 0.7.0 This is minor update with two breaking changes: - Bounds of the representative variables in the master are now global (multiplicity of subproblem * bound) - DivideOutput has only one argument now - Generating child nodes from branching candidates is moved from select!() to advanced_select!(). This allows us to simplify the interface of branching candidates (we remove nodes from them). This simplification also serves to prepare the diving implementation. (PR 1072) - SbNode and Node definitions have been changed. # Coluna 0.6.6 - DynamicSparseArrays 0.7 main features are tested against JET. - Bug with the integer tolerance of projected solutions fixed - Improvements about the construction of the reformulation - Strong branching was not returning ip solution found when evaluating candidates # Coluna 0.6.5 This is a minor update that provides documentation together with tests and several bug fixes for the tree search algorithm. Stabilization for the column generation algorithm is now in beta version. # Coluna 0.6.4 This is a minor update that provides documentation and a bug fix in the integration of the column generation algorithm with the branch-and-bound. # Coluna 0.6.3 This is a minor update that provides: - improvements in column generation interface and generic functions - bugfix in column generation (wrong calculation of the lagrangian dual bound when identical subproblems) - column generation stabilization (alpha version) # Coluna 0.6.2 This is a minor update that provides fixes in the Benders cut generation algorithm and documentation for the Benders API. # Coluna 0.6.1 This is a minor update but some changes may affect the integration of external algorithms with Coluna. Fixes: - Workflow of Benders algorithms is now fixed. More documentation will be available soon. Changes: - `ColunaBase.Bound{Space, Sense}` is now `ColunaBase.Bound`. The two parameters are now flags in the struct. All mathematical operations are not supported anymore, we need to convert the `Bound` to a `<:Real`. - `Algorithm.OptimizationState{F,S}` does not depend on the objective sense anymore and is now `Algorithm.OptimizationState{F}` - Improve Benders implementation & starting writing documentation # Coluna 0.6.0 This release is a major update of the algorithms as it implements the architectural choices of 0.5.0 in column generation and benders. About the algorithms: - We separated the generic codes and the interfaces from the implementation (doc will be available soon). The default implementation of algorithms is in the `Algorithm` module. Four new submodules `TreeSearch`, `Branching`, `ColGen`, and `Benders` contain generic code and interface. They are independent. - Refactoring of column generation - Refactoring and draft of benders cut generation - Tests and documentation - Various bug fixes - Some regressions as indicated in the Readme. # Coluna 0.5.0 This release is a major update of the algorithms. From now on, we will release new versions more frequently. In the `Algorithm` submodule: - Interface & generic implementation for the tree search algorithm; default implementation of a branch & bound; documentation - Simplified interface for storages; documentation - Interface & generic implementation for the branching algorithm; interface & default implementation for the strong branching; documentation - Preparation of the conquer algorithm refactoring - Preparation of the column generation algorithm refactoring - Preparation of the refactoring of the algorithms calling the subsolver - End of development of the Preprocessing algorithm (no unit tests and had bugs); it will be replaced by the Presolve algorithm that does not work - Increase of the reduced cost tolerance in the column generation algorithm - Separation of algorithm and printing logic - Various bug fixes In the `MathProg` submodule: - `VarIds` & `ConstrIds` are subtype of Integer so we can use them as indices of sparse vectors and arrays - Solution are stored in sparse array from `SparseArrays` (not a packed memory array from `DynamicSparseArray` anymore because the solution is static) Other: - Documentation of dynamic sparse arrays - Support of expressions in BlockDecomposition

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Coluna.jl [![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://atoptima.github.io/Coluna.jl/stable) ![CI](https://github.com/atoptima/Coluna.jl/workflows/CI/badge.svg?branch=master) [![codecov](https://codecov.io/gh/atoptima/Coluna.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/atoptima/Coluna.jl) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![License: MPL 2.0](https://img.shields.io/badge/License-MPL%202.0-brightgreen.svg)](https://opensource.org/licenses/MPL-2.0) Coluna is a branch-and-price-and-cut framework written in Julia. You write an original MIP that models your problem using the [JuMP](https://github.com/jump-dev/JuMP.jl) modeling language and our specific extension [BlockDecomposition](https://github.com/atoptima/BlockDecomposition.jl) offers a syntax to specify the problem decomposition. Then, Coluna reformulates the original MIP and optimizes the reformulation using the algorithms you choose. Coluna aims to be very modular and tweakable so that you can define the behavior of your customized branch-and-price-and-cut algorithm. ## Installation Coluna is a [Julia Language](https://julialang.org/) package. You can install Coluna through the Julia package manager. Open Julia's interactive session (REPL) and type: ``` ] add Coluna ``` The documentation provides examples to run advanced branch-cut-and-price. Improvements in documentation are expected in the future. You can browse the [stable documentation](https://atoptima.github.io/Coluna.jl/stable) if you work with the latest release or the [dev documentation](https://atoptima.github.io/Coluna.jl/latest) if you work with the master version of Coluna. ## Features We aim to integrate into Coluna the state-of-the-art techniques used for branch-and-cut-and-price algorithms. - ![Stable](https://img.shields.io/badge/-stable-brightgreen) Features which are well-tested (but performance may still be improved). - Dantzig-Wolfe decomposition - Branch-and-bound algorithm (with branching in master) - Column generation (MILP pricing solver/pricing callback) - ![Beta](https://img.shields.io/badge/-beta-green) Features that work well but need more tests/usage and performance review before being stable: - Strong branching (with branching in master) - Stabilization for column generation - Cut generation (robust and non-robust) - Benders decomposition - Preprocessing (presolve) of formulations and reformulations - ![Alpha](https://img.shields.io/badge/-alpha-yellow) Features that should work. Structural work is done but these features may have bugs: - Benders cut generation - ![Dev](https://img.shields.io/badge/-dev-orange) Features in development. - Clean-up of the master formulation (removal of unpromising columns and cuts) - Saving/restoring LP basis when changing a node in branch-and-bound ## Contributing If you encounter a bug or something unexpected happens while using Coluna, please open an issue via the GitHub issues tracker. See the list of [contributors](https://github.com/atoptima/Coluna.jl/graphs/contributors) who make Coluna possible. ## Premium support Using Coluna for your business? [Contact us](https://atoptima.com/contact/?sup) to get tailored and qualified support. ## Acknowledgments The platform development has received an important support grant from the international scientific society [**Mathematical Optimization Society (MOS)**](http://www.mathopt.org/) and [**Région Nouvelle-Aquitaine**](https://www.nouvelle-aquitaine.fr/). [**Atoptima**](https://atoptima.com/) [**University of Bordeaux**](https://www.u-bordeaux.fr/) [**Inria**](https://www.inria.fr/fr) ## Related packages - [BlockDecomposition](https://github.com/atoptima/BlockDecomposition.jl) is a JuMP extension to model decomposition. - [DynamicSparseArrays](https://github.com/atoptima/DynamicSparseArrays.jl) provides data structures based on packed-memory arrays for dynamic sparse matrices.

Coluna

https://github.com/atoptima/Coluna.jl.git

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--- name: Bug report about: Create a report to help us improve title: '' labels: bug assignees: '' --- **Describe the bug** A clear and concise description of the bug. **To Reproduce** Give us a link to your model and instance (a Julia project with the Manifest file for example). **Expected behavior** A clear and concise description of what you expected to happen. If possible, copy the error message with the stacktrace. **Environment (please complete the following information):** - Julia version [e.g. 1.2] - OS: [e.g. Linux, Windows, MacOs] **Additional context** Add any other context about the problem here.

Coluna

https://github.com/atoptima/Coluna.jl.git

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--- name: Feature request about: Suggest an idea for this project title: '' labels: enhancement assignees: guimarqu --- **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the feature or solution you'd like** A clear and concise description of what you want to happen. **Do you feel able to code this feature or solution?** Why? What do you need?

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Dynamic Sparse Arrays This package aims to provide dynamic sparse vectors and matrices in Julia. Unlike the sparse arrays provided in `SparseArrays`, arrays from this package have unfixed sizes. It means that we can add or delete rows and columns after the instantiation of the array. `DynamicSparseArrays` is a registered package. ## Introduction Coluna is a branch-cut-and-price framework. It means that Coluna's algorithms dynamically generate constraints and variables. Therefore, the coefficient matrix (which is usually sparse) must support the addition of new rows and columns. For this purpose, we implemented the packed-memory array data structure to handle the dynamic sparse vector introduced in the following papers: > BENDER, Michael A. et HU, Haodong. An adaptive packed-memory array. ACM Transactions on Database Systems (TODS), 2007, vol. 32, no 4, p. 26. > BENDER, Michael A., DEMAINE, Erik D., et FARACH-COLTON, Martin. Cache-oblivious B-trees. SIAM Journal on Computing, 2005, vol. 35, no 2, p. 341-358. On top of the packed-memory array, we implemented the data structure introduced in the following paper to handle the dynamic sparse matrix. > WHEATMAN, Brian et XU, Helen. Packed Compressed Sparse Row: A Dynamic Graph Representation. In : 2018 IEEE High Performance extreme Computing Conference (HPEC). IEEE, 2018. p. 1-7. The implementation may vary from the description in the papers. If you find some enhancements, please contact [guimarqu](https://github.com/guimarqu). ## Overview The packed-memory array (`PackedMemoryArray{K,T}`) is a `Vector{Union{Nothing,Tuple{K,T}}}` where `K` is the type of the keys and `T` is the type of the values. We keep empty entries (i.e. `Nothing`) in the array to add new values later fast. Non-empty entries are sorted by ascending key order. The array is virtually split into segments of equal size. The goal is to maintain the density (i.e. number of non-empty values/size of the segment) of each segment between pre-defined bounds. We also consider the density of certain unions of segments represented by nodes of the tree in gray. The root node of the tree is the union of all segments, thus the whole array. When one node of the tree has a density outside the allowed bounds, we need to rebalance the parent. It means that we redistribute the empty and non-empty entries to fit the density bounds. If the density bounds are not respected at the root node, we resize the array. On top of the packed-memory array, there is the (`PackedCSC{K,T}`). This is a particular case of a matrix where values are of type `T`, row keys of type `K`, and column keys of type `Int`. Each column of the matrix (partition) is delimited by a semaphore which is a non-empty entry with a reserved key value defined by the `semaphore_key` function. In the example, the first partition has its semaphore at position 1, starts at position 2, and finishes at position 9. At position 10, it's the semaphore that signals the beginning of the second partition. In each partition, non-empty entries are sorted by ascending key order. As you can see, the `PackedCSC{K,T}` is not well suited to the matrix. Indeed, each column is associated with a partition. If you have a column with only zero values, the array will contain a partition with only empty entries. Lastly, the type of column key is `Int`. Therefore, built on top of `PackedCSC{K,T}`, `MappedCSC{K,L,T}` corrects all these shortcomings. This data structure just associates a column key of type `L` to each partition of `PackedCSC{K,T}`. ```@raw html <div style="width:75%; margin-left:auto; margin-right:auto"> ``` ![Dynamic Sparse Arrays](assets/img/dynamic_sparse_arrays.svg) ```@raw html <p style="text-align: center;">Architecture overview.</p> </div> ``` ## References ```@meta CurrentModule = DynamicSparseArrays ``` ```@docs dynamicsparsevec dynamicsparse ```

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Introduction !!! warning We assume that readers are familiar with integer programming and exact optimization methods. Coluna is under active development. Some features are undocumented because they are very experimental. Current users are expected to read the source code. Coluna is a framework written in Julia to implement a decomposition approach to optimize block-structured mixed-integer programs (MIP). Coluna relies on the tools of the JuMP-dev community at both ends of the problem treatment. It uses the JuMP modeling language upfront and MathOptInterface (MOI) to delegate master and subproblems to MIP solvers. The user introduces an original MIP that models his problem using the JuMP along our specific extension BlockDecomposition that offers a syntax to specify the problem decomposition. Coluna reformulates the original MIP using Dantzig-Wolfe and Benders decomposition techniques. Then, Coluna optimizes the reformulation using the algorithm chosen by the user. Coluna offers a "black-box" implementation of the branch-and-cut-and-price algorithm: 1. The input is the set of constraints and variables of the MIP in its natural/compact formulation (formulated with JuMP or MOI). 2. BlockDecomposition allows the user to provide Coluna with his decomposition of the model. The BlockDecomposition syntax allows the user to implicitly define subsystems in the MIP on which the decomposition is based. These subsystems are described by rows and/or columns indices. 3. The reformulation associated with the decomposition defined by the user is automatically generated by Coluna, without requiring any input from the user to define master columns, their reduced cost, pricing/separation problem, or Lagrangian bound. 4. A default column (and cut) generation procedure is implemented. It relies on underlying MOI optimizers to handle master and subproblems. However, the user can use pricing callbacks to solve the subproblems. 5. A branching scheme that preserves the pricing problem structure is offered by default; it runs based on priorities and directives specified by the user on the original variables. ## Installation Coluna is a package for [Julia 1.6+](https://docs.julialang.org/en/v1/manual/documentation/index.html). It requires JuMP to model the problem, BlockDecomposition to define the decomposition, and a [MIP solver supported by MathOptInterface](https://jump.dev/JuMP.jl/stable/installation/#Getting-Solvers-1) to optimize the master and the subproblems. You can install Coluna and its dependencies through the package manager of Julia by entering : ``` ] add Coluna ``` ## Acknowledgements **Atoptima**, **Mathematical Optimization Society (MOS)**, **Région Nouvelle-Aquitaine**, **University of Bordeaux**, and **Inria**

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Question & Answer #### Default algorithms of Coluna do not beat the commercial solver I usually use. Is it normal ? Yes it is. Solvers such as Gurobi, Cplex ... are handy powerful black-box tools. They can run a very efficient presolve step to simplify the formulation, automatically apply lots of valid inequalities (such as MIR or cover cuts), choose good branching strategies, or also run heuristics. However, when your formulation reaches a certain size, commercial solvers may run for hours without finding anything. This is the point where you may want to decompose your formulation. Coluna is a framework, not a solver. It provides algorithms to try column generation on your problem very easily. Then, you can devise your own branch-cut-and-price algorithm on top of Coluna's algorithms. to scale up and hopefully beats the commercial solver. To start customizing Coluna for your own problem, you can [separate valid inequalities](../user/callbacks/#Separation-callbacks) or [call your own algorithm that optimizes subproblems](../user/callbacks/#Pricing-callback). ## I'm using Gurobi as a subsolver #### My license prevents me from running several environments at the same time. How can I use a single environment for the master and all subproblems? You can use the `Gurobi.Env` constructor to create a single environment and pass it to the optimizers. ```julia const GRB_ENV = Gurobi.Env() coluna = optimizer_with_attributes( Coluna.Optimizer, "params" => Coluna.Params( solver = Coluna.Algorithm.TreeSearchAlgorithm() # default branch-cut-and-price ), "default_optimizer" => () -> Gurobi.Optimizer(GRB_ENV) ); ``` If you get a Gurobi error 10002, you should wrap the Gurobi environment as a reference to initialize it during runtime instead of compile time ([reference](https://github.com/jump-dev/Gurobi.jl/issues/424)). ```julia const GRB_ENV_REF = Ref{Gurobi.Env}() function __init__() GRB_ENV_REF[] = Gurobi.Env() return nothing end coluna = optimizer_with_attributes( Coluna.Optimizer, "params" => Coluna.Params( solver = Coluna.Algorithm.TreeSearchAlgorithm() # default branch-cut-and-price ), "default_optimizer" => () -> Gurobi.Optimizer(GRB_ENV_REF[]) ); ``` #### How to disable all outputs from Gurobi? You can refer to the following [article](https://support.gurobi.com/hc/en-us/articles/360044784552-How-do-I-suppress-all-console-output-from-Gurobi-) from Gurobi's knowledge base. We confirm that adding the following entry in the `gurobi.env` file works with Gurobi 10+: ``` LogToConsole 0 ```

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Algorithm API !!! danger This is WIP. The API will change in future releases. An algorithm is a procedure that given a model and and input performs some operations and returns an output. ```@docs run! ``` Parameters of an algorithm may contain its child algorithms which used by it. Therefore, the algoirthm tree is formed, in which the root is the algorithm called to solver the model (root algorithm should be an optimization algorithm, see below). > TODO: explain why the parent algorithm must manage the records/storages of child algorithm. Algorithms are divided into two types : "manager algorithms" and "worker algorithms". Worker algorithms just continue the calculation. They do not store and restore units as they suppose it is done by their master algorithms. Manager algorithms may divide the calculation flow into parts. Therefore, they store and restore units to make sure that their child worker algorithms have units prepared. A worker algorithm cannot have child manager algorithms. Examples of manager algorithms : TreeSearchAlgorithm (which covers both BCP algorithm and diving algorithm), conquer algorithms, strong branching, branching rule algorithms (which create child nodes). Examples of worker algorithms : column generation, SolveIpForm, SolveLpForm, cut separation, pricing algorithms, etc. ## Optimization algorithms Optimization algorithms return an `OptimizationState`. ```@docs OptimizationState ``` ### Conventions > TODO: WIP - **infeasible**: infinite bounds, no solution, infeasible termination status.

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Algorithms !!! danger Work in progress. ## Parameters of an algorithm From a user perspective, the algorithms are objects that contains a set of parameters. The object must inherit from `Coluna.AlgoAPI.AbstractAlgorithm`. We usually provide a keyword constructor to define default values for parameters and therefore ease the definition of the object. ```julia struct MyCustomAlgorithm <: Coluna.AlgoAPI.AbstractAlgorithm param1::Int param2::Float64 child_algo::Coluna.AlgoAPI.AbstractAlgorithm end # Help the user to define the algorithm: function MyCustomAlgorithm(; param1 = 1, param2 = 2, child_algo = AnotherAlgorithm() ) return MyCustomAlgorithm(param1, param2, child_algo) end ``` Algorithms can use other algorithms. They are organized as a tree structure. ** Example for the TreeSearchAlgorithm **: ```mermaid graph TD TreeSearchAlgorithm ---> ColCutGenConquer TreeSearchAlgorithm ---> ClassicBranching ColCutGenConquer --> ColumnGeneration ColCutGenConquer --> RestrictedHeuristicMaster RestrictedHeuristicMaster --> SolveIpForm#2 ColumnGeneration --> SolveLpForm#1 ColumnGeneration --> SolveIpForm#1 ColumnGeneration --> CutCallbacks ``` ```@docs Coluna.AlgoAPI.AbstractAlgorithm ``` ## Init ### Parameters checking When Coluna starts, it initializes the algorithms chosen by the user. A most important step is to check the consistency of the parameters supplied by the user and the compatibility of the algorithms with the model that will be received (usually `MathProg.Reformulation`). Algorithms usually have many parameters and are sometimes interdependent and nested. It is crucial to ensure that the user-supplied parameters are correct and give hints to fix them otherwise. The entry-point of the parameter consistency checking is the following method: ```@docs Coluna.Algorithm.check_alg_parameters ``` Developer of an algorithm must implement the following methods: ```@docs Coluna.Algorithm.check_parameter ``` ### Units usage ```@docs Coluna.AlgoAPI.get_child_algorithms Coluna.AlgoAPI.get_units_usage ``` ## Run ```@docs Coluna.AlgoAPI.run! ```

Coluna

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```@meta CurrentModule = Coluna ``` # [Benders cut generation](@id api_benders) Coluna provides an interface and generic functions to implement a Benders cut generation algorithm. In this section, we are first going to present the generic functions, the implementation with some theory backgrounds and then give the references of the interface. The default implementation is based on the paper of You can find the generic functions and the interface in the `Benders` submodule and the default implementation in the `Algorithm` submodule at `src/Algorithm/benders`. ## Context The `Benders` submodule provides an interface and generic functions to implement a benders cut generation algorithm. The implementation depends on an object called `context`. ```@docs Coluna.Benders.AbstractBendersContext ``` Benders provides two types of context: ```@docs Coluna.Algorithm.BendersContext Coluna.Algorithm.BendersPrinterContext ``` ## Generic functions Generic functions are the core of the Benders cut generation algorithm. There are three generic functions: ```@docs Coluna.Benders.run_benders_loop! ``` See ... ```@docs Coluna.Benders.run_benders_iteration! ``` See ... These functions are independent of any other submodule of Coluna. You can use them to implement your own Benders cut generation algorithm. ## Reformulation The default implementation works with a reformulated problem contained in `MathProg.Reformulation` where master and subproblems are `MathProg.Formulation` objects. The master has the following form: ```math \begin{aligned} \min \quad& cx + \sum_{k \in K} \eta_k & &\\ \text{s.t.} \quad& Ax \geq a & & (1) \\ & \text{< benders cuts>} & & (2) \\ & l_1 \leq x \leq u_1 & & (3) \\ & \eta_k \in \mathbb{R} & \forall k \in K \quad& (4) \end{aligned} ``` where $x$ are first-stage variables, $\eta_k$ is the second-stage cost variable for the subproblem $k$, constraints $(1)$ are the first-stage constraints, constraints $(2)$ are the Benders cuts, constraints $(3)$ are the bounds on the first-stage variables, and expression $(4)$ shows that second-stage variables are free. The subproblems have the following form: ```math \begin{aligned} \min \quad& fy + {\color{gray} \mathbf{1}z' + \mathbf{1}z''} &&& \\ \text{s.t.} \quad& Dy {\color{gray} + z'} \geq d - B\bar{x} && (5) \quad& {\color{blue}(\pi)} \\ & Ey {\color{gray} + z''} \geq e && (6) \quad& {\color{blue}(\rho)} \\ & l_2 \leq y \leq u_2 && (7) \quad& {\color{blue}(\sigma)} \end{aligned} ``` where $y$ are second-stage variables, $z'$ and $z''$ are artificial variables (in grey because they are deactivated by default), constraints (5) are the reformulation of linking constraints using the first-stage solution $\bar{x}$, constraints (6) are the second-stage constraints, and constraints (7) are the bounds on the second-stage variables. In blue, we define the dual variables associated to these constraints. **References**: ```@docs Coluna.Benders.is_minimization Coluna.Benders.get_reform Coluna.Benders.get_master Coluna.Benders.get_benders_subprobs ``` ## Main loop This is a description of how the `Coluna.Benders.run_benders_loop!` generic function behaves with the default implementation. The loop stops if one of the following conditions is met: - the master is infeasible - a separation subproblem is infeasible - the time limit is reached - the maximum number of iterations is reached - no new cut is generated The default implementation returns: ```@docs Coluna.Algorithm.BendersOutput ``` **References**: ```@docs Coluna.Benders.setup_reformulation! Coluna.Benders.stop_benders Coluna.Benders.after_benders_iteration Coluna.Benders.AbstractBendersOutput Coluna.Benders.benders_output_type Coluna.Benders.new_output ``` ## Benders cut generation iteration This is a description of how the `Coluna.Benders.run_benders_iteration!` generic function behaves with the default implementation. These are the main steps of a Benders cut generation iteration without stabilization. Click on the step to go to the corresponding section. ```mermaid flowchart TB; id1(Optimize master) id2(Treat unbounded master) id3(Setup separation subproblems) id4(Separation subproblem iterator) id5(Optimize separation subproblem) id6(Push cut into set) id9(Master is unbounded?) id10(Error) id7(Insert cuts) id11(Build primal solution) id8(Iteration output) id1 --unbounded--> id2 id2 --certificate--> id3 id1 -- optimal --> id3 id3 --> id4 id4 -- subproblem --> id5 id5 --> id6 id6 --> id4 id4 -- end --> id9 id9 -- yes --> id10 id9 -- no --> id7 id7 --> id11 id11 --> id8 click id1 href "#Master-optimization" "Link to doc" click id2 href "#Unbounded-master-case" "Link to doc" click id3 href "#Setup-separation-subproblems" "Link to doc" click id4 href "#Subproblem-iterator" "Link to doc" click id5 href "#Separation-subproblem-optimization" "Link to doc" click id6 href "#Set-of-generated-cuts" "Link to doc" click id9 href "#Unboundedness-check" "Link to doc" click id11 href "#Current-primal-solution" "Link to doc" click id7 href "#Cuts-insertion" "Link to doc" click id8 href "#Iteration-output" "Link to doc" ``` In the default implementation, some sections may have different behaviors depending on the result of previous steps. ### Master optimization This operation consists in optimizing the master problem in order to find a first-level solution $\bar{x}$. In the default implementation, master optimization can be performed using `SolveLpForm` (LP solver) or `SolveIpForm` (MILP solver). When getting the solution, we store the current value of second stage variables $\bar{\eta}_k$ as incumbent value (see `Coluna.MathProg.getcurincval`). It returns an object of the following type: ```@docs Coluna.Algorithm.BendersMasterResult ``` **References**: ```@docs Coluna.Benders.optimize_master_problem! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Unbounded master case Second stage cost $\eta_k$ variables are free. As a consequence, the master problem is unbounded when there is no optimality Benders cuts. In this case, `Coluna.Benders.treat_unbounded_master_problem_case!` is called. The main goal of the default implementation of this method is to get the dual infeasibility certificate of the master problem. If the master has been solved with a MIP solver at the previous step, we need to relax the integrality constraints to get a dual infeasibility certificate. If the solver does not provide a dual infeasibility certificate, the implementation has an "emergency" routine to provide a first-stage feasible solution by solving the master LP with cost of second stage variables set to zero. We recommend using a solver that provides a dual infeasibility certificate and avoiding the "emergency" routine. **References**: ```@docs Coluna.Benders.treat_unbounded_master_problem_case! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Setup separation subproblems !!! info The separation subproblems differs depending on whether the restricted master is unbounded or not: - if the restricted master is optimal, the generic function calls `Coluna.Benders.update_sp_rhs!` - if the restricted master is unbounded, the generic function calls `Coluna.Benders.setup_separation_for_unbounded_master_case!` Default implementation of `Coluna.Benders.update_sp_rhs!` updates the right-hand side of the linking constraints (5). **Reference**: ```@docs Coluna.Benders.update_sp_rhs! ``` Default implementation of `Coluna.Benders.setup_separation_for_unbounded_master_case!` gives rise to the formulation proposed in Lemma 2 of Bonami et al: ```math \begin{aligned} (SepB) \equiv \min \quad& fy + {\color{gray} \mathbf{1}z' + \mathbf{1}z''} &&& \\ \text{s.t.} \quad& Dy {\color{gray} + z'} \geq -B\bar{x} && (5a) \quad& {\color{blue}(\pi)} \\ & Ey {\color{gray} + z''} \geq 0 && (6a) \quad& {\color{blue}(\rho)} \\ & y \geq 0 && (7a) \quad& {\color{blue}(\sigma)} \end{aligned} ``` where $y$ are second-stage variables, $z'$ and $z''$ are artificial variables (in grey because they are deactivated by default), and $\bar{x}$ is an unbounded ray of the restricted master. **Reference**: ```@docs Coluna.Benders.setup_separation_for_unbounded_master_case! ``` ### Subproblem iterator Not implemented yet. ### Separation subproblem optimization The default implementation first optimize the subproblem without the artificial variables $z'$ and $z''$. In the case where it finds $(\bar{\pi}, \bar{\rho}, \bar{\sigma})$ an optimal dual solution to the subproblem, the following cut is generated: ```math \eta_k + \bar{\pi}Bx \geq d\bar{\pi} + \bar{\rho}e + \bar{\sigma_{\leq}} l_2 + \bar{\sigma_{\geq}} u_2 ``` with $\bar{\sigma_{\leq}} l_2$ (respectively $\bar{\sigma_{\geq}} u_2$) the dual of the left part (respectively the right part) of constraint $l_2 \leq y \leq u_2$ of the subproblem. In the case where it finds the subproblem infeasible, it calls `Coluna.Benders.treat_infeasible_separation_problem_case!`. The default implementation of this method activates the artificial variables $z'$ and $z''$, sets the cost of second stage variables to 0, and optimizes the subproblem again. If a solution with no artificial variables is found, the following cut is generated: ```math \bar{\pi}Bx \geq d\bar{\pi} + \bar{\rho}e + \bar{\sigma_{\leq}} l_2 + \bar{\sigma_{\geq}} u_2 ``` Both methods return an object of the following type: ```@docs Coluna.Algorithm.BendersSeparationResult ``` **References**: ```@docs Coluna.Benders.optimize_separation_problem! Coluna.Benders.treat_infeasible_separation_problem_case! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Set of generated cuts You can define your data structure to manage the cuts generated at a given iteration. Columns are inserted after the optimization of all the separation subproblems to allow the parallelization of the latter. In the default implementation, cuts are represented by the following data structure: ```@docs Coluna.Algorithm.GeneratedCut ``` We use the following data structures to store the cuts and the primal solutions to the subproblems: ```@docs Coluna.Algorithm.CutsSet Coluna.Algorithm.SepSolSet ``` The default implementation of `push_in_set!` has the responsibility to check if the cut is violated. Given $\bar{\eta}_k$ solution to the restricted master and $\bar{y}$ solution to the separation problem, the cut is considered as violated when: - the separation subproblem was infeasible - or $\bar{\eta}_k \geq f\bar{y}$ **References**: ```@docs Coluna.Benders.set_of_cuts Coluna.Benders.set_of_sep_sols Coluna.Benders.push_in_set! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Unboundedness check !!! info This check is performed only when the restricted master is unbounded. To perform this check, we need a solution to each separation problem. Let $(\bar{\eta}_k)_{k \in K}$ be the value of second stage variables in the dual infeasibility certificate of the restricted master. Let $\bar{y}$ be an optimal solution to the separation problem **(SepB)**. As indicated by Bonami et al., if $f\bar{y} \leq \sum\limits_{k \in K} \bar{\eta}_k$, then the original problem is unbounded (by definition of an unbounded ray of the original problem). **References**: ```@docs Coluna.Benders.master_is_unbounded ``` ### Cuts insertion The default implementation inserts into the master all the cuts stored in the `CutsSet` object. **Reference**: ```@docs Coluna.Benders.insert_cuts! ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Current primal solution Lorem ipsum. **References**: ```@docs Coluna.Benders.build_primal_solution ``` ### Iteration output ```@docs Coluna.Algorithm.BendersIterationOutput ``` **References**: ```@docs Coluna.Benders.AbstractBendersIterationOutput Coluna.Benders.benders_iteration_output_type Coluna.Benders.new_iteration_output ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ### Getters for Result data structures | Method name | Master | Separation | | ---------------- | ------ | ---------- | | `is_unbounded` | X | X | | `is_infeasible` | X | X | | `is_certificate` | X | | | `get_primal_sol` | X | X | | `get_dual_sol` | X | | | `get_obj_val` | X | X | ```@docs Coluna.Benders.is_unbounded Coluna.Benders.is_infeasible Coluna.Benders.is_certificate Coluna.Benders.get_primal_sol Coluna.Benders.get_dual_sol Coluna.Benders.get_obj_val ``` Go back to the [cut generation iteration diagram](#Benders-cut-generation-iteration). ## Stabilization Not implemented yet.

Coluna

https://github.com/atoptima/Coluna.jl.git

[ "MPL-2.0" ]

0.8.1

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```@meta CurrentModule = Coluna ``` # Branching API Coluna provides default implementations for the branching algorithm and the strong branching algorithms. Both implementations are built on top of an API that we describe here. ## Candidates selection Candidates selection is the first step (and sometimes the only step) of any branching algorithm. It chooses what are the possible branching constraints that will generate the children of the current node of the branch-and-bound tree. Coluna provides the following function for this step: ```@docs Branching.select! ``` It works as follows. The user chooses one or several branching rules that indicate the type of branching he wants to perform. This may be on a single variable or on a linear expression of variables for instance. The branching rule must implement `apply_branching_rule` that generates the candidates. The latter are the variables or expressions on which the branch-and-bound may branch with additional information that is requested by Coluna's branching implementation through the API. Then, candidates are sorted according to a selection criterion (e.g. most fractional). The algorithm keeps a certain number of candidates (one for classic branching, and several for strong branching). It generates the children of each candidate kept. At last, it returns the candidates kept. ### Branching rule ```@docs Branching.AbstractBranchingRule Branching.apply_branching_rule ``` ### Candidate ```@docs Branching.AbstractBranchingCandidate Branching.getdescription Branching.get_lhs Branching.get_local_id Branching.generate_children! ``` ### Selection criterion ```@docs Branching.AbstractSelectionCriterion Branching.select_candidates! ``` ### Branching API ```@docs Branching.get_selection_nb_candidates Branching.branching_context_type Branching.new_context Branching.get_int_tol Branching.get_rules Branching.get_selection_criterion ``` Method `advanced_select!` is part of the API but presented just below. ## Advanced candidates selection If the candidates' selection returns several candidates will all their children, advanced candidates selection must keep only one of them. The advanced candidates' selection is the place to evaluate the children to get relevant additional key performance indicators about each branching candidate. Coluna provides the following function for this step. ```@docs Branching.advanced_select! ``` Coluna has two default implementations for this method: - for the classic branching that does nothing because the candidates selection returns 1 candidate - for the strong branching that performs several evaluations of the candidates. Let us focus on the strong branching. Strong branching is a procedure that heuristically selects a branching constraint that potentially gives the best progress of the dual bound. The procedure selects a collection of branching candidates based on their branching rule (done in classic candidate selection) and their score (done in advanced candidate selection). Then, the procedure evaluates the progress of the dual bound in both branches of each branching candidate by solving both potential children using a conquer algorithm. The candidate that has the largest score is chosen to be the branching constraint. However, the score can be difficult to compute. For instance, when the score is based on dual bound improvement produced by the branching constraint which is time-consuming to evaluate in the context of column generation Therefore, one can let the branching algorithm quickly estimate the score of each candidate and retain the most promising branching candidates. This is called a **phase**. The goal is to first evaluate a large number of candidates with a very fast conquer algorithm and retain a certain number of promising ones. Then, over the phases, it evaluates the improvement with a more precise conquer algorithm and restricts the number of retained candidates until only one is left. ### Strong Branching API ```@docs Branching.get_units_to_restore_for_conquer Branching.get_phases Branching.get_score Branching.get_conquer Branching.get_max_nb_candidates ``` The following methods are part of the API but have a default implementation. We advise you to not change them. ```@docs Branching.perform_branching_phase! Branching.eval_candidate! Branching.eval_child_of_candidate! ``` #### Score ```@docs Branching.AbstractBranchingScore Branching.compute_score ```

Coluna

https://github.com/atoptima/Coluna.jl.git

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0.8.1

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```@meta CurrentModule = Coluna ``` # Column generation Coluna provides an interface and generic functions to implement a multi-stage column generation algorithm together with a default implementation of this algorithm. In this section, we are first going to present the generic functions, the implementation with some theory backgrounds and then give the references of the interface. You can find the generic functions and the interface in the `ColGen` submodule and the default implementation in the `Algorithm` submodule at `src/Algorithm/colgen`. ## Context The `ColGen` submodule provides an interface and generic functions to implement a column generation algorithm. The implementation depends on an object called `context`. ```@docs Coluna.ColGen.AbstractColGenContext ``` Coluna provides two types of context: ```@docs Coluna.Algorithm.ColGenContext Coluna.Algorithm.ColGenPrinterContext ``` ## Generic functions Generic functions are the core of the column generation algorithm. There are three generic functions: ```@docs Coluna.ColGen.run! ``` See the [main loop](#Main-loop) section for more details. ```@docs Coluna.ColGen.run_colgen_phase! ``` See the [phase loop](#Phase-loop) section for more details. ```@docs Coluna.ColGen.run_colgen_iteration! ``` See the [column generation iteration](#Column-generation-iteration) section for more details. They are independent of any other submodule of Coluna. You can use them to implement your own column generation algorithm. ## Reformulation The default implementation works with a reformulated problem contained in `MathProg.Reformulation` where master and subproblems are `MathProg.Formulation` objects. The master has the following form: ```math \begin{aligned} \min \quad& \sum_{k \in K} c^k \lambda^k+\bar{c} y & \\ \text{s.t.} \quad& \sum_{k \in K} A^k \lambda^k+\bar{A} y \geq a & (1)\\ & l_k \leq \mathbf{1} \lambda^k \leq u_k & (2) \\ & \bar{l} \leq y \leq \bar{u} & (3) \end{aligned} ``` where $\lambda$ are the master columns, $y$ are the pure master variables, constraints (1) are the linking constraints, constraints (2) are the convexity constraints that depend on $l_k$ and $u_k$ (e.g. the lower and upper multiplicity of the subproblem $k$ respectively), and constraints (3) are the bounds on the pure master variables. The subproblems have the following form: ```math \begin{aligned} \min \quad& cx + 0z \\ \text{s.t.} \quad& Bx \geq b \\ & 1 \leq z \leq 1 \end{aligned} ``` where $x$ are the subproblem variables, $z$ is a setup variable that always takes the value one in a solution to the subproblem. The coefficients of the columns in constraints (1) and (2) of the master are computed using representative variables of the subproblems. You can read this section (TODO Natacha) to understand how we map the subproblem solutions into master columns. **References**: ```@docs Coluna.ColGen.get_reform Coluna.ColGen.get_master Coluna.ColGen.get_pricing_subprobs Coluna.ColGen.is_minimization ``` ## Main loop This is a description of how the `Coluna.ColGen.run!` generic function behaves in the default implementation. The main loop stops when the `Coluna.ColGen.stop_colgen` method returns `true`. This is the case when one of the following conditions holds: - the master or a pricing subproblem is infeasible - the time limit is reached - the maximum number of iterations is reached Otherwise, the main loop runs until there is no more phase or stage to execute. The method returns: ```@docs Coluna.Algorithm.ColGenOutput ``` **References**: ```@docs Coluna.ColGen.stop_colgen Coluna.ColGen.setup_reformulation! Coluna.ColGen.setup_context! Coluna.ColGen.AbstractColGenOutput Coluna.ColGen.colgen_output_type Coluna.ColGen.new_output ``` ## Phase loop This is a description of how the `Coluna.ColGen.run_colgen_phase!` generic function behaves in the default implementation. This function is responsible for maintaining the incumbent dual bound and the incumbent master IP primal solution. The phase loop stops when the `Coluna.ColGen.stop_colgen_phase` method returns `true`. This is the case when one of the following conditions holds: - the maximum number of iterations is reached - the time limit is reached - the master is infeasible - the master is unbounded - a pricing subproblem is infeasible - a pricing subproblem is unbounded - there is no new column generated at the last iteration - there is a new constraint or valid inequality in the master - the incumbent dual bound and the primal master LP solution value converged The method returns: ```@docs Coluna.Algorithm.ColGenPhaseOutput ``` **References**: ```@docs Coluna.ColGen.stop_colgen_phase Coluna.ColGen.before_colgen_iteration Coluna.ColGen.after_colgen_iteration Coluna.ColGen.is_better_dual_bound ``` ### Phase iterator In the first iterations, the restricted master LP contains a few columns and may be infeasible. To prevent this, we introduced artificial variables $v$ and we activate/deactivate these variables depending on whether we want to prove the infeasibility of the master LP or find the optimal LP solution. The default implementation provides three phases: ```@docs Coluna.Algorithm.ColGenPhase0 Coluna.Algorithm.ColGenPhase1 Coluna.Algorithm.ColGenPhase2 ``` Column generation always starts with Phase 0. The default implementation of the phase iterator belongs to the following type: ```@docs Coluna.Algorithm.ColunaColGenPhaseIterator ``` Transitions between the phases depend on four conditions: - (A) the presence of artificial variables in the master LP solution - (B) the generation of new essential constraints (may happen when a new master IP solution is found) - (C) the current stage is exact - (D) column generation converged Transitions are the following: ```mermaid flowchart TB; id1(Phase 0) id2(Phase 1) id3(Phase 2) id4(end) id5(error) id1 --A & !B & C--> id2 id1 --!A & !B & C & D--> id4 id1 -- otherwise --> id1 id2 --!A & !B--> id3 id2 --A & C & D--> id4 id2 -- otherwise --> id2 id3 -- !B & C & D --> id4 id3 -- otherwise --> id3 id3 -- B --> id2 id3 -- A --> id5 style id5 stroke:#f66 ``` **References**: ```@docs Coluna.ColGen.AbstractColGenPhase Coluna.ColGen.AbstractColGenPhaseIterator Coluna.ColGen.new_phase_iterator Coluna.ColGen.initial_phase Coluna.ColGen.decrease_stage Coluna.ColGen.next_phase ``` ### Phase output ```@docs Coluna.ColGen.AbstractColGenPhaseOutput Coluna.ColGen.colgen_phase_output_type Coluna.ColGen.new_phase_output ``` ## Stages A stage is a set of consecutive iterations in which we use a given pricing solver. The aim is to speed up the resolution of the pricing problem by first using an approximate but fast pricing algorithm and then switching to increasingly less heuristic algorithms until the last stage where an exact solver is used. and an exact solver at the last stage. Given a pricing solver, when the column generation does not progress anymore or the pricing solver does not return any new column, the default implementation switch to a more exact pricing solver. Stages are created using the `stages_pricing_solver_ids` of the `ColumnGenerationAlgorithm` parameter object. The default implementation implements the interface around the following object: ```@docs Coluna.Algorithm.ColGenStageIterator ``` **References**: ```@docs Coluna.ColGen.AbstractColGenStage Coluna.ColGen.AbstractColGenStageIterator Coluna.ColGen.new_stage_iterator Coluna.ColGen.initial_stage Coluna.ColGen.next_stage Coluna.ColGen.get_pricing_subprob_optimizer Coluna.ColGen.stage_id Coluna.ColGen.is_exact_stage ``` ## Column generation iteration This is a description of how the `Coluna.ColGen.run_colgen_iteration!` generic function behaves in the default implementation. These are the main steps of a column generation iteration without stabilization. Click on the step to go to the relevant section. ```mermaid flowchart TB; id1(Optimize master LP) id2{{Solution to master LP is integer?}} id3(Update incumbent primal solution if better than current one) id4(Compute reduced cost of subproblem variables) id5{{Subproblem iterator}} id6(Optimize pricing subproblem) id7(Push subproblem solution into set) id8(Compute dual bound) id9(Insert columns) id10(Iteration output) id1 --> id2 id2 --yes--> id3 id2 --no--> id4 id3 --> id4 id4 --> id5 id5 --subproblem--> id6 id6 --> id7 id7 --> id5 id5 --end--> id8 id8 --> id9 id9 --> id10 click id1 href "#Optimize-master-LP" "Link to doc" click id2 href "#Check-integrality-of-the-master-LP-solution" "Link to doc" click id3 href "#Update-incumbent-primal-solution" "Link to doc" click id4 href "#Reduced-costs-calculation" "Link to doc" click id5 href "#Pricing-subproblem-iterator" "Link to doc" click id6 href "#Pricing-subproblem-optimization" "Link to doc" click id7 href "#Set-of-generated-columns" "Link to doc" click id8 href "#Dual-bound-calculation" "Link to doc" click id9 href "#Columns-insertion" "Link to doc" click id10 href "#Iteration-output" "Link to doc" ``` #### Optimize master LP At each iteration, the algorithm requires a dual solution to the master LP to compute the reduced cost of subproblem variables. The default implementation optimizes the master with an LP solver through MathOptInterface. It returns a primal and a dual solution. In the default implementation, the master LP output is in the following data structure: ```@docs Coluna.Algorithm.ColGenMasterResult ``` **References**: ```@docs Coluna.ColGen.optimize_master_lp_problem! ``` You can see the additional methods to implement in the [result data structures](#Result-data-structures) section. Go back to the [column generation iteration overview](#Column-generation-iteration). #### Check the integrality of the master LP solution The algorithm checks the integrality of the primal solution to the master LP to improve the global primal bound of the branch-cut-price algorithm. By default, the integrality check is done using the `MathProg.proj_cols_is_integer` method. It implements the mapping procedure from the paper "F. Vanderbeck, Branching in branch-and-price: a generic scheme, Math.Prog. (2011)". Basically, it sorts the column used in the master LP primal solution in lexicographic order. It assigns a weight to each column equal to the value of the column in the master LP solution. It then forms columns of weight one by accumulating the columns of the fractional solution. If columns are integral, the solution is integral. This is a heuristic procedure so it can miss some integer solutions. In the case the pricing subproblems are solved by a callback, and some subproblem integer variables are "hidden" from _Coluna_ (values of these variables are usually stored in `CustomData` associated with the pricing problem solution), the mapping procedure may not be valid. In this case, the integrality should be checked in the "strict" way, i.e., by explicitly verifying that all columns are integer. Integrality check procedure is set using parameter `strict_integrality_check` (`false` by default) of the `ColumnGenerationAlgorithm`. If the solution is integral, the essential cut callback is called to make sure it is feasible. **References**: ```@docs Coluna.ColGen.check_primal_ip_feasibility! Coluna.ColGen.is_better_primal_sol ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Update incumbent primal solution If the solution to master LP is integral and better than the current best one, we need to update the incumbent. This solution is then used by the tree-search algorithm in the bounding mechanism that prunes the nodes. **References**: ```@docs Coluna.ColGen.update_inc_primal_sol! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Reduced costs calculation Reduced costs calculation is written as a math operation in the `run_colgen_iteration!` generic function. As a consequence, the dual solution to the master LP and the implementation of the two following methods must return data structures that support math operations. To speed up this operation, we cache data in the following data structure: ```@docs Coluna.Algorithm.ReducedCostsCalculationHelper ``` Reduced costs calculation also requires the implementation of the two following methods: ```@docs Coluna.ColGen.update_master_constrs_dual_vals! Coluna.ColGen.update_reduced_costs! Coluna.ColGen.get_subprob_var_orig_costs Coluna.ColGen.get_subprob_var_coef_matrix Coluna.ColGen.update_sp_vars_red_costs! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Pricing subproblem iterator The pricing strategy is basically an iterator used to iterate over the pricing subproblems to optimize at each iteration of the column generation. The context can serve as a memory of the pricing strategy to change the way we iterate over subproblems between each column generation iteration. The default implementation iterates over all subproblems. Here are the references for the interface: ```@docs Coluna.ColGen.AbstractPricingStrategy Coluna.ColGen.get_pricing_strategy Coluna.ColGen.pricing_strategy_iterate ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Pricing subproblem optimization At each iteration, the algorithm requires primal solutions to the pricing subproblems. The generic function supports multi-column generation so you can return any number of solutions. The default implementation supports optimization of the pricing subproblems using a MILP solver or a pricing callback. Non-robust valid inequalities are not supported by MILP solvers as they change the structure of the subproblems. When using a pricing callback, you must be aware of how Coluna calculates the reduced cost of a column: The reduced cost of a column is split into three contributions: - the contribution of the subproblem variables that is the primal solution cost given the reduced cost of subproblem variables - the contribution of the non-robust constraints (i.e. master constraints that cannot be expressed using subproblem variables except the convexity constraint) that is not supported by MILP solver but that you must take into account in the pricing callback - the contribution of the master convexity constraint that is automatically taken into account by Coluna once the primal solution is returned. Therefore, when you use a pricing callback, you must not discard some columns based only on the primal solution cost because you don't know the contribution of the convexity constraint. ```@docs Coluna.Algorithm.GeneratedColumn Coluna.Algorithm.ColGenPricingResult ``` **References**: ```@docs Coluna.ColGen.optimize_pricing_problem! ``` You can see the additional methods to implement in the [result data structures](#Result-data-structures) section. Go back to the [column generation iteration overview](#Column-generation-iteration). #### Set of generated columns You can define your data structure to manage the columns generated at a given iteration. Columns are inserted after the optimization of all pricing subproblems to allow the parallelization of the latter. In the default implementation, we use the following data structure: ```@docs Coluna.Algorithm.ColumnsSet Coluna.Algorithm.SubprobPrimalSolsSet ``` In the default implementation, `push_in_set!` is responsible for checking if the column has improving reduced cost. Only columns with improving reduced cost are inserted in the set. The `push_in_set!` is also responsible to insert he best primal solution to each pricing problem into the `SubprobPrimalSolsSet` object. **References**: ```@docs Coluna.ColGen.set_of_columns Coluna.ColGen.push_in_set! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Dual bound calculation In the default implementation, given a vector $\pi \geq 0$ of dual values to the master constraints (1), the Lagrangian dual function is given by: ```math L(\pi) = \pi a + \sum_{k \in K} \max_{l_k \leq \mathbf{1} \lambda^k \leq u^k} (c^k - \pi A^k)\lambda^k + \max_{ \bar{l} \leq y \leq \bar{u}} (\bar{c} - \pi \bar{A})y ``` Let: - element $z_k(\pi) \leq \min_i (c^k_i - \pi A^k_i)$ be a lower bound on the solution value of the pricing problem - element $\bar{z}_j(\pi) = \bar{c} - \pi \bar{A}$ be the reduced cost of pure master variable $y_j$ Then, the Lagrangian dual function can be lower bounded by: ```math L(\pi) \geq \pi a + \sum_{k \in K} \max\{ z_k(\pi) \cdot l_k, z_k(\pi) \cdot u_k \} + \sum_{j \in J} \max\{ \bar{z}_j(\pi) \cdot \bar{l}_j, \bar{z}_j(\pi) \cdot \bar{u}_j\} ``` More precisely: - the first term is the contribution of the master obtained by removing the contribution of the convexity constraints (computed by `ColGen.Algorithm._convexity_contrib`), and the pure master variables (but you should see the third term) from the master LP solution value - the second term is the contribution of the subproblem variables which is the sum of the best solution value of each pricing subproblem multiplied by the lower and upper multiplicity of the subproblem depending on whether the reduced cost is negative or positive (this is computed by `ColGen.Algorithm._subprob_contrib`) - the third term is the contribution of the pure master variables which is taken into account by master LP value. Therefore, we can compute the Lagrangian dual bound as follows: ```julia master_lp_obj_val - convexity_contrib + sp_contrib ``` However, if the smoothing stabilization is active, we compute the dual bound at the sep-point. As a consequence, we can't use the master LP value because it corresponds to the dual solution at the out-point. We therefore need to compute the lagrangian dual bound by strictly applying the above formula. **References**: ```@docs Coluna.ColGen.compute_sp_init_pb Coluna.ColGen.compute_sp_init_db Coluna.ColGen.compute_dual_bound ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Columns insertion The default implementation inserts into the master all the columns stored in the `ColumnsSet` object. **Reference**: ```@docs Coluna.ColGen.insert_columns! ``` Go back to the [column generation iteration overview](#Column-generation-iteration). #### Iteration output ```@docs Coluna.Algorithm.ColGenIterationOutput ``` **References**: ```@docs Coluna.ColGen.AbstractColGenIterationOutput Coluna.ColGen.colgen_iteration_output_type Coluna.ColGen.new_iteration_output ``` Go back to the [column generation iteration overview](#Column-generation-iteration). ### Getters for Result data structures | Method name | Master | Pricing | | ---------------- | ------ | ---------- | | `is_unbounded` | X | X | | `is_infeasible` | X | X | | `get_primal_sol` | X | | | `get_primal_sols`| | X | | `get_dual_sol` | X | | | `get_obj_val` | X | | | `get_primal_bound` | | X | | `get_dual_bound` | | X | **References**: ```@docs Coluna.ColGen.is_unbounded Coluna.ColGen.is_infeasible Coluna.ColGen.get_primal_sol Coluna.ColGen.get_primal_sols Coluna.ColGen.get_dual_sol Coluna.ColGen.get_obj_val Coluna.ColGen.get_primal_bound ``` Go back to the [column generation iteration overview](#Column-generation-iteration). ### Getters for Output data structures | Method name | ColGen | Phase | Iteration | | ---------------- | ------ | ----- | --------- | | `get_nb_new_cols` | | | X | | `get_master_ip_primal_sol` | X | X | X | | `get_master_lp_primal_sol` | X | | | | `get_master_dual_sol` | X | | | | `get_dual_bound` | X | | X | | `get_master_lp_primal_bound` | X | | | | `is_infeasible` | X | | | **References**: ```@docs Coluna.ColGen.get_nb_new_cols Coluna.ColGen.get_master_ip_primal_sol Coluna.ColGen.get_master_lp_primal_sol Coluna.ColGen.get_master_dual_sol Coluna.ColGen.get_master_lp_primal_bound ``` Go back to the [column generation iteration overview](#Column-generation-iteration). ## Stabilization Coluna provides a default implementation of the smoothing stabilization with a self-adjusted $\alpha$ parameter, $0 \leq \alpha < 1$. At each iteration of the column generation algorithm, instead of generating columns for the dual solution to the master LP, we generate columns for a perturbed dual solution defined as follows: ```math \pi^{\text{sep}} = \alpha \pi^{\text{in}} + (1-\alpha) \pi^{\text{out}} ``` where $\pi^{\text{in}}$ is the dual solution that gives the best Lagrangian dual bound so far (also called stabilization center) and $\pi^{\text{out}}$ is the dual solution to the master LP at the current iteration. This solution is returned by the default implementation of `Coluna.ColGen.get_stab_dual_sol`. Some elements of the column generation change when using stabilization. - Columns are generated using the smoothed dual solution $\pi^{\text{sep}}$ but we still need to compute the reduced cost of the columns using the original dual solution $\pi^{\text{out}}$. - The dual bound is computed using the smoothed dual solution $\pi^{\text{sep}}$. - The pseudo bound is computed using the smoothed dual solution $\pi^{\text{sep}}$. - The smoothed dual bound can result in the generation of no improving columns. This is called a **misprice**. In that case, we need to move away from the stabilization center $\pi^{\text{in}}$ by decreasing $\alpha$. When using self-adjusted stabilization, the smoothing coefficient $\alpha$ is adjusted to make the smoothed dual solution $\pi^{\text{sep}}$ closer to the best possible dual solution on the line between $\pi^{\text{in}}$ and $\pi^{\text{out}}$ (i.e. where the subgradient of the current primal solution is perpendicular to the latter line). To compute the subgradient, we use the following data structure: ```@docs Coluna.Algorithm.SubgradientCalculationHelper ``` **References**: ```@docs Coluna.ColGen.setup_stabilization! Coluna.ColGen.update_stabilization_after_master_optim! Coluna.ColGen.get_stab_dual_sol Coluna.ColGen.check_misprice Coluna.ColGen.update_stabilization_after_pricing_optim! Coluna.ColGen.update_stabilization_after_misprice! Coluna.ColGen.update_stabilization_after_iter! Coluna.ColGen.get_output_str ```

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Presolve Currently, the presolve algorithm supports only the Dantzig-Wolfe decomposition. The presolve algorithm operates on matrix representations of the formulation. It requires two representations of the master formulation: - the restricted master that contains master column variables, pure master variables and artificial variables; - the representative master that contains subproblem representative variables and pure master variables; and the representation of the pricing subproblems. The current presolve operations available are the following (taxonomy of Achterberg et al. 2016): - model cleanup & removal of redundant constraints - bound strengthening - removal of fixed variables ## Partial solution The presolve algorithm has the responsibility to define and fix a partial solution when it exists. When a variable $x$ has a value $\bar{x} > 0$ (resp. $\bar{x} < 0$) in the partial solution, it means that $x$ has a lower (resp. upper) bounds $\bar{x}$ that will definitely be part of the solution at the current branch-and-bound node and its successors. In other words, the partial solution describes a minimal distance of the variables from zero in the all the solutions to a problem at a given branch-and-bound node. It always restricts the domain of the variables (i.e. increase distance from zero). The only way to relax the domains is to backtrack to an ancestor of the current branch-and-bound node (i.e. go back to a previous partial solution). ### Augmenting the partial solution Consider a _local partial solution_ $(\bar{x}^{\rm pure}, \bar{\lambda})$ (where $\bar{x}^{\rm pure}$ is the vector of values for pure master variables, and $\bar{\lambda}$ is the vector of values for master columns), which should be added to the _global partial solution_ $(\bar{y}^{\rm pure}, \bar{\theta})$: 1. augment the global partial solution: $(\bar{y}^{\rm pure}, \bar{\theta})\leftarrow(\bar{x}^{\rm pure}+\bar{y}^{\rm pure}, \bar{\lambda}+\bar{\theta})$. 2. update the right-hand side values of the master constraints: ${\rm rhs}_i\leftarrow {\rm rhs}_i - {A}^{\rm pure}\cdot\bar{x}^{\rm pure} - {A}^{\rm col}\cdot\bar{ \lambda}$, where ${A}^{\rm pure}$ is the matrix of coefficients of pure master constraints and ${A}^{\rm col}$ is the matrix of coefficients of master columns. 3. update subproblem multiplicities $U_k\leftarrow U_k - \sum_{q\in Q_k}\bar\lambda_q$, and $L_k\leftarrow \max\left\{0,\; L_k - \sum_{q\in Q_k}\bar\lambda_q\right\}$, where $Q_k$ is the set of indices of columns associated with solutions from subproblem $k$. 4. update the bounds of pure master variables and representative master variables using the representative local partial solution: $\bar{x}^{\rm repr} = \sum_{q\in Q}{s_q}\cdot \bar\lambda_q$, where $Q$ is the total number of columns, and ${s_q}$ is the subproblem solution associated with column $\lambda_q$. ### Updating bounds of pure & representative master variables Consider a pure master variable $x^{\rm pure}_j$ with $\bar{x}^{\rm pure}_j\neq 0$ and bounds $[lb_j,ub_j]$ before augmenting the partial solution. If $\bar{x}^{\rm pure}_j > 0$, then we have $lb_j\leftarrow 0$, $ub_j\leftarrow ub_j - \bar{x}^{\rm pure}_j$. If $\bar{x}^{\rm pure}_j < 0$, then we have $lb_j\leftarrow lb_j - \bar{x}^{\rm pure}_j$, $ub_j\leftarrow 0$. Consider a representative master variable $x^{\rm repr}_j$ with bounds $[lb^g_j, ub^g_j]$ before augmenting the partial solution. Assume that $x^{\rm repr}_j$ represents exactly one variable $x^k_j$ in subproblem $k$ with bounds $[lb^l_j, ub^l_j]$ before augmenting the partial solution. _This assumption should be verified before augmenting the partial solution!_ For the clarity of presentation, we omit index $j$ for the remainder of this section. After augmenting the partial solution, the following inequalities should be satisfied: $$ lb^g - \bar{x}^{\rm repr}\leq x^{\rm repr} \leq ub^g - \bar{x}^{\rm repr}.$$ At the same time, we should have $$\min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\leq x^{\rm repr}\leq \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}$$ Thus, the following update should be done $$ lb^g\leftarrow \max\left\{lb^g - \bar{x}^{\rm repr},\; \min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\right\}$$ $$ ub^g\leftarrow \min\left\{ub^g - \bar{x}^{\rm repr},\; \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}\right\}$$ > _**Example 1:**_ $0\leq x^k\leq 3$, $0\leq x^{\rm repr}\leq 6$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=2$. Then after augmenting the partial solution, we have > $$ \max\left\{-2,\; 0\right\}\leq x^{\rm repr} \leq \min\left\{4,\; 3\right\} \Rightarrow 0 \leq x^{\rm repr} \leq 3$$ > _**Example 2:**_ $0\leq x^k\leq 5$, $3\leq x^{\rm repr}\leq 6$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=2$. Then after augmenting the partial solution, we have > $$ \max\left\{1,\; 0\right\}\leq x'_{\rm repr} \leq \min\left\{4,\; 5\right\} \Rightarrow 1 \leq x'_{\rm repr} \leq 4$$ > _**Example 3:**_ $-1\leq x^k\leq 4$, $-2\leq x^{\rm repr}\leq 2$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=-1$. Then after augmenting the partial solution, we have > $$ \max\left\{-1,\; -1\right\}\leq x^{\rm repr} \leq \min\left\{3,\; 4\right\} \Rightarrow -1 \leq x^{\rm repr} \leq 3$$

Coluna

https://github.com/atoptima/Coluna.jl.git

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```@meta EditURL = "<unknown>/src/api/storage.jl" ``` # Storage API ```@meta CurrentModule = Coluna ``` ## API To summarize from a developer's point of view, there is a one-to-one correspondence between storage unit types and record types. This correspondence is implemented by methods `record_type(StorageUnitType)` and `storage_unit_type(RecordType)`. The developer must also implement methods `storage_unit(StorageUnitType)` and `record(RecordType, id, model, storage_unit)` that must call constructors of the custom storage unit and one of its associated records. Arguments of `record` allow the developer to record the state of entities from both the storage unit and the model. At last, he must implement `restore_from_record!(storage_unit, model, record)` to restore the state of the entities represented by the storage unit. Entities can be in the storage unit, the model, or both of them. ```@docs ColunaBase.record_type ColunaBase.storage_unit_type ColunaBase.storage_unit ColunaBase.record ColunaBase.restore_from_record! ``` --- *This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Tree search API !!! danger Update needed. Now, we define the two concepts we'll use in the tree search algorithms: the *node* and the *search space*. The third concept is the *explore strategy* and implemented in Coluna. Every tree search algorithm must be associated to a search space. ## Implementing tree search interface First, we indicate the type of search space used by our algorithms. Note that the type of the search space can depends on the configuration of the algorithm. So there is a 1-to-n relation between tree search algorithm configurations and search space. because one search space can be used by several tree search algorithms configuration. Now, we implement the method that calls the constructor of a search space. The type of the search space is known from above method. A search space may receive information from the tree-search algorithm. The `model`, and `input` arguments are the same than those received by the tree search algorithm. We implement the method that returns the root node. The definition of the root node depends on the search space. Then, we implement the method that converts the branching rules into nodes for the tree search algorithm. We implement the `node_change` method to update the search space called by the tree search algorithm just after it finishes to evaluate a node and chooses the next one. Be careful, this method is not called after the evaluation of a node when there is no more unevaluated nodes (i.e. tree exploration is finished). There are two ways to store the state of a formulation at a given node. We can distribute information across the nodes or store the whole state at each node. We follow the second way (so we don't need `previous`). Method `after_conquer` is a callback to do some operations after the conquer of a node and before the divide. Here, we update the best solution found after the conquer algorithm. We implement one method for each search space. We implement getters to retrieve the input from the search space and the node. The input is passed to the conquer and the divide algorithms. At last, we implement methods that will return the output of the tree search algorithms. We return the cost of the best solution found. We write one method for each search space. ## API ### Search space ```@docs Coluna.TreeSearch.AbstractSearchSpace Coluna.TreeSearch.search_space_type Coluna.TreeSearch.new_space ``` ### Node ```@docs Coluna.TreeSearch.AbstractNode Coluna.TreeSearch.new_root Coluna.TreeSearch.get_parent Coluna.TreeSearch.get_priority ``` Additional methods needed for Coluna's algorithms: ```@docs Coluna.TreeSearch.get_opt_state Coluna.TreeSearch.get_records Coluna.TreeSearch.get_branch_description Coluna.TreeSearch.isroot ``` ### Tree search algorithm ```@docs Coluna.TreeSearch.AbstractExploreStrategy Coluna.TreeSearch.tree_search Coluna.TreeSearch.children Coluna.TreeSearch.stop Coluna.TreeSearch.tree_search_output ``` ### Tree search algorithm for Coluna ```@docs Coluna.Algorithm.AbstractColunaSearchSpace ``` The `children` method has a specific implementation for `AbstractColunaSearchSpace`` that involves following methods: ```@docs Coluna.Algorithm.get_previous Coluna.Algorithm.set_previous! Coluna.Algorithm.node_change! Coluna.Algorithm.get_divide Coluna.Algorithm.get_reformulation Coluna.Algorithm.get_input Coluna.Algorithm.after_conquer! Coluna.Algorithm.new_children ``` --- *This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*

Coluna

https://github.com/atoptima/Coluna.jl.git

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```@meta CurrentModule = Coluna ``` # Built-in Algorithms ## Branch-and-Bound Branch-and-Bound algorithm aims to find an optimal solution of a MIP by successive divisions of the search space. An introduction to the Branch-and-Bound algorithm can be found [here](https://en.wikipedia.org/wiki/Branch_and_bound). Coluna provides a generic Branch-and-Bound algorithm whose three main elements can be easily modified: ```@docs Algorithm.TreeSearchAlgorithm ``` Conquer, divide algorithms and the explore strategy available with the `TreeSearchAlgorithm` are listed in the following mind map. ```mermaid mindmap TreeSearchAlgorithm (conquer) BendersConquer ColCutGenConquer RestrMasterLpConquer (divide) NoBranching ClassicBranching StrongBranching (explore) DepthFirstStrategy BestDualBoundStrategy ``` ## Conquer algorithms ```@docs Algorithm.BendersConquer Algorithm.ColCutGenConquer Algorithm.RestrMasterLpConquer ``` ## Divide algorithms ```@docs Algorithm.NoBranching Algorithm.ClassicBranching ``` Strong branching is the main algorithm that we provide and it is the default implementation of the `Branching` submodule. You can have more information about the algorithm by reading the `Branching` submodule documentation. ```@docs Algorithm.StrongBranching ``` All the possible algorithms that can be used within the strong branching are listed in the following mind map. ```mermaid mindmap StrongBranching (phases) (conquer) BendersConquer ColCutGenConquer RestrMasterLpConquer (score) ProductScore TreeDepthScore (rules) SingleVarBranchingRule (selection_criterion) FirstFoundCriterion MostFractionalCriterion ``` ## Explore strategies ```@docs TreeSearch.DepthFirstStrategy TreeSearch.BestDualBoundStrategy ``` ## Cut generation algorithms ```@docs Algorithm.BendersCutGeneration ``` ```@docs Algorithm.CutCallbacks ``` ## Column generation algorithms ```@docs Algorithm.ColumnGeneration ``` ## External call to optimize a linear program ```@docs Algorithm.SolveLpForm ``` ## External call to optimize a mixed-integer program / combinatorial problem ```@docs Algorithm.SolveIpForm Algorithm.MoiOptimize Algorithm.UserOptimize Algorithm.CustomOptimize ```

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Setup decomposition with BlockDecomposition BlockDecomposition allows the user to perform two types of decomposition using [`BlockDecomposition.@dantzig_wolfe_decomposition`](@ref) and [`BlockDecomposition.@benders_decomposition`](@ref). For both decompositions, the index-set of the subproblems is declared through an [`BlockDecomposition.@axis`](@ref). It returns an array. Each value of the array is a subproblem index wrapped into a `BlockDecomposition.AxisId`. Each time BlockDecomposition finds an `AxisId` in the indices of a variable and a constraint, it knows to which subproblem the variable or the constraint belongs. The macro creates a decomposition tree where the root is the master and the depth is the number of nested decompositions. A classic Dantzig-Wolfe or Benders decomposition produces a decomposition tree of depth 1. At the moment, nested decomposition is not supported. You can get the subproblem membership of all variables and constraints using the method [`BlockDecomposition.annotation`](@ref). BlockDecomposition does not change the JuMP model. It decorates the model with additional information. All this information is stored in the `ext` field of the JuMP model. ```@meta CurrentModule = BlockDecomposition ``` ## Errors and warnings ```@docs MasterVarInDwSp VarsOfSameDwSpInMaster ``` ## References ```@docs BlockModel ``` These are the methods to decompose a JuMP model : ```@docs @axis @benders_decomposition @dantzig_wolfe_decomposition ``` These are the methods to set additional information to the decomposition (multiplicity and optimizers) : ```@docs getmaster getsubproblems specify! ``` This method helps you to check your decomposition : ```@docs annotation ``` ```@meta CurrentModule = nothing ```

Coluna

https://github.com/atoptima/Coluna.jl.git

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# User-defined Callbacks Callbacks are functions defined by the user that allow him to take over part of the default conquer algorithm. The more classical callbacks in Branch-and-Cut and Branch-and-Price solvers are: - Pricing callback (only in Branch-and-Price solvers) that takes over the procedure to determine whether the current master LP solution is optimum or produces an entering variable with negative reduced cost by solving subproblems - Separation callback that takes over the procedure to determine whether the current master LP solution is feasible or produces a valid problem constraint that is violated - Branching callback that takes over the procedure to determine whether the current master LP solution is integer or produces a valid branching disjunctive constraint that rules out the current fractional solution. !!! note You can't change the original formulation in a callback because Coluna does not propagate the changes into the reformulation and does not check if the solutions found are still feasible. ## Pricing callbacks Pricing callbacks let you define how to solve the subproblems of a Dantzig-Wolfe decomposition to generate a new entering column in the master program. This callback is useful when you know an efficient algorithm to solve the subproblems, i.e. an algorithm better than solving the subproblem with a MIP solver. See the example in the [tutorial section](@ref tuto_pricing_callback). ### Errors and Warnings ```@docs Algorithm.IncorrectPricingDualBound Algorithm.MissingPricingDualBound Algorithm.MultiplePricingDualBounds ``` ## Separation callbacks Separation callbacks let you define how to separate cuts or constraints. ### Facultative & essential cuts (user cut & lazy constraint) This callback allows you to add cuts to the master problem. The cuts must be expressed in terms of the original variables. Then, Coluna expresses them over the master variables. You can find an example of [essential cut separation](https://jump.dev/JuMP.jl/stable/tutorials/Mixed-integer%20linear%20programs/callbacks/#Lazy-constraints) and [facultative cut separation](https://jump.dev/JuMP.jl/stable/tutorials/Mixed-integer%20linear%20programs/callbacks/#User-cut) in the JuMP documentation.

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Coluna Configuration todo ## Raw Parameters todo ### `params` ```@meta CurrentModule = Coluna ``` ```@docs Params ``` ```@meta CurrentModule = nothing ``` ### `default_optimizer` todo ## Other Supported Parameters ### From BlockDecomposition ```@meta CurrentModule = BlockDecomposition ``` ```@docs objectiveprimalbound! objectivedualbound! ``` ```@meta CurrentModule = nothing ``` ### From MathOptInterface todo

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Dantzig-Wolfe and Benders decompositions Coluna is a framework to optimize mixed-integer programs that you can decompose. In other words, if you remove the linking constraints or linking variables from your program, you'll get sets of constraints (blocks) that you can solve independently. Decompositions are typically used on programs whose constraints or variables can be divided into a set of "easy" constraints (respectively easy variables) and a set of "hard" constraints (respectively hard variables). Decomposing on constraints leads to Dantzig-Wolfe transformation while decomposing on variables leads to the Benders transformation. Both of these decompositions are implemented in Coluna. ## Dantzig-Wolfe ### Original formulation Let's consider the following coefficient matrix that has a block diagonal structure in gray and some linking constraints in blue : ![Dantzig-Wolfe decomposition](../assets/img/dwdec.png) You penalize the violation of the linking constraints in the objective function. You can then solve the blocks independently. The Dantzig-Wolfe reformulation gives rise to a master problem with an exponential number of variables. Coluna dynamically generates these variables by solving the subproblems. It's the column generation algorithm. Let's consider the following original formulation in which we partition variables into two vectors $x_1$ and $x_2$ : ```math \begin{aligned} \min \quad& c_1' x_1 + c_2' x_2 & \\ \text{s.t.} \quad& A_1 x_1 + A_2 x_2 \geq b & (1)\\ & D_1 x_1 \quad \quad \quad \geq d_1 & (2) \\ & \quad \quad \quad D_2 x_2 \geq d_2 & (3) \\ \end{aligned} ``` - variables $x_1$ and $x_2$ are the original variables of the problem (duty: `OriginalVar`) - constraints $(1)$ are the linking constraints (duty: `OriginalConstr`) - constraints $(2)$ shapes the first subproblem (duty: `OriginalConstr`) - constraints $(3)$ shapes the second subproblem (duty: `OriginalConstr`) ### Master When you apply a Dantzig-Wofe decomposition to this formulation, Coluna reformulates it into the following master problem : ```math \begin{aligned} \min \quad& \sum\limits_{q \in Q_1} c_1' \tilde{x_1}^q \lambda_q + \sum\limits_{q \in Q_2} c_2' \tilde{x_2}^q \lambda_q + f'a \\ \text{s.t.} \quad& \sum\limits_{q \in Q_1} A_1 \tilde{x_1}^q \lambda_q + \sum\limits_{q \in Q_2} A_2 \tilde{x_2}^q \lambda_q + a \geq b & (1)\\ & L_1 \leq \sum\limits_{q \in Q_1} \tilde{z}_1\lambda_q \leq U_1 & (2)\\ & L_2 \leq \sum\limits_{q \in Q_2} \tilde{z}_2\lambda_q \leq U_2 & (3)\\ & \lambda_q \geq 0, \quad q \in Q_1 \cup Q_2 \end{aligned} ``` where: - set $Q_1$ is the index set of the solutions to the first subproblem - set $Q_2$ is the index set of the solutions to the second subproblem - set of the solutions to the first is $\{\tilde{x}^q_1\}_{q \in Q_1}$ (duty: ` MasterRepPricingVar`) - set of the solutions to the second subproblem is $\{\tilde{x}^q_2\}_{q \in Q_2}$ respectively (duty: ` MasterRepPricingVar`) - constraint $(1)$ is the reformulation of the linking constraints (duty: `MasterMixedConstr`) - constraint $(2)$ is the convexity constraint of the first subproblem and involves the lower $L_1$ and upper $U_1$ multiplicity of the subproblem (duty: `MasterConvexityConstr`) - constraint $(3)$ is the convexity constraint of the second subproblem and involves the lower $L_2$ and upper $U_2$ multiplicity of the subproblem (duty: `MasterConvexityConstr`) - variables $\tilde{z}_1$ and $\tilde{z}_2$ are representative of pricing setup variables in the master (always equal to $1$) (duty: `MasterRepPricingVar`) - variables $\lambda_q$ are the columns (duty: `MasterCol`) - variable $a$ is the artificial variable (duty: `MasterArtVar`) At the beginning of the column generation algorithm, the master formulation does not have any master columns. Therefore, the master may be infeasible. To prevent this, Coluna adds a local artificial variable $a$ specific to each constraint of the master and a global artificial variable. Costs $f$ of artificial and global artificial variables can be defined in [Coluna.Params](@ref). Lower and upper multiplicities of subproblems are $1$ by default. However, when some subproblems are identical (same coefficient matrix and right-hand side), you can avoid solving all of them at each iteration by defining only one subproblem and setting its multiplicity to the number of times it appears. See this [tutorial](@ref tuto_identical_sp) to get an example of Dantzig-Wolfe decomposition with identical subproblems. ### Pricing Subproblem Subproblems take the following form (here, it's the first subproblem): ```math \begin{aligned} \min \quad& \bar{c_1}' x_1 + z_1\\ \text{s.t.} \quad& D_1x_1 \geq d_1 & (1)\\ & \quad x_1 \geq 0 \end{aligned} ``` where: - vector $\bar{c}$ is the reduced cost of the subproblem variables computed by the column generation algorithm. - variables $x_1$ are the subproblem variables (duty: `DwSpPricingVar`) - constraint $(1)$ is the subproblem constraint (duty: `DwSpPureConstr`) - variable $z_1$ is the pricing setup variable (always equal to $1$) (duty: `DwSpSetupVar`) ## Benders ### Original formulation Let's consider the following coefficient matrix that has a block diagonal structure in gray and some linking variables in blue : ![Benders decomposition](../assets/img/bdec.png) The intuition behind Benders decomposition is that some hard problems can become much easier with some of their variables fixed. Benders aims to divide the variables of the problem into two "levels": the 1st level variables which, once fixed, make it easier to find a solution for the remaining variables, the so-called 2nd-level variables. The question is how to set the 1st level variables. Benders' theory proceeds by the successive generation of cuts: given a 1st-level solution, we ask the following questions: - Is the subproblem infeasible? If so, then the 1st-level solution is not correct and must be eliminated. A feasibility cut will be derived from the dual subproblem and added to the master. - Does the aggregation of the master and subproblem solutions give rise to an optimal solution to the problem? It depends on a criterion that can be computed. If it is the case, we are done, else, we derive an optimality cut from the dual subproblem and add it into the master. Formally, given an original MIP: ```math \begin{aligned} \min \quad& cx + fy & \\ \text{s.t.} \quad& Ax \geq a & (2) \\ & Ey \geq e & (3) \\ & Bx + Dy \geq d & (4)\\ & x, y \geq 0, ~ x \in \mathbb{Z}^n\\ \end{aligned} ``` where: - variables $x$ are the 1st-level variables (duty: `OriginalVar`) - variables $y$ are the 2nd-level variables (duty: `OriginalVar`) - constraints (2) are the 1st-level constraints (duty: `OriginalConstr`) - constraints (3) are the 2nd-level constraints (duty: `OriginalConstr`) - constraints (4) are the linking constraints (duty: `OriginalConstr`) ### Master When you apply a Benders decomposition to this formulation, Coluna reformulates it into the following master problem : ```math \begin{aligned} \min \quad& cx + \sum\limits_{k \in K}\eta_k & \\ \text{s.t.} \quad& Ax \geq a & (5)\\ & <~\text{benders cuts}~> & (6) \\ & \eta_k \in \mathbb{R} \quad \forall k \in K\\ \end{aligned} ``` where: - variables $x$ are the 1st-level variables (duty: `MasterBendFirstStageVar`) - variables $\eta$ are the second stage cost variables (duty: `MasterBendSecondStageCostVar`) - constraints (5) are the first-level constraints (duty: `MasterPureConstr`) - constraints (6) are the benders cuts (duty: ``) Note that the $\eta$ variables are free. ### Separation subproblem Here is the form of a given separation subproblem: ```math \begin{aligned} \min \quad& fy & \\ \text{s.t.} \quad& Dy \geq d - B\bar{x} & (7) \\ & Ey \geq e & (8) \\ & y \geq 0 \\ \end{aligned} ``` where: - variables $y$ are the 2nd-level variables (duty: `BendSpSepVar`) - values $\bar{x}$ are a solution to the master problem - constraints (7) are the linking constraints with the 1st-level variables fixed to $\bar{x}$ (duty: `BendSpTechnologicalConstr`) - constraints (8) are the 2nd-level constraints (duty: `BendSpPureConstr`) Note that in the special case where the master problem is unbounded, the shape of the subproblem is slightly modified. See the [API](@ref api_benders) section to get more information.

Coluna

https://github.com/atoptima/Coluna.jl.git

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# Presolve algorithm >### Ruslan Sadykov, 20/10/2023, revised 16/01/2024 The document presents the presolve algorithm implemented in _Coluna_. It is particularly important to run this algorithm after augmenting the partial solution in rounding and diving heuristics. ## 1. Augmenting the partial solution This is an optional step, which should be performed in the case a local partial solution is passed in the input of the preprocessing algorithm. Consider a _local partial solution_ $(\bar{\bm x}^{\rm pure}, \bar{\bm\lambda})$ (where $\bar{\bm x}^{\rm pure}$ is the vector of values for pure master variables, and $\bar{\bm\lambda}$ is the vector of values for master columns), which should be added to the _global partial solution_ $(\hat{\bm x}^{\rm pure}, \hat{\bm\lambda})$. First, we augment the global partial solution: $(\hat{\bm x}^{\rm pure}, \hat{\bm\lambda})\leftarrow(\hat{\bm x}^{\rm pure}+\bar{\bm x}^{\rm pure}, \hat{\bm\lambda}+\bar{\bm\lambda})$. Second, we update the right-hand side values of all master constraints (both robust and non-robust): ${\rm rhs}_i\leftarrow {\rm rhs}_i - {\bm A}^{\rm pure}\cdot\bar{\bm x}^{\rm pure} - {\bm A}^{\rm col}\cdot\bar{\bm \lambda}$, where ${\bm A}^{\rm pure}$ is the matrix of coefficients of pure master constraints and ${\bm A}^{\rm col}$ is the matrix of coefficients of master columns. Third, we update subproblem multiplicities $U_k\leftarrow U_k - \sum_{q\in Q_k}\bar\lambda_q$, and $L_k\leftarrow \max\left\{0,\; L_k - \sum_{q\in Q_k}\bar\lambda_q\right\}$, where $Q_k$ is the set of indices of columns associated with solutions from subproblem $k$. Afterwards, we should update the bounds of pure master variables and representative master variables. For that we first calculate the representative local partial solution: $\bar{\bm x}^{\rm repr} = \sum_{q\in Q}{\bm s}^q\cdot \bar\lambda_q$, where $Q$ is the total number of columns, and ${\bm s}^q$ is the subproblem solution associated with column $\lambda_q$. Update of bounds of variables can be performed one variable at a time. This is presented in the next two sections. ### Pure master variables Consider a pure master variable $x^{\rm pure}_j$ with $\bar{x}^{\rm pure}_j\neq 0$ and bounds $[lb_j,ub_j]$ before augmenting the partial solution. If $\bar{x}^{\rm pure}_j > 0$, then we have $lb_j\leftarrow \max\left\{0, lb_j - \bar{x}^{\rm pure}_j\right\}$, $ub_j\leftarrow ub_j - \bar{x}^{\rm pure}_j$. If $\bar{x}^{\rm pure}_j < 0$, then we have $lb_j\leftarrow lb_j - \bar{x}^{\rm pure}_j$, $ub_j\leftarrow \min\left\{0, ub_j - \bar{x}^{\rm pure}_j\right\}$. > _**Note:**_ An alternative would be to fix pure master variable $x^{\rm pure}_j$ by setting $lb_j\leftarrow 0$ and $ub_j\leftarrow 0$. This would leave less freedom for future updates of the partial solution (i.e. this would lead to a more aggressive diving for example). ### Representative variables We consider a representative master variable $x^{\rm repr}_j$ with bounds $[lb^g_j, ub^g_j]$ before augmenting the partial solution. We assume that $x^{\rm repr}_j$ represents exactly one variable $x^k_j$ in subproblem $k$ with bounds $[lb^l_j, ub^l_j]$ before augmenting the partial solution. _This assumption should be verified before augmenting the partial solution!_ For the clarity of presentation, we omit index $j$ for the remainder of this section. After augmenting the partial solution, the following inequalities should be satisfied: $$ lb^g - \bar{x}^{\rm repr}\leq x^{\rm repr} \leq ub^g - \bar{x}^{\rm repr}.$$ At the same time, we should have $$\min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\leq x^{\rm repr}\leq \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}$$ Thus, the following update should be done $$ lb^g\leftarrow \max\left\{lb^g - \bar{x}^{\rm repr},\; \min\{lb^l\cdot L_k,\; lb^l\cdot U_k\}\right\}$$ $$ ub^g\leftarrow \min\left\{ub^g - \bar{x}^{\rm repr},\; \max\{ub^l\cdot U_k,\; ub^l\cdot L_k\}\right\}$$ > _**Example 1:**_ Let $0\leq x^k\leq 3$, $0\leq x^{\rm repr}\leq 6$, $L_k=0$, and $U_k=2$ before augmenting the partial solution. Let local partial solution $\bar{x}^{\rm repr}=2$. After augmenting partial solution, we have $U_k\leftarrow 1$ and > $$ \max\left\{-2,\; 0\right\}\leq x^{\rm repr} \leq \min\left\{4,\; 3\right\} \Rightarrow 0 \leq x^{\rm repr} \leq 3$$ > _**Example 2:**_ Let $0\leq x^k\leq 5$, $3\leq x^{\rm repr}\leq 6$, $L_k=0$, and $U_k=2$ before augmenting the partial solution. Let local partial solution $\bar{x}^{\rm repr}=2$. Then after augmenting the partial solution, we have $U_k\leftarrow 1$ and > $$ \max\left\{1,\; 0\right\}\leq x'_{\rm repr} \leq \min\left\{4,\; 5\right\} \Rightarrow 1 \leq x'_{\rm repr} \leq 4$$ > _**Example 3:**_ $-1\leq x^k\leq 4$, $-2\leq x^{\rm repr}\leq 2$, $L_k=0$, $U_k=2$. Let $\bar{x}^{\rm repr}=-1$. Then after augmenting the partial solution, we have > $$ \max\left\{-1,\; -1\right\}\leq x^{\rm repr} \leq \min\left\{3,\; 4\right\} \Rightarrow -1 \leq x^{\rm repr} \leq 3$$ ### Implementation details To update bounds of representative and pure master variables in an unified way after augmenting a partial solution, we first calculate so-called _variable domains_. For a subproblem variable $x_j^k$, its domain is obtained as follows: $$[{\rm dom}^-_j,\;{\rm dom}^+_j] = \left[\min\{lb^l_j\cdot L_k,\; lb^l_j\cdot U_k\}, \max\{ub^l_j\cdot U_k,\; ub^l_j\cdot L_k\}\right]$$ For a pure master variable $x^{\rm pure}_j$, its domain depends on value $\bar{x}^{\rm pure}_j$: $$ [{\rm dom}^-_j,\;{\rm dom}^+_j] = \left\{ \begin{array}{ll} [0,+\infty), & \text{ if } \bar{x}^{\rm pure}_j > 0, \\ (-\infty,0], & \text{ if } \bar{x}^{\rm pure}_j < 0, \\ (-\infty,+\infty), & \text{ if } \bar{x}^{\rm pure}_j = 0. \\ \end{array}\right.$$ After calculating variable domains, their bounds can be updated simply by $$ lb_j\leftarrow \max\left\{lb_j - \bar{x}_j,\; {\rm dom}_j^-\right\}$$ $$ ub_j\leftarrow \min\left\{ub_j - \bar{x}_j,\; {\rm dom}_j^+\right\}$$ ## 2. Preprocessing "core" This step is always performed. Preprocessing is done iteratively for a fixed number of iterations. Each iteration consists of the following steps. ### Presolving the representative master Here we apply the standard MIP presolving of the representative master formulation consisting of pure master constraints, representative master variables, and robust master constraints. _Non-robust constraints should be excluded from presolving!_ Such presolve updates slacks of constraints and bounds of variables. It may * deactivate redundant constraints, * fix pure master variables $x_j^{\rm pure}$ with bounds $lb_j=ub_j$ (in this case $\bar{x}_j^{\rm pure}=lb_j$ is added to the global partial solution, we set $lb_j=ub_j\leftarrow 0$ and update the corresponding right-hand-sides of constraints). * detect infeasibility due to variables $x_j^{\rm pure}$ or $x_j^{\rm repr}$ such that $lb_j>ub_j$ (in this case the whole procedure stops with infeasibility). * deactivate pure master variables $x_j^{\rm pure}$ with bounds $lb_j=ub_j=0$. ### Propagate bounds from representative master variables to subproblem variables For each subproblem $k$, $U_k\geq 1$, and each subproblem variable $x_j^k$, we set: $$lb^l_j\leftarrow \max\left\{lb^l_j,\; lb^g_j - (U_k-1)\cdot ub^l_j\right\}$$ $$ub^l_j\leftarrow \min\left\{ub^l_j,\; ub^g_j - \max\{0, L_k-1\}\cdot lb^l_j\right\}$$ ### Presolving the subproblems Again, the standard MIP presolving is applied for each subproblem $k$. Such presolve updates slacks of constraints and bounds of variables. It may * remove redundant constraints, * detect infeasibility due to variables $x_j^k$ such that $lb^l_j>ub^l_j$ (in this case we set $L_k=U_k\leftarrow 0$). * deactivate variables $x_j^k$ with bounds $lb_j^l=ub_j^l=0$ (in this case representative variables $x_j^{\rm repr}$ in the master are also deactivated). ### Updating subproblem multiplicities For each subproblem $k$, $U_k\geq 1$, we try to update its multiplicities, based on local and global bounds of its variables. Consider a variable $x_j^k$, * If $lb^g_j>0$ and $ub^l>0$, then $L_k\leftarrow\max\{L_k, \lceil lb^g_j/ub^l_j\rceil\}$ * If $ub^g_j<0$ and $lb^l<0$, then $L_k\leftarrow\max\{L_k, \lceil ub^g_j/lb^l_j\rceil\}$ * If $lb^l_j>0$ and $ub^g>0$, then $U_k\leftarrow\min\{U_k, \lfloor ub^g_j/lb^l_j\rfloor\}$ * If $ub^l_j<0$ and $lb^g<0$, then $U_k\leftarrow\min\{U_k, \lfloor lb^g_j/ub^l_j\rfloor\}$ ### Propagate bounds from subproblem variables to representative master variables For each representative variable $x^{\rm repr}_j$ representing variable $x_j^k$ in subproblem $k$: $$lb^g_j\leftarrow \max\left\{lb^g_j,\; \min\{lb^l_j\cdot L_k,\; lb^l_j\cdot U_k\}\right\}$$ $$ub^g_j\leftarrow \min\left\{ub^g_j,\; \max\{ub^l_j\cdot L_k,\; ub^l_j\cdot U_k\}\right\}$$ ## 3. Removing non-proper columns Finally, we should deactivate non-proper columns for each subproblem $k$, i.e., columns $\lambda_q$, $q\in Q_k$, such that $s^q_j<lb^l_j$ or $s^q_j>ub^l_j$, where $s^q_j$ is the value of variable $x_j^k$ in solution ${\bm s}_q$ associated with column $\lambda_q$.

Coluna

https://github.com/atoptima/Coluna.jl.git

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Here is a non-exhaustive list of classic problems tackled with Coluna: - [Generalized Assignement](@ref tuto_gen_assignement) and some variants using [pricing callback](@ref tuto_pricing_callback) and [cut callback](@ref tuto_cut_callback) - [Bin Packing](@ref tuto_custom_data)

Coluna

https://github.com/atoptima/Coluna.jl.git

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module PartialWaveFunctions export f_logfact include("factorials.jl") export wignerd, wignerd_doublearg export wignerD, wignerD_doublearg export kronecker include("wignerd.jl") export CG, CG_doublearg export clebschgordan, clebschgordan_doublearg include("clebsch_gordan.jl") end # module # # # # # # # # # # # # # # # # # # # # # # # # # created with PkgTemplates # using PkgTemplates # t = Template(; # user="mmikhasenko", # license="MIT", # authors="Misha Mikhasenko", # dir=joinpath(DEPOT_PATH[1], "dev"), # julia_version=v"1.2", # plugins=[ # TravisCI(), # Codecov(), # AppVeyor(), # ], # ) # generate("PartialWaveFunctions", t) # # # # # # # # # # # # # # # # # # # # # # # # # #

PartialWaveFunctions

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""" CG(j1,m1,j2,m2,j,m) a shortcut for `clebschgordan` """ CG(j1,m1,j2,m2,j,m) = clebschgordan(j1,m1,j2,m2,j,m) """ CG_doublearg(j1,m1,j2,m2,j,m) a shortcut for `clebschgordan_doublearg` """ CG_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) = clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) """ clebschgordan(j1,m1,j2,m2,j,m) gives a numerical value of the Clebsch-Gordan coefficient ``` ⟨ j₁ m₁ ; j₂ m₂ | j m ⟩ ``` The input values are expected to be __integers__. For a general case including half-integers see `clebschgordan_doublearg`. """ clebschgordan(j1,m1,j2,m2,j,m) = clebschgordan_doublearg(2j1,2m1,2j2,2m2,2j,2m) """ clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) gives a numerical value of the Clebsch-Gordan coefficient ``` ⟨ j₁ m₁ ; j₂ m₂ | j m ⟩ ``` The function requires __doubled value of the momenta__ for the input. """ function clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) ((abs(two_m1) > two_j1) || (abs(two_m2) > two_j2) || (abs(two_m ) > two_j )) && return 0.0 ((two_m1+two_m2 != two_m) || !(abs(two_j1-two_j2) ≤ two_j ≤ two_j1+two_j2)) && return 0.0 # prefactor = sqrt(two_j+1)* exp( ( f_logfact2(two_j1+two_j2-two_j) + f_logfact2(two_j1+two_j -two_j2) + f_logfact2(two_j2+two_j -two_j1) - f_logfact2(two_j1+two_j2+two_j+2) + # f_logfact2(two_j1+two_m1) + f_logfact2(two_j1-two_m1) + f_logfact2(two_j2+two_m2) + f_logfact2(two_j2-two_m2) + f_logfact2(two_j +two_m ) + f_logfact2(two_j -two_m ) ) / 2) res = 0.0 two_t_min = max(0, two_j2-two_m1-two_j, two_j1+two_m2-two_j) two_t_max = min(two_j1+two_j2-two_j, two_j1-two_m1, two_j2+two_m2) # for two_t = two_t_min:2:two_t_max logs = f_logfact2(two_t) + f_logfact2(two_j-two_j2+two_m1+two_t) + f_logfact2(two_j-two_j1-two_m2+two_t) + f_logfact2(two_j1+two_j2-two_j-two_t) + f_logfact2(two_j1-two_m1-two_t) + f_logfact2(two_j2+two_m2-two_t); res += (abs(two_t) % 4 == 2 ? -1.0 : 1.0) * exp(-logs); end res *= prefactor return res; end

PartialWaveFunctions

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const logfact = [0,[sum(log(i) for i=1:n) for n=1:50]...]; """ f_logfact(n) The function returns logarithm of n! """ function f_logfact(n) (n < 0 || n > 50) && error("n < 0 || n > 50. Modify if needed.") @inbounds return logfact[n+1] end # special function used in the code: # (two_n/2)! for even numbers # (two_n-1/2)! for odd numbers const logfact2 = [f_logfact(div(two_n,2)) for two_n=1:100]; function f_logfact2(two_n) (two_n < 0 || two_n > 100) && error("two_n < 0 || two_n > 100. Modify if needed.") @inbounds return logfact2[two_n+1] end

PartialWaveFunctions

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# _| _| # _|_|_| _|_|_|_| _|_| _|_|_| _|_| _| _|_| # _| _| _| _| _|_|_|_| _| _| _|_|_|_| _|_| # _| _| _| _| _| _| _| _| _| # _| _| _| _|_| _|_|_| _|_|_| _|_|_| _| # _| # _|_| function jacobi_pols(n, a, b, z) if (n+a > length(logfact) || n+b > length(logfact)) error("Error: j is too high, please check the implementation of jacobi polynomials!") end # special case when I can not calculate log if (z ≈ 1) || (z ≈ -1) return sum((s % 2 == 0 ? 1.0 : -1.0) * exp(logfact[n+a+1] + logfact[n+b+1]-logfact[n-s+1]-logfact[a+s+1]-logfact[s+1]-logfact[n+b-s+1])* ((1-z)/2.0)^s*((1+z)/2.0)^(n-s) for s = 0:n) end # general case ls = log((1.0-z)/2.0); lc = log((1.0+z)/2.0); res = 0.0; for s = 0:n logs = logfact[(n+a+1)::Int] + logfact[(n+b+1)::Int]- logfact[(n-s+1)::Int]-logfact[(a+s+1)::Int]- logfact[(s+1)::Int]-logfact[(n+b-s+1)::Int]; args = s*ls + (n-s)*lc; res += (s % 2 == 0 ? 1.0 : -1.0) * exp(logs+args); end return res; end function wignerd_hat(j, m1, m2, z) if abs(m1) > j || abs(m2) > j return zero(z) end factor = ((abs(m1-m2)+m1-m2)/2) % 2 == 0 ? one(z) : -one(z); am1 = abs(m1); am2 = abs(m2); M = (am1 > am2) ? am1 : am2; N = (am1 < am2) ? am1 : am2; gammas = logfact[Int(j-M+1)]+logfact[Int(j+M+1)]-(logfact[Int(j-N+1)]+logfact[Int(j+N+1)]) return factor / 2^M * exp(gammas/2)* jacobi_pols(Int(j-M), Int(abs(m1-m2)), Int(abs(m1+m2)), z); end """ wignerd(j, m1, m2, cosβ) Small wigner d-function for representation `j` with indices `m1`, `m2`, the argument `cosβ` is the cosine of the rotation angle. The function gives the value of the matrix element ``` ⟨ j m1 | exp(-i Jy β)| j m2 ⟩. ``` The input values are expected to be __integers__. For a general case including half-integers see `wignerd_doublearg`. """ function wignerd(j, m1, m2, z) (z ≈ 1) && return m1 == m2 ? one(z) : zero(z) (z ≈ -1) && return m1 == -m2 ? (iseven(j-m2) ? one(z) : -one(z)) : zero(z) # hat = wignerd_hat(j, m1, m2, z); xi = sqrt(1-z)^abs(Int(m1-m2))*sqrt(1+z)^abs(Int(m1+m2)); return hat*xi; end """ wignerD(j, m1, m2, α, cosβ, γ) Wigner D-function for representation `j` with indices `m1`, `m2`, α, β, and γ are the rotation angles The function gives the value of the matrix element ``` ⟨ j m1 | exp(-i Jz α) exp(-i Jy β) exp(-i Jz γ) | j m2 ⟩. ``` The input values are expected to be __integers__. For a general case including half-integers see `wignerD_doublearg`. """ wignerD(j, m1, m2, α, cosβ, γ) = wignerd(j, m1, m2, cosβ) * cis(-m1*α-m2*γ) # # _| _| _|_| _| _| # _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_| _| _|_| # _| _| _| _| _| _|_|_|_| _|_|_|_|_| _| _| _| _| _|_|_|_| _| _| _|_|_|_| _|_| # _| _| _| _| _| _| _| _| _| _| _| _| _| _| _| # _| _| _|_|_| _| _| _| _| _| _|_| _|_|_| _|_|_| _|_|_| _| # _| # _|_| function wignerd_hat_doublearg(two_j, two_m1, two_m2, z) # @show j, m1, m2 if abs(two_m1) > two_j || abs(two_m2) > two_j return zero(z) end factor = (abs(two_m1-two_m2)+two_m1-two_m2) % 8 == 4 ? -one(z) : one(z); two_am1 = abs(two_m1); two_am2 = abs(two_m2); (two_M, two_N) = (two_am1 > two_am2) ? (two_am1,two_am2) : (two_am2,two_am1); # j_mnus_M = div(two_j-two_M,2) j_plus_M = div(two_j+two_M,2) j_mnus_N = div(two_j-two_N,2) j_plus_N = div(two_j+two_N,2) # gammas = logfact[j_mnus_M+1] + logfact[j_plus_M+1] - logfact[j_mnus_N+1] - logfact[j_plus_N+1] return factor / 2^(two_M/2) * exp(gammas/2) * jacobi_pols(j_mnus_M, div(abs(two_m1-two_m2),2), div(abs(two_m1+two_m2),2), z); end """ wignerd_doublearg(j, m1, m2, cosβ) Small wigner d-function for representation `j` with indices `m1`, `m2`, the argument `cosβ` is the cosine of the rotation angle. The function gives the value of the matrix element ``` ⟨ j m1 | exp(-i Jy β)| j m2 ⟩. ``` The function requires __doubled value of the momenta__ for the input. """ function wignerd_doublearg(two_j, two_m1, two_m2, z) (z ≈ 1) && return two_m1 == two_m2 ? one(z) : zero(z) (z ≈ -1) && return two_m1 == -two_m2 ? (iseven(div(two_j-two_m2,2)) ? one(z) : -one(z)) : zero(z) # hat = wignerd_hat_doublearg(two_j, two_m1, two_m2, z); xi = (1-z)^(abs(two_m1-two_m2)/4)*(1+z)^(abs(two_m1+two_m2)/4); return hat*xi; end """ wignerD_doublearg(two_j, two_m1, two_m2, α, cosβ, γ) Wigner D-function for representation `j` with indices `m1`, `m2`, α, β, and γ are the rotation angles The function gives the value of the matrix element ``` ⟨ j m1 | exp(-i Jz α) exp(-i Jy β) exp(-i Jz γ) | j m2 ⟩. ``` The function requires __doubled value of the momenta__ for the input. """ wignerD_doublearg(two_j, two_m1, two_m2, α, cosβ, γ) = wignerd_doublearg(two_j, two_m1, two_m2, cosβ) * cis(-two_m1*α/2-two_m2*γ/2); """ kronecker(i, j) = (i==j) ? 1 : 0 """ kronecker(i, j) = (i==j) ? 1 : 0

PartialWaveFunctions

https://github.com/mmikhasenko/PartialWaveFunctions.jl.git

[ "MIT" ]

0.2.0

9e94fb961f02c4eaeb8e83268cb34245395030dd

code

625

using PartialWaveFunctions using Test @testset "PartialWaveFunctions.jl" begin # Clebsch Gordan coefficient @test clebschgordan_doublearg(3, 3, 1, 1, 2*2, 2*2) == 1 @test clebschgordan_doublearg(3, 3, 1, -1, 2*1, 2*1) ≈ sqrt(3)/2 @test clebschgordan_doublearg(3, -1, 1, 1, 2*1, 2*0) ≈ -sqrt(2)/2 # Wigner Function @test wignerD(3, 2, 1, π, 0, -π) ≈ -sqrt(10)/8 + 0im @test wignerd(2, 0, 0, 1/sqrt(2)) ≈ (3/2-1)/2 @test wignerd(1, 1, 0, 0.3) ≈ -sqrt(1-0.3^2)/sqrt(2) @test wignerd_doublearg(1, 1, 1, 0) ≈ 1/sqrt(2) # @test sum(kronecker(i,j) for i in 1:5, j in 1:5) == 5 end

PartialWaveFunctions

https://github.com/mmikhasenko/PartialWaveFunctions.jl.git

[ "MIT" ]

0.2.0

9e94fb961f02c4eaeb8e83268cb34245395030dd

code

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using Test using Parameters using BenchmarkTools # import PartialWaveFunctions: CG_doublearg, clebschgordan_doublearg ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) = clebschgordan_doublearg(two_j1,two_m1,two_j2,two_m2,two_j,two_m) # using GSL function ClGd_gsl(two_j1,two_m1,two_j2,two_m2,two_j,two_m) factor = sqrt(two_j+1)*(mod(two_j1-two_j2+two_m,4)==2 ? -1 : +1) three_j = sf_coupling_3j(two_j1,two_j2,two_j,two_m1,two_m2,-two_m) return factor*three_j; end # using SymPy import PyCall PyCall.pyimport_conda("sympy.physics.wigner","sympy") import_from(sympy.physics.wigner) # ClGd_sympy(two_j1,two_m1,two_j2,two_m2,two_j,two_m) = convert(Float64, clebsch_gordan(Sym(two_j1)/2, Sym(two_j2)/2, Sym(two_j)/2, Sym(two_m1)/2, Sym(two_m2)/2, Sym(two_m)/2)) # import HalfIntegers: half using WignerSymbols function ClGd_WS(two_j1,two_m1,two_j2,two_m2,two_j,two_m) ((abs(two_m1) > two_j1) || (abs(two_m2) > two_j2) || (abs(two_m ) > two_j )) && return 0.0 return clebschgordan(half(two_j1),half(two_m1),half(two_j2),half(two_m2),half(two_j),half(two_m)) end # _| # _| _|_| _|_|_| _|_|_| _|_|_| # _|_| _| _| _| _| _| _| # _| _| _| _| _| _| _| # _| _|_|_| _| _| _|_|_| function rand_clebsch(;two_j_max::Int=15) two_j1 = rand(0:two_j_max); two_m1 = rand(-two_j1:2:two_j1) two_j2 = rand(0:two_j_max); two_m2 = rand(-two_j2:2:two_j2) two_j = rand(abs(two_j2-two_j1):2:(two_j1+two_j2)) two_m = two_m1+two_m2 abs(two_m) > two_j && return rand_clebsch(; two_j_max=two_j_max) (two_j1=two_j1, two_j2=two_j2, two_j=two_j, two_m1=two_m1, two_m2=two_m2, two_m=two_m) end # fraction of vanishing clebshces ≈ 0.6% sum(let @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) ≈ 0 end for _ in 1:10_000) # _| # _|_|_| _|_| _|_|_| _|_| _|_|_| _|_|_| _| _|_| _|_|_| _|_| _|_|_| # _| _| _| _| _| _| _| _| _| _| _|_| _| _|_| _| _| _| _| # _| _| _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| # _|_|_| _|_| _| _| _| _|_|_| _|_|_| _| _| _|_|_| _|_| _| _| # _| # _| for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() # v1 = ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) v2 = ClGd_gsl(two_j1,two_m1,two_j2,two_m2,two_j,two_m) v3 = ClGd_sympy(two_j1,two_m1,two_j2,two_m2,two_j,two_m) v4 = ClGd_WS(two_j1,two_m1,two_j2,two_m2,two_j,two_m) # (abs(v1-v2) > 1e-5) && error("gsl is different: $(v1) $(v2)") (abs(v1-v3) > 1e-5) && error("sympy is different") (abs(v1-v4) > 1e-5) && error("WS is different") end # _| _| _| # _|_|_|_| _|_|_| _|_| _|_|_| _|_|_| # _| _| _| _| _| _| _| _| _| _| # _| _| _| _| _| _| _| _| _| _| # _|_| _| _| _| _| _| _| _| _|_|_| # _| # _|_| @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_pwf(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 227.200 μs @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_gsl(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 921.8 μs @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_WS(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 7.483 ms @btime for _ in 1:1_000 @unpack two_j1, two_j2, two_j, two_m1, two_m2, two_m = rand_clebsch() ClGd_sympy(two_j1,two_m1,two_j2,two_m2,two_j,two_m) end # 1.61 s # Summary # ------------- # function | `PartialWaveFunctions.jl` | `GSL.jl` | `WignerSymbols.jl` | `SymPy.jl` | # ------------- # random Clebsh-Gordan coef.(j<15) | 227.2 μs | 921.8 μs | 7.483 ms | 1.61 s | # -------------

PartialWaveFunctions

https://github.com/mmikhasenko/PartialWaveFunctions.jl.git

[ "MIT" ]

0.2.0

9e94fb961f02c4eaeb8e83268cb34245395030dd

docs

2812

# PartialWaveFunctions [![Build Status](https://travis-ci.com/mmikhasenko/PartialWaveFunctions.jl.svg?branch=master)](https://travis-ci.com/mmikhasenko/PartialWaveFunctions.jl) [![Build Status](https://ci.appveyor.com/api/projects/status/github/mmikhasenko/PartialWaveFunctions.jl?svg=true)](https://ci.appveyor.com/project/mmikhasenko/PartialWaveFunctions-jl) [![Codecov](https://codecov.io/gh/mmikhasenko/PartialWaveFunctions.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/mmikhasenko/PartialWaveFunctions.jl) Julia native implementation of the special functions used in the Partial Wave Analysis for High Energy Physics. Currently, Wigner D-functions and Clebsch-Gordan(CG) coefficients are available. The implementation of the CG coefficient is by factor 4 faster than the C++ code from the GSL package (see details in [test/timing](test/timing_different_packages.jl)). ## Installation ```julia ] add PartialWaveFunctions ``` ## Usage ```julia using PartialWaveFunctions # convenient call for integer indices let j=3, (m1,m2) = (1,-1), cosθ=0.3 wignerd(j,m1,m2,cosθ) end # return 0.293 clebschgordan(1,0,1,0,1,0) # <1, 0; 1, 0 | 1, 0> = 0.0 : ρ⁰ → π⁰ π⁰ CG(1,0,1,0,1,0) # a shortcut ``` General implementation includes the half-integer indices: ```julia let two_j=3, (two_m1,two_m2) = (1,-1), cosθ=0.3 wignerd_doublearg(two_j,two_m1,two_m2, cosθ) end # return -0.562 clebschgordan_doublearg(2,0,1,1,1,1) # <1, 0; 1/2, 1/2 | 1/2, 1/2> = -0.577 CG_doublearg(2,0,1,1,1,1) # a shortcut ``` ## Related packages: * python calls via `SymPy.jl`. Ideal for symbolic calculations. Works pretty with jupyter notebooks due to the latex output. See details in the [test/physics](https://github.com/JuliaPy/SymPy.jl/blob/master/test/test-physics.jl). * [WignerD.jl](https://github.com/jishnub/WignerD.jl) interfaces `Fortran` for the `WignerD`. * [WignerSymbols.jl](https://github.com/Jutho/WignerSymbols.jl) `Julia` package specialized on Symbols. Particularly it contains the Clebsch-Gordan coefficients. * [GSL.jl](https://github.com/JuliaMath/GSL.jl) interfaces `C++`. It can calculate Sperical Harmionics, Legendre polynomials. `WignerD` is not [wrapped-up](https://github.com/JuliaMath/GSL.jl/issues/66). ## References * The Wigner functions are expressed via the Jacobi polynomials Pₙ⁽ᵃᵇ⁾(z) using Eq. (3.74) of L. Biedenharn, J. Louck, and P. Carruthers, Angular Momentum in Quantum Physics: Theory and Application * The Jacobi polynomials Pₙ⁽ᵃᵇ⁾(z) are codded using a series expression in powers of (1-z), see e.g. [wikipedia page](https://en.wikipedia.org/wiki/Jacobi_polynomials). * Clebsch-Gordan coefficients are computed from explicit expression via a finite series, see e.g. [wikipedia page](https://en.wikipedia.org/wiki/Table_of_Clebsch%E2%80%93Gordan_coefficients)

PartialWaveFunctions

https://github.com/mmikhasenko/PartialWaveFunctions.jl.git

[ "MIT" ]

0.7.0

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using QuanticsTCI using Documenter DocMeta.setdocmeta!(QuanticsTCI, :DocTestSetup, :(using QuanticsTCI); recursive=true) makedocs(; modules=[QuanticsTCI], authors="Ritter.Marc <[email protected]> and contributors", sitename="QuanticsTCI.jl", format=Documenter.HTML(; canonical="https://github.com/tensor4all/QuanticsTCI.jl", edit_link="main", assets=String[]), pages=[ "Home" => "index.md", "API Reference" => "apireference.md", ]) deploydocs(; repo="github.com/tensor4all/QuanticsTCI.jl.git", devbranch="main", )

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

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function berrycurvature_dets(H::Array{Matrix{ComplexF64}}, n::Integer) Heigen = eigen.(H) vecs00 = [vectors[:, 1:n] for (vals, vectors) in Heigen] vecs01 = circshift(vecs00, (0, 1)) vecs10 = circshift(vecs00, (1, 0)) vecs11 = circshift(vecs00, (1, 1)) function getdets(vecs1, vecs2) d = det(adjoint(vecs1) * vecs2) return d end dets = ( getdets.(vecs00, vecs10) .* getdets.(vecs10, vecs11) .* getdets.(vecs11, vecs01) .* getdets.(vecs01, vecs00) ) # bc = angle.(dets) bc = @. mod(angle(dets) + pi / 2, pi) - pi / 2 return bc end function modindex(i::Integer, n::Integer) return mod(i - 1, n) + 1 end function berrycurvature_quantics_dets( Hfunc, n::Integer, q::Vector{<:Integer}, nquantics::Integer ) k = [quantics_to_index(qi)[1] for qi in split_dimensions(q, 2)] Hplaquette = [ Hfunc(modindex.(k .+ [dkx, dky], 2^nquantics)) for dkx in -1:0, dky in -1:0] return berrycurvature_dets(Hplaquette, n)[1, 1] end function berrycurvature_derivatives( H::Matrix{ComplexF64}, Hderivative1::Matrix{ComplexF64}, Hderivative2::Matrix{ComplexF64}, n::Integer ) E, U = eigen(Hermitian(H)) return -1 * sum(imag( ( (U[:, v]' * Hderivative1 * U[:, c]) * (U[:, c]' * Hderivative2 * U[:, v]) - (U[:, v]' * Hderivative2 * U[:, c]) * (U[:, c]' * Hderivative1 * U[:, v]) ) / (E[c] - E[v])^2 ) for v in 1:n, c in n+1:length(E)) # Ediff = E[n+1:end] .- E[1:n]' # v1 = U[:, 1:n]' * Hderivative1 * U[:, n+1:end] ./ Ediff' # v2 = U[:, n+1:end]' * Hderivative2 * U[:, 1:n] ./ Ediff # return 2 * tr(imag.(v1 * v2)) end function berrycurvature_quantics_derivatives( Hfunc, Hderivfunc, n::Integer, q::Vector{<:Integer} ) k = [quantics_to_index(qi)[1] for qi in deinterleave_dimensions(q, 2)] return berrycurvature_derivatives( Hfunc(k), Hderivfunc(k, 1), Hderivfunc(k, 2), n) end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

5622

module chern using LinearAlgebra using PyPlot import TensorCrossInterpolation as TCI using QuanticsTCI using BenchmarkTools using ITensors include("honeycomb.jl") include("kanemele.jl") include("latticeplot.jl") include("berry.jl") function maxlinkdim(n::Integer, localdim::Integer=2) return 0:n-2, [min(localdim^i, localdim^(n - i)) for i in 1:(n-1)] end function sumqtt(qtt) return prod(sum(T, dims=2)[:, 1, :] for T in qtt)[1] end function sum_quantics_mps(mps) m = mps[1] * ITensor(1, siteind(mps, 1)) for i in 2:length(mps) m *= mps[i] * ITensor(1, siteind(mps, i)) end return scalar(m) end function mps_to_array(mps) result = Vector{Array{Float64, 3}}() T1 = Array(mps[1], siteind(mps, 1), linkind(mps, 1)) push!(result, reshape(T1, 1, size(T1)...)) for i in 2:length(mps)-1 push!(result, Array(mps[i], linkind(mps, i-1), siteind(mps, i), linkind(mps, i))) end Tlast = Array(mps[end], linkind(mps, length(mps)-1), siteind(mps, length(mps))) push!(result, reshape(Tlast, size(Tlast)..., 1)) return result end mutable struct functioncounter f::Function n::Int end Base.broadcastable(m::functioncounter) = Ref(m) function functioncounter(f::Function) return functioncounter(f, 0) end function (f::functioncounter)(k) f.n += 1 return f.f(k) end function getberryqtt_dets( nquantics::Integer, kxvals::Vector{Float64}, kyvals::Vector{Float64}, n::Integer, lattice::TCI.IndexSet{HoneycombSite}, q::Integer, lambdaSO::Float64; spinindex=1, tolerance=1e-12 ) Hcached = TCI.CachedFunction{Matrix{ComplexF64}}( kindex -> get_H( q, lambdaSO, [kxvals[kindex[1]], kyvals[kindex[2]]], lattice )[:, spinindex, :, spinindex], [2^nquantics, 2^nquantics] ) f = functioncounter(k -> berrycurvature_quantics_dets(Hcached, n, k, nquantics)) firstpivot = TCI.optfirstpivot(f, fill(2, 2 * nquantics)) f.n = 0 tci, ranks, errors = TCI.crossinterpolate( Float64, f, fill(2, 2 * nquantics), firstpivot, tolerance=tolerance, maxiter=200, verbosity=1, ) qtt = TCI.tensortrain(tci) return sumqtt(qtt) / 2pi, qtt, ranks, errors, f.n end function getberrymps_dets( nquantics::Integer, kxvals::Vector{Float64}, kyvals::Vector{Float64}, n::Integer, lattice::TCI.IndexSet{HoneycombSite}, q::Integer, lambdaSO::Float64; spinindex=1, tolerance=1e-12 ) H = [ get_H(q, lambdaSO, [kx, ky], lattice)[:, spinindex, :, spinindex] for kx in kxvals, ky in kyvals ] quanticsindices = [ Index(2, i % 2 == 0 ? "qx$(div(i, 2))" : "qy$(div(i, 2))") for i in 1:2nquantics ] A = ITensor(berrycurvature_dets(H, n), quanticsindices) mps = MPS(A, quanticsindices, cutoff=tolerance, maxdim=200) return sum_quantics_mps(mps) / 2pi, mps_to_array(mps), linkdims(mps), prod(size(A)) end function getberryqtt_derivs( nquantics::Integer, kxvals::Vector{Float64}, kyvals::Vector{Float64}, n::Integer, lattice::TCI.IndexSet{HoneycombSite}, q::Integer, lambdaSO::Float64; spinindex=1, tolerance=1e-12 ) Hfunc(kindex) = get_H(q, lambdaSO, [kxvals[kindex[1]], kyvals[kindex[2]]], lattice)[:, spinindex, :, spinindex] Hderivfunc(kindex, derivdirection) = get_H(q, lambdaSO, [kxvals[kindex[1]], kyvals[kindex[2]]], lattice, derivative_direction=derivdirection)[:, spinindex, :, spinindex] f = functioncounter(k -> berrycurvature_quantics_derivatives(Hfunc, Hderivfunc, n, k)) firstpivot = TCI.optfirstpivot(f, fill(2, 2 * nquantics)) f.n = 0 tci, ranks, errors = TCI.crossinterpolate( Float64, f, fill(2, 2 * nquantics), firstpivot, tolerance=tolerance, maxiter=200, verbosity=1, ) qtt = TCI.tensortrain(tci) return sumqtt(qtt) / 2pi, qtt, ranks, errors, f.n end struct BerryResult nq::Int chernnumber::Float64 qtt::Vector{Array{Float64,3}} ranks::Vector{Int} errors::Vector{Float64} nevals::Int timeestimate::Float64 chernnumbermps::Float64 mps::Vector{Array{Float64,3}} mpsranks::Vector{Int} mpsnevals::Int mpstimeestimate::Float64 end function testberry(q::Integer, lambdaSO::Float64, nquantics=5:10) lattice = honeycomblattice(0, 1, 0, q - 1) BZedgex = pi / 1.5 BZedgey = pi / sqrt(3) results = BerryResult[] for nq in nquantics ndiscretization = 2^nq kxvals = collect(range(-BZedgex, BZedgex; length=ndiscretization)) .+ (BZedgex / ndiscretization) kyvals = collect(range(-BZedgey, BZedgey; length=ndiscretization)) .+ (BZedgey / ndiscretization) timeestimate = @elapsed result = getberryqtt_dets(nq, kxvals, kyvals, 2q, lattice, q, lambdaSO, tolerance=1e-5) mpstime = @elapsed mpsresult = getberrymps_dets(nq, kxvals, kyvals, 2q, lattice, q, lambdaSO, tolerance=1e-5) push!( results, BerryResult( nq, result..., timeestimate, mpsresult..., mpstime )) println( "Finished nq = $nq. Chern number from qtt is $(last(results).chernnumber). Time elapsed is $timeestimate for $(last(results).nevals) function evaluations. Chern number from mps is $(last(results).chernnumbermps). Time elapsed is $mpstime for $(last(results).mpsnevals) function evaluations.") end return results end end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

505

using PyPlot using JLD2 include("chern.jl") nq = parse(Int, ARGS[1]) result = chern.testberry(4, 0.2, nq)[1] jldsave("chern_results/nq$nq.jld2"; nq=result.nq, chernnumber=result.chernnumber, qtt=result.qtt, ranks=result.ranks, errors=result.errors, nevals=result.nevals, timeestimate=result.timeestimate, chernnumbermps=result.chernnumbermps, mps=result.mps, mpsranks=result.mpsranks, mpsnevals=result.mpsnevals, mpstimeestimate=result.mpstimeestimate)

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

7719

import TensorCrossInterpolation as TCI using QuanticsTCI using JLD2 include("chern.jl") pauli0 = [1. 0.; 0. 1.] pauli = [ [0. 1.; 1. 0.], [0. -1.0im; 1.0im 0.], [1. 0.; 0. -1.] ] antisymmetricproduct(u, v) = u[1] * v[2] - u[2] * v[1] function haldane(k, t2, ϕ, m) a::Vector{Vector{Float64}} = [ [1, 0], [-0.5, 0.5sqrt(3)], [-0.5, -0.5sqrt(3)] ] b::Vector{Vector{Float64}} = [a[2] - a[3], a[3] - a[1], a[1] - a[2]] return 2 * t2 * cos(ϕ) * sum(cos(k' * bi) for bi in b) * pauli0 + # NNN hopping sum(cos(k' * ai) * pauli[1] + sin(k' * ai) * pauli[2] for ai in a) + # NN hopping (m - 2 * t2 * sin(ϕ) * sum(sin(k' * bi) for bi in b)) * pauli[3] # staggered offset end function scalar(a::Matrix) if size(a) == (1, 1) return first(a) else throw(ArgumentError("$a is not a scalar.")) end end function evaluate_qtt(qtt, q::Vector{<:Integer}) return scalar(prod(T[:, i, :] for (T, i) in zip(qtt, q))) end struct cachedfunc{ValueType} f::Function d::Dict{Vector{Int}, ValueType} function cachedfunc(::Type{ValueType}, f::Function) where ValueType new{ValueType}(f, Dict()) end end function (cf::cachedfunc{ValueType})(x::Vector{Int})::ValueType where {ValueType} if haskey(cf.d, x) return cf.d[x] else val = cf.f(x) cf.d[deepcopy(x)] = val return val end end Base.broadcastable(x::cachedfunc) = Ref(x) function sumqtt(qtt) return prod(sum(T, dims=2)[:, 1, :] for T in qtt)[1] end function maxrelerror(f, qtt::Vector{Array{Float64, 3}}, indices::Vector{Vector{Int}}) return maximum(abs(f(i) - evaluate_qtt(qtt, i)) / abs(f(i)) for i in indices) end function maxabserror(f, qtt::Vector{Array{Float64, 3}}, indices::Vector{Vector{Int}}) return maximum(abs(f(i) - evaluate_qtt(qtt, i)) for i in indices) end function crossinterpolate_chern( ::Type{ValueType}, f, localdims::Vector{Int}, firstpivot::TCI.MultiIndex=ones(Int, length(localdims)); tolerance::Float64=1e-8, maxiter::Int=200, sweepstrategy::TCI.SweepStrategies.SweepStrategy=TCI.SweepStrategies.back_and_forth, pivottolerance::Float64=1e-12, normalizeerror=true, verbosity::Int=0, additionalpivots::Vector{TCI.MultiIndex}=TCI.MultiIndex[], evalooserror::Bool=false, oosindices::Vector{TCI.MultiIndex}=[rand([1, 2], length(localdims)) for _ in 1:2000], ) where {ValueType} tci = TCI.TensorCI{ValueType}(f, localdims, firstpivot) n = length(tci) errors = Float64[] cherns = Float64[] ranks = Int[] inserrors = Float64[] ooserrors = Float64[] for pivot in additionalpivots println("Adding pivot $pivot") TCI.addglobalpivot!(tci, f, pivot, tolerance) println("Rank $(TCI.rank(tci))") end for iter in TCI.rank(tci)+1:maxiter foward_sweep = ( sweepstrategy == TCI.SweepStrategies.forward || (sweepstrategy != TCI.SweepStrategies.backward && isodd(iter)) ) if foward_sweep TCI.addpivot!.(tci, 1:n-1, f, pivottolerance) else TCI.addpivot!.(tci, (n-1):-1:1, f, pivottolerance) end push!(errors, TCI.lastsweeppivoterror(tci)) push!(ranks, maximum(TCI.rank(tci))) if evalooserror tt = TCI.tensortrain(tci) insindices = collect(setdiff(keys(f.d), oosindices)) push!(inserrors, maxabserror(f, tt, insindices)) push!(ooserrors, maxabserror(f, tt, oosindices)) push!(cherns, sumqtt(tt) / 4 / 2pi) end if verbosity > 0 && (mod(iter, 10) == 0 || last(errors) < tolerance) if evalooserror println("rank = $(last(ranks)), error = $(last(errors)), chern = $(last(cherns))") else println("rank = $(last(ranks)), error = $(last(errors))") end end errornormalization = normalizeerror ? tci.maxsamplevalue : 1.0 if last(errors) < tolerance * errornormalization break end end errornormalization = normalizeerror ? tci.maxsamplevalue : 1.0 return tci, ranks, errors ./ errornormalization, cherns, inserrors, ooserrors end function evaluatechern_haldane( deltam::Float64, nquantics::Int; t2::Float64=1e-1, tolerance::Float64=1e-4, evalooserror::Bool=false, ) phi = pi/2 m = 3sqrt(3) * t2 + deltam domainboundx = [-4pi/3, 4pi/3] domainboundy = [-6pi/(3sqrt(3)), 6pi/(3sqrt(3))] ndiscretization = 2^nquantics kxvals = range(domainboundx..., length=ndiscretization+1)#[2:end] kxvals = 0.5 .* (kxvals[1:ndiscretization] .+ kxvals[2:end]) kyvals = range(domainboundy..., length=ndiscretization+1)#[2:end] kyvals = 0.5 .* (kyvals[1:ndiscretization] .+ kyvals[2:end]) f(q) = chern.berrycurvature_quantics_dets( kindex -> haldane([kxvals[kindex[1]], kyvals[kindex[2]]], t2, phi, m), 1, q, nquantics) localdims = fill(4, nquantics) #cf = TCI.CachedFunction{Float64}(f, localdims) cf = cachedfunc(Float64, f) # proposedpivots = [ # TCI.optfirstpivot(cf, dims, rand([1, 2, 3, 4], nquantics)) for p in 1:1000 # ] #firstpivot = proposedpivots[argmax(cf.(proposedpivots))] firstpivot = TCI.optfirstpivot(cf, localdims) println("$firstpivot, $(cf(firstpivot))") # [2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, # 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1][1:2*nquantics]) additionalpivots = [] # TCI.optfirstpivot(cf, localdims, # [2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, # 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1][1:2*nquantics]), # TCI.optfirstpivot(cf, localdims), # TCI.optfirstpivot(cf, localdims, localdims)] # TCI.optfirstpivot(cf, localdims, repeat([1, 2], nquantics)), # TCI.optfirstpivot(cf, localdims, repeat([2, 1], nquantics))] # sort!(additionalpivots, by=abs ∘ cf, rev=true) # for a in additionalpivots # println(a, cf(a)) # end cutxstep = div(ndiscretization, 4) #quarter = div(ndiscretization, 4) cutxvals = 1:cutxstep:ndiscretization cutystep = div(ndiscretization, 8192) oosindices = [ index_to_quantics([kxi, kyi], nquantics) for kxi in cutxvals, kyi in 684:cutystep:ndiscretization ] walltime = @elapsed tci, ranks, errors, cherns, inserrors, ooserrors = crossinterpolate_chern( Float64, cf, localdims, firstpivot, tolerance=tolerance, maxiter=200, verbosity=1, pivottolerance=1e-16, #additionalpivots = additionalpivots, evalooserror=evalooserror, oosindices=oosindices[:] ) chernnumber = NaN if !evalooserror walltimeint = @elapsed chernnumber = sumqtt(TCI.tensortrain(tci)) / 4 / 2pi walltime += walltimeint else chernnumber = last(cherns) end println("δm = $deltam, R = $nquantics : C = $chernnumber") savepath::String = ( evalooserror ? "example/chern/haldane_results_oos/nq$(nquantics)_deltam$(deltam).jld2" : "example/chern/haldane_results/nq$(nquantics)_deltam$(deltam).jld2" ) jldsave( savepath; deltam=deltam, nquantics=nquantics, cherns=cherns, chernnumber=chernnumber, tci=tci, ranks=ranks, errors=errors, nevals=length(cf.d), walltime=walltime, inserrors=inserrors, ooserrors=ooserrors ) end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

1857

using TensorCrossInterpolation struct HoneycombSite R::Vector{Int} c::Int function HoneycombSite(R::Vector{Int}, c::Int) if length(R) != 2 || (c != 1 && c != 2) throw(ArgumentError("Invalid site specification R = $R, c = $c")) end return new(R, c) end end function Base.isequal(l::HoneycombSite, r::HoneycombSite) return l.R == r.R && l.c == r.c end function Base.hash(s::HoneycombSite, h::UInt) return foldr(hash, [s.R, s.c, :HoneycombSite]; init=h) end function realspacecoordinates(s::HoneycombSite) A1::Vector{Float64} = [3 / 2, sqrt(3) / 2] A2::Vector{Float64} = [0, sqrt(3)] cshift::Vector{Float64} = s.c == 1 ? [0, 0] : [1, 0] return A1 * s.R[1] + A2 * s.R[2] + cshift end function neighbours(s::HoneycombSite) if s.c == 1 return [ HoneycombSite(s.R, 2), HoneycombSite(s.R + [-1, 0], 2), HoneycombSite(s.R + [-1, 1], 2) ] else return [ HoneycombSite(s.R, 1), HoneycombSite(s.R + [1, 0], 1), HoneycombSite(s.R + [1, -1], 1) ] end end function nextneighbours(s::HoneycombSite) return [ HoneycombSite(s.R + [1, 0], s.c), HoneycombSite(s.R + [1, -1], s.c), HoneycombSite(s.R + [-1, 0], s.c), HoneycombSite(s.R + [-1, 1], s.c), HoneycombSite(s.R + [0, 1], s.c), HoneycombSite(s.R + [0, -1], s.c) ] end function reducesite(s::HoneycombSite, Lx::Int, Ly::Int) xnew = mod(s.R[1], 2 * Lx) return HoneycombSite( [xnew, mod(s.R[2] - div(xnew - s.R[1], 2), Ly)], s.c ) end function honeycomblattice(xmin::Integer, xmax::Integer, ymin::Integer, ymax::Integer) return TCI.IndexSet( [HoneycombSite([x, y], c) for c in 1:2, x in xmin:xmax, y in ymin:ymax][:] ) end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

3342

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

740

function displayhamiltonian( ax, H::Matrix{Float64}, lattice::TCI.IndexSet; vmax::Float64=maximum(abs.(H)), cm=get_cmap("Purples") ) ax.set_aspect(1) for s in lattice.fromint for n in neighbours(s) rs, rn = realspacecoordinates.([s, n]) ax.plot([rs[1], rn[1]], [rs[2], rn[2]], color="gray", linewidth=0.5) end end coords = realspacecoordinates.(lattice.fromint) for (i, ri) in enumerate(coords) for (j, rj) in enumerate(coords) value = H[i, j] / vmax ax.plot( [ri[1], rj[1]], [ri[2], rj[2]], color=cm(value), zorder=1 + value, alpha=0.5) end end end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

321

module QuanticsTCI using TensorCrossInterpolation import TensorCrossInterpolation as TCI import QuanticsGrids as QG import LinearAlgebra: rank import Base: sum export quanticscrossinterpolate, evaluate, sum, integral export cachedata, quanticsfouriermpo include("tciinterface.jl") include("fouriertransform.jl") end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

5034

module fourierimpl import TensorCrossInterpolation as TCI struct LagrangePolynomials{T} grid::Vector{T} baryweights::Vector{T} end function (P::LagrangePolynomials{T})(alpha::Int, x::T)::T where {T} if abs(x - P.grid[alpha+1]) >= 1e-14 return prod(x .- P.grid) * P.baryweights[alpha+1] / (x - P.grid[alpha+1]) else return one(T) end end function getChebyshevGrid(K::Int)::LagrangePolynomials{Float64} chebgrid = 0.5 * (1.0 .- cospi.((0:K) / K)) baryweights = [ prod(j == m ? 1.0 : 1.0 / (chebgrid[j+1] - chebgrid[m+1]) for m in 0:K) for j in 0:K ] return LagrangePolynomials{Float64}(chebgrid, baryweights) end function dftcoretensor( P::LagrangePolynomials{Float64}, alpha::Int, beta::Int, sigma::Int, tau::Int; sign::Float64 )::ComplexF64 x = (sigma + P.grid[beta+1]) / 2 return P(alpha, x) * cispi(2 * sign * x * tau) end end @doc raw""" function quanticsfouriermpo( R::Int; sign::Float64=-1.0, maxbonddim::Int=12, tolerance::Float64=1e-14, K::Int=25, method::Symbol=:SVD, normalize::Bool=true )::TCI.TensorTrain{ComplexF64} Generate a quantics Fourier transform operator in tensor train form. When contracted with a quantics tensor train ``F_{\boldsymbol{\sigma}}`` representing a function, the result will be the fourier transform of the function in quantics tensor train form, ``\tilde{F}_{\boldsymbol{\sigma}'} = \sum_{\boldsymbol{\sigma}} F_{\boldsymbol{\sigma}} \exp(-2\pi i (k_{\boldsymbol{\sigma'}}-1) (m_{\boldsymbol{\sigma}} - 1)/M)``, where ``k_{\boldsymbol{\sigma}} = \sum_{\ell=1}^R 2^{R-\ell} \sigma_\ell``, ``m_{\boldsymbol{\sigma}'} = \sum_{\ell=1}^R 2^{R-\ell} \sigma'_\ell``, and ``M=2^R``. !!! note "Index ordering" Before the Fourier transform, the left most index corresponds to ``\sigma_1``, which describes the largest length scale, and the right most index corresponds to ``\sigma_R``, which describes the smallest length scale. The indices ``\sigma_1' \ldots \sigma_{R}'`` in the fourier transformed QTT are aligned in the *inverse* order; that is, the left most index corresponds to ``\sigma'_R``, which describes the smallest length scale. This allows construction of an operator with small bond dimension (see reference 1). If necessary, a call to `TCI.reverse(tt)` can restore large-to-small index ordering. The Fourier transform operator is implemented using a direct analytic construction of the tensor train by Chen and Lindsey (see reference 2). The tensor train thus obtained is then re-compressed to the user-given bond dimension and tolerance. Arguments: - `R`: number of bits of the fourier transform. - `sign`: sign in the exponent ``\exp(2i\pi \times \mathrm{sign} \times (k_{\boldsymbol{\sigma'}}-1) (x_{\boldsymbol{\sigma}}-1)/M)``, usually ``\pm 1``. - `maxbonddim`: bond dimension to compress the operator to. From observations, `maxbonddim = 12` is generally big enough to reach an accuracy of `1e-12`. - `tolerance`: tolerance of the TT compression. Note that the error in the fourier transform is generally a bit larger than this error tolerance. - `K`: bond dimension of the TT before compression, i.e. number of basis functions to approximate the Fourier transform with (see reference 2). The TT will become inaccurate for `K < 22`; higher values may be necessary for very high precision. - `method`: method with which to compress the TT. Choose between `:SVD` and `:CI`. - `normalize`: whether or not to normalize the operator as an isometry. !!! details "References" 1. [J. Chen, E. M. Stoudenmire, and S. R. White, Quantum Fourier Transform Has Small Entanglement, PRX Quantum 4, 040318 (2023).](https://link.aps.org/doi/10.1103/PRXQuantum.4.040318) 2. [J. Chen and M. Lindsey, Direct Interpolative Construction of the Discrete Fourier Transform as a Matrix Product Operator, arXiv:2404.03182.](http://arxiv.org/abs/2404.03182) """ function quanticsfouriermpo( R::Int; sign::Float64=-1.0, tolerance::Float64=1e-14, maxbonddim::Int=12, K::Int=25, method::Symbol=:SVD, normalize::Bool=true )::TCI.TensorTrain{ComplexF64} P = fourierimpl.getChebyshevGrid(K) A = [ fourierimpl.dftcoretensor(P, alpha, beta, sigma, tau; sign) for alpha in 0:K, tau in [0, 1], sigma in [0, 1], beta in 0:K ] Afirst = reshape(sum(A, dims=1), (1, 2, 2, K + 1)) Alast = reshape(A[:, :, :, 1], (K + 1, 2, 2, 1)) tt = TCI.TensorTrain{ComplexF64,4}([Afirst, fill(A, R - 2)..., Alast]) TCI.compress!(tt, method; tolerance, maxbonddim) if normalize for t in tt.sitetensors t ./= sqrt(2.0) end end return tt end function swaphalves!(tt::TCI.TensorTrain{V,3}) where {V} tt.sitetensors[1][:, :, :] = tt.sitetensors[1][:, [2, 1], :] nothing end function swaphalves(tt::TCI.AbstractTensorTrain{V}) where {V} ttcopy = TCI.tensortrain(deepcopy(TCI.sitetensors(tt))) swaphalves!(ttcopy) return ttcopy end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

code

10176

struct QuanticsTensorCI2{ValueType} tci::TensorCrossInterpolation.TensorCI2{ValueType} grid::QG.Grid quanticsfunction::TCI.CachedFunction{ValueType} end function evaluate( qtci::QuanticsTensorCI2{ValueType}, indices::Union{Array{Int},NTuple{N,Int}} )::ValueType where {N,ValueType} bitlist = QG.grididx_to_quantics(qtci.grid, Tuple(indices)) return TensorCrossInterpolation.evaluate(qtci.tci, bitlist) end function evaluate(qtci::QuanticsTensorCI2{V}, indices::Int...)::V where {V} return evaluate(qtci, collect(indices)::Vector{Int}) end function (qtci::QuanticsTensorCI2{V})(indices)::V where {V} return evaluate(qtci, indices) end function (qtci::QuanticsTensorCI2{V})(indices::Int...)::V where {V} return evaluate(qtci, indices...) end function sum(qtci::QuanticsTensorCI2{V})::V where {V} return sum(qtci.tci) end function integral(qtci::QuanticsTensorCI2{V})::V where {V} return sum(qtci) * prod(QG.grid_step(qtci.grid)) end function cachedata(qtci::QuanticsTensorCI2{V}) where {V} return Dict( QG.quantics_to_origcoord(qtci.grid, k) => v for (k, v) in TCI.cachedata(qtci.quanticsfunction) ) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, grid::QuanticsGrids.Grid{n}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; nrandominitpivot=5, kwargs... ) where {ValueType} Interpolate a function ``f(\mathbf{x})`` as a quantics tensor train. The tensor train itself is constructed using the 2-site tensor cross interpolation algorithm implemented in [`TensorCrossInterpolation.crossinterpolate2`](https://tensor4all.github.io/TensorCrossInterpolation.jl/dev/documentation/#TensorCrossInterpolation.crossinterpolate2-Union{Tuple{N},%20Tuple{ValueType},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}}},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}},%20Vector{Vector{Int64}}}}%20where%20{ValueType,%20N}). Arguments: - `ValueType` is the return type of `f`. Automatic inference is too error-prone. - `f` is the function to be interpolated. `f` may take multiple arguments. The return type should be `ValueType`. - `grid` is a `Grid{n}` object from [`QuanticsGrids.jl`](https://github.com/tensor4all/QuanticsGrids.jl) that describes a d-dimensional grid of discrete points indexed by binary digits. To avoid constructing a grid explicitly, use one of the other overloads. - `initialpivots` is a vector of pivots to be used for initialization. - `nrandominitpivot` determines how many random pivots should be used for initialization if no initial pivot is given. All other arguments are forwareded to `crossinterpolate2`. Most importantly: - `tolerance::Float64` is a float specifying the target tolerance for the interpolation. Default: `1e-8`. - `pivottolerance::Float64` is a float that specifies the tolerance for adding new pivots, i.e. the truncation of tensor train bonds. It should be <= tolerance, otherwise convergence may be impossible. Default: `tolerance`. - `maxbonddim::Int` specifies the maximum bond dimension for the TCI. Default: `typemax(Int)`, i.e. effectively unlimited. - `maxiter::Int` is the maximum number of iterations (i.e. optimization sweeps) before aborting the TCI construction. Default: `200`. For all other arguments, see the documentation for [`TensorCrossInterpolation.crossinterpolate2`](https://tensor4all.github.io/TensorCrossInterpolation.jl/dev/documentation/#TensorCrossInterpolation.crossinterpolate2-Union{Tuple{N},%20Tuple{ValueType},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}}},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}},%20Vector{Vector{Int64}}}}%20where%20{ValueType,%20N}). """ function quanticscrossinterpolate( ::Type{ValueType}, f, grid::QG.Grid{n}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; nrandominitpivot=5, kwargs... ) where {ValueType,n} R = grid.R qlocaldimensions = if grid.unfoldingscheme === :interleaved fill(2, n * R) else fill(2^n, R) end qf_ = (n == 1 ? q -> f(only(QG.quantics_to_origcoord(grid, q))) : q -> f(QG.quantics_to_origcoord(grid, q)...)) qf = TCI.CachedFunction{ValueType}(qf_, qlocaldimensions) qinitialpivots = (initialpivots === nothing ? [ones(Int, length(qlocaldimensions))] : [QG.grididx_to_quantics(grid, Tuple(p)) for p in initialpivots]) # For stabity kwargs_ = Dict{Symbol,Any}(kwargs) if !(:nsearchglobalpivot ∈ keys(kwargs)) kwargs_[:nsearchglobalpivot] = 5 end if !(:strictlynested ∈ keys(kwargs)) kwargs_[:strictlynested] = false end # random initial pivot for _ in 1:nrandominitpivot pivot = [rand(1:d) for d in qlocaldimensions] push!( qinitialpivots, TensorCrossInterpolation.optfirstpivot(qf, qlocaldimensions, pivot) ) end qtt, ranks, errors = TensorCrossInterpolation.crossinterpolate2( ValueType, qf, qlocaldimensions, qinitialpivots; kwargs_...) return QuanticsTensorCI2{ValueType}(qtt, grid, qf), ranks, errors end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector{<:AbstractVector}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; unfoldingscheme::Symbol=:interleaved, nrandominitpivot=5, kwargs... ) where {ValueType} Interpolate a function ``f(\mathbf{x})`` as a quantics tensor train. This overload automatically constructs a Grid object from the ``\mathbf{x}`` points given in `xvals`. Arguments: - `xvals::AbstractVector{<:AbstractVector}`: A set of discrete points where `f` can be evaluated, given as a set of arrays, where `xvals[i]` describes the `i`th axis. Each array in `xvals` should contain `2^R` points for some integer `R`. - For all other arguments, see the documentation of the main overload. """ function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector{<:AbstractVector}, initialpivots::Union{Nothing,AbstractVector{<:AbstractVector}}=nothing; unfoldingscheme::Symbol=:interleaved, nrandominitpivot=5, kwargs... ) where {ValueType} localdimensions = log2.(length.(xvals)) if !allequal(localdimensions) throw(ArgumentError( "This method only supports grids with equal number of points in each direction. If you need a different grid, please use index_to_quantics and quantics_to_index and determine the index ordering yourself.")) elseif !all(isinteger.(localdimensions)) throw(ArgumentError("This method only supports grid sizes that are powers of 2.")) end n = length(localdimensions) R = Int(first(localdimensions)) grid = QG.DiscretizedGrid{n}(R, Tuple(minimum.(xvals)), Tuple(maximum.(xvals)); unfoldingscheme=unfoldingscheme, includeendpoint=true) return quanticscrossinterpolate(ValueType, f, grid, initialpivots; nrandominitpivot=nrandominitpivot, kwargs...) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector, initialpivots::AbstractVector=[1]; kwargs... ) where {ValueType} Interpolate a function ``f(x)`` as a quantics tensor train. This is an overload for 1d functions. For an explanation of arguments and return type, see the documentation of the main overload. """ function quanticscrossinterpolate( ::Type{ValueType}, f, xvals::AbstractVector, initialpivots::AbstractVector=[1]; nrandominitpivot=5, kwargs... ) where {ValueType} return quanticscrossinterpolate( ValueType, f, [xvals], [initialpivots]; nrandominitpivot=nrandominitpivot, kwargs...) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, size::NTuple{d,Int}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; unfoldingscheme::Symbol=:interleaved, kwargs... ) where {ValueType,d} Interpolate a function ``f(\mathbf{x})`` as a quantics tensor train. This overload automatically constructs a Grid object using the information contained in `size`. Here, the `i`th argument runs from `1` to `size[i]`. """ function quanticscrossinterpolate( ::Type{ValueType}, f, size::NTuple{d,Int}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; unfoldingscheme::Symbol=:interleaved, kwargs... ) where {ValueType,d} localdimensions = log2.(size) if !allequal(localdimensions) throw(ArgumentError( "This method only supports grids with equal number of points in each direction. If you need a different grid, please use index_to_quantics and quantics_to_index and determine the index ordering yourself.")) elseif !all(isinteger.(localdimensions)) throw(ArgumentError("This method only supports grid sizes that are powers of 2.")) end R = Int(first(localdimensions)) grid = QG.InherentDiscreteGrid{d}(R; unfoldingscheme=unfoldingscheme) return quanticscrossinterpolate(ValueType, f, grid, initialpivots; kwargs...) end @doc raw""" function quanticscrossinterpolate( ::Type{ValueType}, f, size::NTuple{d,Int}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; unfoldingscheme::Symbol=:interleaved, kwargs... ) where {ValueType,d} Interpolate a Tensor ``F`` as a quantics tensor train. For an explanation of arguments, etc., see the documentation of the main overload. """ function quanticscrossinterpolate( F::Array{ValueType,d}, initialpivots::AbstractVector{<:AbstractVector}=[ones(Int, d)]; kwargs... ) where {ValueType,d} return quanticscrossinterpolate( ValueType, (i...) -> F[i...], size(F), initialpivots; kwargs...) end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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using QuanticsTCI using Test using LinearAlgebra include("test_with_aqua.jl") include("test_with_jet.jl") include("test_tciinterface.jl") include("test_fouriertransform.jl") include("test_samplescripts.jl")

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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import QuanticsTCI as QTCI import TensorCrossInterpolation as TCI import QuanticsGrids as QG import Random @testset "Quantics Fourier Transform, R=$R" for R in [4, 16, 62] Random.seed!(23593243) r = 12 coeffs = randn(ComplexF64, r) fm(x) = sum(coeffs .* cispi.(2 * (0:r-1) * x)) fq(q) = fm((QG.quantics_to_index_fused(q)[1] - 1) / 2^big(R)) qtci, = TCI.crossinterpolate2(ComplexF64, fq, fill(2, R); tolerance=1e-14) fouriertt = QTCI.quanticsfouriermpo(R; normalize=false) / 2^big(R) qtcif = TCI.contract(fouriertt, qtci) for i in 1:min(r, 2^big(R)) q = QG.index_to_quantics(i, numdigits=R) @test qtcif(reverse(q)) ≈ coeffs[i] end for i in Int.(round.(range(r+2, 2^big(R); length=100))) q = QG.index_to_quantics(i, numdigits=R) @test abs(qtcif(reverse(q))) < 1e-12 end end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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using Test using Glob # Directory where the script files are located script_dir = joinpath(@__DIR__, "samplescripts") # Get all .jl files in the directory script_files = glob("*.jl", script_dir) # Function to execute a script using a new Julia process function run_script_in_new_process(script_file) cmd = `julia --project=@. --startup-file=no $script_file` result = read(cmd, String) # Run the command and capture the output return result end # Test each script file @testset "External Script Tests" begin for script_file in script_files @testset "Testing $script_file" begin try run_script_in_new_process(script_file) @test true # If no error occurs catch e @test false # If an error occurs println("Error in $script_file: ", e) end end end end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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using Random import QuanticsGrids as QG @testset "quanticscrossinterpolate" for unfoldingscheme in [ :interleaved, :fused ] f(x, y) = 0.1 * x^2 + 0.01 * y^3 - pi * x * y + 5 xvals = range(-3, 2; length=32) yvals = range(-17, 12; length=32) qtt, ranks, errors = quanticscrossinterpolate( Float64, f, [xvals, yvals]; unfoldingscheme, tolerance=1e-8) @test last(errors) < 1e-8 cache = cachedata(qtt) for (k, v) in cache @test v ≈ f(k...) end for (i, x) in enumerate(xvals) for (j, y) in enumerate(yvals) @test f(x, y) ≈ qtt(i, j) end end @test sum(qtt) ≈ sum(f.(xvals, transpose(yvals))) end @testset "quanticscrossinterpolate, 1d overload" begin f(x) = 0.1 * x^2 - pi * x + 2 g(x) = f(x[1]) xvals = range(-3, 2; length=128) Random.seed!(1234) qttf, ranksf, errorsf = quanticscrossinterpolate(Float64, f, xvals; tolerance=1e-8) Random.seed!(1234) qttg, ranksg, errorsg = quanticscrossinterpolate(Float64, g, [xvals]; tolerance=1e-8) @test last(errorsf) < 1e-8 @test last(errorsg) < 1e-8 @test ranksf == ranksg @test errorsf == errorsg for (i, x) in enumerate(xvals) @test f(x) == g([x]) @test f(x) ≈ qttf(i) @test g([x]) ≈ qttg([i]) end end @testset "quanticscrossinterpolate with DiscretizedGrid" for unfoldingscheme in [ :interleaved, :fused ] R = 5 f(x, y) = 0.1 * x^2 + 0.01 * y^3 - pi * x * y + 5 grid = QG.DiscretizedGrid{2}( R, (-3, -17), (2, 12); unfoldingscheme ) Random.seed!(1234) qtt, ranks, errors = quanticscrossinterpolate(Float64, f, grid; tolerance=1e-8) @test last(errors) < 1e-8 for i in 1:2^R for j in 1:2^R @test f(QG.grididx_to_origcoord(grid, (i, j))...) ≈ qtt(i, j) end end end @testset "quanticscrossinterpolate with InherentDiscreteGrid" for unfoldingscheme in [ :interleaved, :fused ] R = 3 Random.seed!(1234) A = rand(2^R, 2^R, 2^R) grid = QG.InherentDiscreteGrid{3}(R; unfoldingscheme) qtt, ranks, errors = quanticscrossinterpolate( Float64, (i...) -> A[i...], grid; tolerance=1e-8) @test last(errors) < 1e-8 for i in CartesianIndices(size(A)) @test A[i] ≈ qtt(Tuple(i)) end qtt, ranks, errors = quanticscrossinterpolate( Float64, (i...) -> A[i...], size(A); unfoldingscheme, tolerance=1e-8) @test last(errors) < 1e-8 for i in CartesianIndices(size(A)) @test A[i] ≈ qtt(Tuple(i)) end qtt, ranks, errors = quanticscrossinterpolate( A; unfoldingscheme, tolerance=1e-8) @test last(errors) < 1e-8 for i in CartesianIndices(size(A)) @test A[i] ≈ qtt(Tuple(i)) end end @testset "quanticscrossinterpolate for integrals" begin R = 40 xgrid = QG.DiscretizedGrid{1}(R, 0, 1) F(x) = sin(1/(x^2 + 0.01)) f(x) = -2*x * cos(1/(x^2 + 0.01)) / (x^2 + 0.01)^2 tci, ranks, errors = quanticscrossinterpolate(Float64, f, xgrid; tolerance=1e-13) @test sum(tci) * QG.grid_step(xgrid) ≈ F(1) - F(0) @test integral(tci) ≈ F(1) - F(0) end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

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using Aqua import QuanticsTCI @testset "Aqua" begin Aqua.test_all(QuanticsTCI; ambiguities = false, unbound_args = false, deps_compat = false) end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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using JET import QuanticsTCI @testset "JET" begin if VERSION ≥ v"1.9" JET.test_package(QuanticsTCI; target_defined_modules=true) end end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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using QuanticsTCI import QuanticsGrids as QG R = 40 # Number of bits <@$\cR$@> M = 2^R # Number of discretization points <@$M$@> xgrid = QG.DiscretizedGrid{1}(R, -10, 10) # Discretization grid <@$x(\bsigma)$@> function f(x) # Function of interest <@$f(x)$@> return ( sinc(x) + 3 * exp(-0.3 * (x - 4)^2) * sinc(x - 4) - cos(4 * x)^2 - 2 * sinc(x + 10) * exp(-0.6 * (x + 9)) + 4 * cos(2 * x) * exp(-abs(x + 5)) + 6 * 1 / (x - 11) + sqrt(abs(x)) * atan(x / 15)) end # Construct and optimize quantics TCI <@$\tf_\bsigma$@> f_tci, ranks, errors = quanticscrossinterpolate(Float64, f, xgrid; maxbonddim=12) # Print a table to compare <@$f(x)$@> and <@$\tF_\bsigma$@> on some regularly spaced points println("x\t f(x)\t\t\t f_tt(x)") for m in 1:2^(R-5):M x = QG.grididx_to_origcoord(xgrid, m) println("$x\t$(f(x))\t$(f_tci(m))") end

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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using QuanticsTCI import QuanticsGrids as QG R = 40 # Number of bits <@$\cR$@> xygrid = QG.DiscretizedGrid{2}(R, (-5.0, -5.0), (5.0, 5.0)) # Discretization grid <@$\vec{x}(\bsigma)$@> function f(x, y) # Function of interest <@$f(x)$@> return exp(-0.4*(x^2 + y^2)) + 1 + sin(x * y) * exp(-x^2) + cos(3*x*y) * exp(-y ^ 2) + cos(x+y) end # Construct and optimize quantics TCI <@$\tf_\bsigma$@> f_tci, ranks, errors = quanticscrossinterpolate(Float64, f, xygrid; tolerance=1e-10) # Print a table to compare <@$f(x)$@> and <@$\tF_\bsigma$@> on some regularly spaced points println("x\t y\t f(x)\t\t\t f_tt(x)") for index in CartesianIndices((10, 10)) m = Tuple(index) .* div(2^R, 10) x, y = QG.grididx_to_origcoord(xygrid, m) println("$x\t$y\t$(f(x, y))\t$(f_tci(m))") end println("Value of the integral: $(integral(f_tci))")

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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using QuanticsTCI import TensorCrossInterpolation as TCI # Number of bits R = 8 # Replace with your dataset grid = range(-pi, pi; length=2^R+1)[1:end-1] # exclude the end point dataset = [cos(x) + cos(y) + cos(z) for x in grid, y in grid, z in grid] # Perform QTCI tolerance = 1e-5 qtt, ranks, errors = quanticscrossinterpolate( dataset, tolerance=tolerance, unfoldingscheme=:fused) # Check error qttdataset = [qtt([i, j, k]) for i in axes(grid, 1), j in axes(grid, 1), k in axes(grid, 1)] error = abs.(qttdataset .- dataset) println( "Quantics TCI compression of the dataset with tolerance $tolerance has " * "link dimensions $(TCI.linkdims(qtt.tci)), for a max error of $(maximum(error))." )

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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import TensorCrossInterpolation as TCI # Replace this line with the dataset to be tested for compressibility. grid = range(-pi, pi; length=200) dataset = [cos(x) + cos(y) + cos(z) for x in grid, y in grid, z in grid] # Construct TCI tolerance = 1e-5 tt, ranks, errors = TCI.crossinterpolate2( Float64, i -> dataset[i...], collect(size(dataset)), tolerance=tolerance) # Check error ttdataset = [tt([i, j, k]) for i in axes(grid, 1), j in axes(grid, 1), k in axes(grid, 1)] errors = abs.(ttdataset .- dataset) println( "TCI of the dataset with tolerance $tolerance has link dimensions $(TCI.linkdims(tt)), " * "for a max error of $(maximum(errors))." )

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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import TensorCrossInterpolation as TCI import Random import QuanticsGrids as QD #using PythonPlot: pyplot as plt # Number of bits R = 4 tol = 1e-4 # f(q) = 1 if q = (1, 1, ..., 1) or q = (2, 2, ..., 2), 0 otherwise f(q) = (all(q .== 1) || all(q .== 2)) ? 1.0 : 0.0 localdims = fill(2, R) # Perform TCI with an initial pivot at (1, 1, ..., 1) firstpivot = ones(Int, R) tci, ranks, errors = TCI.crossinterpolate2( Float64, f, localdims, [firstpivot]; tolerance=tol, nsearchglobalpivot=0 # Disable automatic global pivot search ) # TCI fails to capture the function at (2, 2, ..., 2) globalpivot = fill(2, R) @assert isapprox(TCI.evaluate(tci, globalpivot), 0.0) # Add (2, 2, ..., 2) as a global pivot tci_globalpivot = deepcopy(tci) TCI.addglobalpivots2sitesweep!( tci_globalpivot, f, [globalpivot], tolerance=tol ) @assert isapprox(TCI.evaluate(tci_globalpivot, globalpivot), 1.0) # Plot the function and the TCI reconstructions grid = QD.InherentDiscreteGrid{1}(R) ref = [f(QD.grididx_to_quantics(grid, i)) for i in 1:2^R] reconst_tci = [tci(QD.grididx_to_quantics(grid, i)) for i in 1:2^R] reconst_tci_globalpivot = [tci_globalpivot(QD.grididx_to_quantics(grid, i)) for i in 1:2^R] #== fig, ax = plt.subplots() ax.plot(ref, label="ref", marker="", linestyle="--") ax.plot(reconst_tci, label="TCI without global pivot", marker="x", linestyle="") ax.plot(reconst_tci_globalpivot, label="TCI with global pivot", marker="+", linestyle="") ax.set_title("Adding global pivot") ax.set_xlabel("Index") ax.legend() fig.savefig("global_pivot.pdf") ==#

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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import TensorCrossInterpolation as TCI N = 5 # Number of dimensions <@$\cN$@> tolerance = 1e-10 # Tolerance of the internal TCI GKorder = 15 # Order of the Gauss-Kronrod rule to use f(x) = 2^N / (1 + 2 * sum(x)) # Integrand integralvalue = TCI.integrate(Float64, f, fill(0.0, N), fill(1.0, N); tolerance, GKorder) # Exact value of integral for <@$\cN = 5$@> i5 = (-65205 * log(3) - 6250 * log(5) + 24010 * log(7) + 14641 * log(11)) / 24 error = abs(integralvalue - i5) @info "TCI integration with GK$GKorder: " integralvalue i5 error

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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import QuanticsGrids as QG import TensorCrossInterpolation as TCI N = 5 # Number of dimensions <@$\cN$@> tolerance = 1e-10 # Tolerance of the internal TCI R = 40 # Number of bits <@$\cR$@> f(x) = 2^N / (1 + 2 * sum(x)) # Integrand <@$f(\vec{x})$@> # Discretization grid with <@$2^{\scN \scR}$@> points grid = QG.DiscretizedGrid{N}(R, Tuple(fill(0.0, N)), Tuple(fill(1.0, N)), unfoldingscheme=:interleaved) quanticsf(sigma) = f(QG.quantics_to_origcoord(grid, sigma)) # <@$f(\vec{x}(\bsigma))$@> # Obtain the QTCI representation and evaluate the integral via factorized sum tci, ranks, errors = TCI.crossinterpolate2(Float64, quanticsf, QG.localdimensions(grid); tolerance) # Integral is sum multiplied with discretization volumne integralvalue = TCI.sum(tci) * prod(QG.grid_step(grid)) # Exact value of integral for <@$\cN = 5$@> i5 = (-65205 * log(3) - 6250 * log(5) + 24010 * log(7) + 14641 * log(11)) / 24 error = abs(integralvalue - i5) # Error for <@$\cN = 5$@> @info "Quantics TCI integration with R=$R: " integralvalue i5 error

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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# QuanticsTCI [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://tensor4all.github.io/QuanticsTCI.jl/dev) [![CI](https://github.com/tensor4all/QuanticsTCI.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/tensor4all/QuanticsTCI.jl/actions/workflows/CI.yml) This module contains utilities for interpolations of functions in the quantics TCI / quantics tensor train (QTT) format. It is a small wrapper around [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl) and [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) with more convenient functionality intended to cover the most common use cases. For more advanced or unusual use cases, it is likely that you will need to rely on those two libraries directly. ## Installation This module has been registered in the General registry. It can be installed by typing the following in a Julia REPL: ```julia using Pkg; Pkg.add("QuanticsTCI.jl") ``` This module depends on: - [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl) - [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) ## Usage *This section only contains the bare minimum to get you started. More examples, including more advanced use cases, can be found in the [the tensor4all website](https://tensor4all.github.io). For a documentation of the API, see the [package documentation](https://tensor4all.github.io/QuanticsTCI.jl/dev/index.html).* The easiest way to construct a quantics tensor train is the `quanticscrossinterpolate` function. For example, the function `f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y)` can be interpolated as follows. ```julia using QuanticsTCI f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y) xvals = range(-6, 6; length=256) yvals = range(-12, 12; length=256) qtt, ranks, errors = quanticscrossinterpolate(Float64, f, [xvals, yvals]; tolerance=1e-8) ``` The output object `qtt` now represents a quantics tensor train. It can then be evaluated a function of indices enumerating the `xvals` and `yvals` arrays: ```julia @show qttvalue = qtt(212, 92) @show truevalue = f(xvals[212], yvals[92]) @show error = abs(qttvalue - truevalue) ``` Output: ``` qttvalue = qtt(212, 92) = -0.2525252152789011 truevalue = f(xvals[212], yvals[92]) = -0.2525252152789314 error = abs(qttvalue - truevalue) = 3.0309088572266774e-14 ``` The output shows that the approximation has an error of only `3 * 10^-14` at `[212, 92]`. This example is continued in the [package documentation](https://tensor4all.github.io/QuanticsTCI.jl/dev/index.html), and more examples can be found in the [the tensor4all website](https://tensor4all.github.io). ## Related libraries - [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl) to calculate tensor cross interpolations. - [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library. - [ITensors.jl](https://github.com/ITensor/ITensors.jl) for MPS / MPO algorithms. ## References - M. K. Ritter, Y. N. Fernández, M. Wallerberger, J. von Delft, H. Shinaoka, and X. Waintal, *Quantics Tensor Cross Interpolation for High-Resolution, Parsimonious Representations of Multivariate Functions in Physics and Beyond*, [arXiv:2303.11819](http://arxiv.org/abs/2303.11819), [Phys. Rev. Lett. 132, 056501 (2024)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.056501).

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

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docs

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# Documentation ```@autodocs Modules = [QuanticsTCI] ```

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

[ "MIT" ]

0.7.0

2724301b6bc6390d8d43d210cecd7dbaabe519d5

docs

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# QuanticsTCI.jl user guide ```@meta CurrentModule = QuanticsTCI ``` This module allows easy translation of functions to quantics representation. It meshes well with the `TensorCrossInterpolation.jl` module, together with which it provides quantics TCI functionality. # Quickstart The easiest way to construct a quantics tensor train is the `quanticscrossinterpolate` function. For example, the function ``f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y)`` can be interpolated as follows. ```@example simple using QuanticsTCI f(x, y) = (cos(x) - cos(x - 2y)) * abs(x + y) xvals = range(-6, 6; length=256) yvals = range(-12, 12; length=256) qtt, ranks, errors = quanticscrossinterpolate(Float64, f, [xvals, yvals]; tolerance=1e-8) ``` The output object `qtt` now represents a quantics tensor train. It can then be evaluated a function of indices enumerating the `xvals` and `yvals` arrays. ```@example simple using Plots qttvals = qtt.(1:256, collect(1:256)') contour(xvals, yvals, qttvals, fill=true) xlabel!("x") ylabel!("y") savefig("simple.svg"); nothing # hide ``` ![](simple.svg) The convergence criterion can be controlled using the keywords `tolerance`, `pivottolerance`, and `maxbonddim`. - `tolerance` is the value of the error estimate at which the optimization algorithm will stop. - `pivottolerance` is the threshold at which each local optimization will truncate the bond. - `maxbonddim` sets the maximum bond dimension along the links. A common default setting is to control convergence using `tolerance`, and to set `pivottolerance` equal or slightly smaller than that. Specifying `maxbonddim` can be useful as a safety. However, if `maxbonddim` is set, one should check the error estimate for convergence afterwards. In the following example, we specify all 3 parameters, but set `maxbonddim` too small. ```@example simple qtt, ranks, errors = quanticscrossinterpolate( Float64, f, [xvals, yvals]; tolerance=1e-8, pivottolerance=1e-8, maxbonddim=8) print(last(errors)) qttvals = qtt.(1:256, collect(1:256)') contour(xvals, yvals, qttvals, fill=true) xlabel!("x") ylabel!("y") savefig("simpletrunc.svg"); nothing # hide ``` ![](simpletrunc.svg) The plot shows obvious noise due to the insufficient maximum bond dimension. Accordingly, the error estimate of ``0.08`` shows that convergence has not been reached, and an increase of the maximum bond dimension is necessary. # Further reading - See the API Reference for all variants of calling [`quanticscrossinterpolate`](@ref). - If you are having trouble with convergence / efficiency of the TCI, you might have to tweak some of its options. All keyword arguments are forwarded to `TensorCrossInterpolation.crossinterpolate2()` internally. See its [documentation](https://tensor4all.github.io/TensorCrossInterpolation.jl/dev/documentation/#TensorCrossInterpolation.crossinterpolate2-Union{Tuple{N},%20Tuple{ValueType},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}}},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}},%20Vector{Vector{Int64}}}}%20where%20{ValueType,%20N}) for further information. - If you intend to work directly with the quantics representation, [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) is useful for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library.

QuanticsTCI

https://github.com/tensor4all/QuanticsTCI.jl.git

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function check_hsg() assert_clean_working_directory() run_hsg() assert_clean_working_directory() return nothing end function assert_clean_working_directory() if !isempty(strip(read(`git status --short`, String))) msg = "The working directory is dirty" @error msg println("Output of `git status`:") println(strip(read(`git status`, String))) run(`git add -A`) println("Output of `git diff HEAD`:") println(strip(read(`git diff HEAD`, String))) run(`git reset`) throw(ErrorException(msg)) else @info "The working directory is clean" return nothing end end function run_hsg() env2 = copy(ENV) delete!(env2, "JULIA_DEPOT_PATH") delete!(env2, "JULIA_LOAD_PATH") delete!(env2, "JULIA_PROJECT") env2["JULIA_DEPOT_PATH"] = mktempdir(; cleanup = true) julia_binary = Base.julia_cmd().exec[1] hsg_directory = joinpath("ext", "HistoricalStdlibGenerator") hsg_generate_file = joinpath(hsg_directory, "generate_historical_stdlibs.jl") cmd_1 = `$(julia_binary) --project=$(hsg_directory) -e 'import Pkg; Pkg.instantiate()'` cmd_2 = `$(julia_binary) --project=$(hsg_directory) --threads $(min(Sys.CPU_THREADS, 8)) $(hsg_generate_file)` run(setenv(cmd_1, env2)) run(setenv(cmd_2, env2)) return nothing end check_hsg()

HistoricalStdlibVersions

https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git

[ "MIT" ]

2.0.0

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code

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#!/usr/bin/env julia using Downloads, JSON3, Base.BinaryPlatforms, Scratch, SHA, Pkg, TOML using Base: UUID # Download versions.json, start iterating over Julia versions versions_json_url = "https://julialang-s3.julialang.org/bin/versions.json" num_concurrent_downloads = 8 @info("Downloading versions.json...") json_buff = IOBuffer() Downloads.download(versions_json_url, json_buff) versions_json = JSON3.read(String(take!(json_buff))) # Collect all versions that are >= 1.0.0, and are a stable release versions = filter(versions_json) do (v, d) if VersionNumber(string(v)) < v"1.0.0" return false end if !d["stable"] return false end return true end # Build download URLs for each one, and then tack on the next release as well function select_url_hash(data, host = HostPlatform()) d = first(filter(f -> platforms_match(f.triplet, host), data.files)) return (d.url, d.sha256) end version_urls = sort(select_url_hash.(values(versions)), by = pair -> pair[1]) function generate_nightly_url(jlver, host = HostPlatform()) # Map arch arch_str = Dict("x86_64" => "x64", "i686" => "x86", "aarch64" => "aarch64", "armv7l" => "armv7l", "ppc64le" => "ppc64le")[arch(host)] # Map OS name os_str = Dict("linux" => "linux", "windows" => "winnt", "macos" => "mac", "freebsd" => "freebsd")[os(host)] # Map wordsize tag wordsize_str = Dict("x86_64" => "64", "i686" => "32", "aarch64" => "aarch64", "armv7l" => "armv7l", "ppc64le" => "ppc64")[arch(host)] # If `jlver` is nothing, we don't namespace by version and just get the absolute latest version ver_str = "" if jlver !== nothing ver_str = string(jlver.major, ".", jlver.minor, "/") end return string( "https://julialangnightlies-s3.julialang.org/bin/", # linux/ os_str, "/", # x64/ arch_str, "/", # 1.6/ (or nothing, if `jlver === nothing`) ver_str, "julia-latest-", # linux64 os_str, wordsize_str, ".tar.gz", ) end highest_release = maximum(VersionNumber.(string.(keys(versions)))) next_release = VersionNumber(highest_release.major, highest_release.minor + 1, 0) push!(version_urls, (generate_nightly_url(next_release), "")) push!(version_urls, (generate_nightly_url(nothing), "")) @info("Identified $(length(version_urls)) versions to try...") # Next, we're going to download each of these to a scratch space scratch_dir = @get_scratch!("julia_installers") # Ensure we always download the nightly nightly_url = last(version_urls)[1] nightly_url_tag = bytes2hex(sha256(nightly_url)) rm(joinpath(scratch_dir, string(nightly_url_tag, "-", basename(nightly_url))); force=true) # Helper function to print out stdlibs from a Julia installation function get_stdlibs(scratch_dir, julia_installer_name) installer_path = joinpath(scratch_dir, julia_installer_name) mktempdir() do dir @info("Extracting $(julia_installer_name)") mount_dir = joinpath(dir, "mount_dir") try if endswith(installer_path, ".dmg") mkdir(mount_dir) # Try to mount many times, as this seems to fail randomly mount_cmd = `hdiutil mount $(installer_path) -mountpoint $(mount_dir)` tries = 0 while !success(mount_cmd) if tries > 10 error("Unable to mount via hdiutil!") end sleep(0.1) tries += 1 end app_dir = first(filter(d -> startswith(basename(d), "Julia-"), readdir(mount_dir; join=true))) symlink(joinpath(app_dir, "Contents", "Resources", "julia", "bin"), joinpath(dir, "bin")) elseif endswith(installer_path, ".exe") error("This script doesn't work with `.exe` downloads") else run(`tar -C $(dir) --strip-components=1 -zxf $(installer_path)`) end jlexe = joinpath(dir, "bin", @static Sys.iswindows() ? "julia.exe" : "julia") jlflags = ["--startup-file=no", "-O0"] jlvers = VersionNumber(readchomp(`$(jlexe) $(jlflags) -e 'print(VERSION)'`)) jlvers = VersionNumber(jlvers.major, jlvers.minor, jlvers.patch) @info("Auto-detected Julia version $(jlvers)") if jlvers < v"1.1" stdlibs_str = readchomp(`$(jlexe) $(jlflags) -e 'import Pkg; print(repr(Pkg.Types.gather_stdlib_uuids()))'`) else stdlibs_str = readchomp(`$(jlexe) $(jlflags) -e 'import Pkg; print(repr(Pkg.Types.load_stdlib()))'`) end # This will give us a dictionary of UUID => (name, version, deps, weakdeps) mappings for all standard libraries stdlibs = Dict{Base.UUID, Tuple}() stdlib_path = readchomp(`$(jlexe) $(jlflags) -e 'import Pkg; print(Pkg.Types.stdlib_path(""))'`) stdlib_names = [isa(name, Tuple) ? first(name) : name for (_, name) in eval(Meta.parse(stdlibs_str))] for name in stdlib_names project_path = joinpath(stdlib_path, name, "Project.toml") version = nothing deps = UUID[] weakdeps = UUID[] if isfile(project_path) d = TOML.parsefile(project_path) uuid = Base.UUID(d["uuid"]) if haskey(d, "version") version = VersionNumber(d["version"]) end if haskey(d, "deps") deps = Base.UUID.(values(d["deps"])) end if haskey(d, "weakdeps") weakdeps = Base.UUID.(values(d["weakdeps"])) end end stdlibs[uuid] = (name, version, deps, weakdeps) end return (jlvers, stdlibs) finally # Clean up mounted directories if isdir(mount_dir) unmount_cmd = `hdiutil detach $(mount_dir)` tries = 0 while !success(unmount_cmd) if tries > 10 error("Unable to unmount $(mount_dir)") end sleep(0.1) tries += 1 end end end end end jobs = Channel() output = Channel() versions_dict = Dict() @sync begin # Feeder task Threads.@spawn begin for (url, hash) in version_urls put!(jobs, (url, hash)) end close(jobs) end # Consumer tasks work_tasks = Task[] for _ in 1:Threads.nthreads() task = Threads.@spawn begin for (url, hash) in jobs try # We might try to download two files that have the same basename url_tag = bytes2hex(sha256(url)) fname = joinpath(scratch_dir, string(url_tag, "-", basename(url))) if !isfile(fname) @info("Downloading $(url)") Downloads.download(url, fname) end if !isempty(hash) calc_hash = bytes2hex(open(io -> sha256(io), fname, "r")) if calc_hash != hash @error("Hash mismatch on $(fname); deleting and re-downloading") rm(fname; force=true) Downloads.download(url, fname) calc_hash = bytes2hex(open(io -> sha256(io), fname, "r")) if calc_hash != hash @error("Hash mismatch on $(fname); re-download failed!") continue end end end version, stdlibs = get_stdlibs(scratch_dir, basename(fname)) put!(output, (version, stdlibs)) catch e if isa(e, InterruptException) rethrow() end @error(e, exception=(e, catch_backtrace())) end end end push!(work_tasks, task) end # output-closing thread Threads.@spawn begin wait.(work_tasks) close(output) end # Collector task Threads.@spawn begin for (version, stdlibs) in output versions_dict[version] = stdlibs end end end # Next, drop versions that are the same as the one "before" them: sorted_versions = sort(collect(keys(versions_dict))) versions_to_drop = VersionNumber[] for idx in 2:length(sorted_versions) if versions_dict[sorted_versions[idx-1]] == versions_dict[sorted_versions[idx]] push!(versions_to_drop, sorted_versions[idx]) end end for v in versions_to_drop delete!(versions_dict, v) end # Next, figure out which stdlibs are actually unresolvable, because they've never been registered all_stdlibs = Dict{UUID,Tuple}() for (julia_ver, stdlibs) in versions_dict merge!(all_stdlibs, stdlibs) end registries = Pkg.Registry.reachable_registries() unregistered_stdlibs = filter(all_stdlibs) do (uuid, _) return !any(haskey(reg.pkgs, uuid) for reg in registries) end # Helper function for getting these printed out in a nicely-sorted order function print_sorted(io::IO, d::Dict; indent::Int=0) println(io, "Dict{UUID,StdlibInfo}(") for (uuid, (name, version, deps, weakdeps)) in sort(collect(d), by = kv-> kv[2][1]) println(io, " "^indent, repr(uuid), " => StdlibInfo(\n", " "^(indent + 4), repr(name), ",\n", " "^(indent + 4), repr(uuid), ",\n", " "^(indent + 4), repr(version), ",\n", " "^(indent + 4), repr(deps), ",\n", " "^(indent + 4), repr(weakdeps), ",\n", " "^indent, "),", ) end print(io, " "^(max(indent - 4, 0)), ")") end output_fname = joinpath(dirname(dirname(@__DIR__)), "src", "version_map.jl") @info("Outputting to $(output_fname)") sorted_versions = sort(collect(keys(versions_dict))) open(output_fname, "w") do io print(io, """ ## This file autogenerated by ext/HistoricalStdlibGenerator/generate_historical_stdlibs.jl # Julia standard libraries with duplicate entries removed so as to store only the # first release in a set of releases that all contain the same set of stdlibs. const STDLIBS_BY_VERSION = [ """) for v in sorted_versions print(io, " $(repr(v)) => ") print_sorted(io, versions_dict[v]; indent=8) println(io, ",") println(io) end println(io, "]") println(io) print(io, """ # Next, we also embed a list of stdlibs that must _always_ be treated as stdlibs, # because they cannot be resolved in the registry; they have only ever existed within # the Julia stdlib source tree, and because of that, trying to resolve them will fail. const UNREGISTERED_STDLIBS =""") print_sorted(io, unregistered_stdlibs; indent=4) end

HistoricalStdlibVersions

https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git

[ "MIT" ]

2.0.0

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""" HistoricalStdlibVersions Loads historical stdlib version information into Pkg to allow Pkg to resolve stdlib versions for prior julia versions. """ module HistoricalStdlibVersions using Pkg import Base: UUID # Use the `Pkg` `StdlibInfo` type if it exists, otherwise just re-define it if !isdefined(Pkg.Types, :StdlibInfo) struct StdlibInfo name::String uuid::UUID # This can be `nothing` if it's an unregistered stdlib version::Union{Nothing,VersionNumber} deps::Vector{UUID} weakdeps::Vector{UUID} end else import Pkg.Types: StdlibInfo end include("version_map.jl") let max_hsg_version = maximum(first.(STDLIBS_BY_VERSION)) # Throw a warning at compile-time if VERSION looks like it's a major or minor version ahead # of the latest version captured within `version_map.jl`. This assumes that we bump at least # one stdlib every minor release, which so far appears to be a safe bet. if VersionNumber(max_hsg_version.major, max_hsg_version.minor) < VersionNumber(VERSION.major, VERSION.minor) @warn("HistoricalStdlibVersions seems to be out of date; please open an issue at https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl/issues") end end function register!() if isdefined(Pkg.Types, :STDLIBS_BY_VERSION) unregister!() if isdefined(Pkg.Types, :StdlibInfo) # We can directly use the datatypes in this package append!(Pkg.Types.STDLIBS_BY_VERSION, STDLIBS_BY_VERSION) merge!(Pkg.Types.UNREGISTERED_STDLIBS, UNREGISTERED_STDLIBS) else # We have to convert our `StdlibInfo` types into the more limited (name, version) format # from earlier julias. Those julias are unable to resolve dependencies of stdlibs properly. for (version, stdlibs) in STDLIBS_BY_VERSION push!(Pkg.Types.STDLIBS_BY_VERSION, version => Dict{UUID,Tuple{String,Union{VersionNumber,Nothing}}}( uuid => (info.name, info.version) for (uuid, info) in stdlibs )) end function find_first_info(uuid) for (_, stdlibs) in STDLIBS_BY_VERSION for (stdlib_uuid, info) in stdlibs if stdlib_uuid == uuid return info end end end return nothing end for (uuid, info) in UNREGISTERED_STDLIBS Pkg.Types.UNREGISTERED_STDLIBS[uuid] = (info.name, nothing) end end end end function unregister!() empty!(Pkg.Types.STDLIBS_BY_VERSION) empty!(Pkg.Types.UNREGISTERED_STDLIBS) end function __init__() if get(ENV, "HISTORICAL_STDLIB_VERSIONS_AUTO_REGISTER", "true") == "true" register!() end end end # module HistoricalStdlibVersions

HistoricalStdlibVersions

https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git

[ "MIT" ]

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## This file autogenerated by ext/HistoricalStdlibGenerator/generate_historical_stdlibs.jl # Julia standard libraries with duplicate entries removed so as to store only the # first release in a set of releases that all contain the same set of stdlibs. const STDLIBS_BY_VERSION = [ v"1.0.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), nothing, UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.0.2" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.0.2", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.0.3" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.0.4" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.0.4", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.1.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.1.2", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.1.1" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.1.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.2.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.2.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.3.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.3.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.3.1" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.3.1", UUID[UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.4.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.4.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.5.0" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.5.3" => Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), ), v"1.6.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.3.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.35.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.3.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.5.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.1", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.2" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.3.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.3" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+3", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.4" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+9", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.5" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+10", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.6" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+10", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.6", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.6.7" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.4.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+3", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a83860b7-747b-57cf-bf1f-3e79990d037f") => StdlibInfo( "LibOSXUnwind_jll", UUID("a83860b7-747b-57cf-bf1f-3e79990d037f"), v"0.0.6+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.10+10", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.6.7", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.4.1+1", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.9.3", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"11.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.7.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.5.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"11.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+4", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+2", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.13+9", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.7.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"12.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"3.0.4+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.7.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.5.2", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"11.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+4", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+2", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.13+11", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.7.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"12.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"3.0.4+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.7.3" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.5.3", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"11.0.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.2", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.73.0+4", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.2.3+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.9.1+2", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.3.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.24.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2020.7.22", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.13+11", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.7.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.36.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.7.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), nothing, UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"12.0.1+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"3.0.4+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.41.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"16.2.1+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.8.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.3.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.2.1", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.20+0", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.8.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"13.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.1.1+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.8.2" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"0.5.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.3.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+11", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.2.1", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.20+0", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.8.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.1", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"13.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.1.1+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.8.4" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8bb1440f-4735-579b-a4ab-409b98df4dab") => StdlibInfo( "DelimitedFiles", UUID("8bb1440f-4735-579b-a4ab-409b98df4dab"), nothing, UUID[UUID("a63ad114-7e13-5084-954f-fe012c677804")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.3.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+11", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.2.1", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.20+0", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.40.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.8.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.0", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.1", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.12+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"13.0.1+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.1.1+0", UUID[UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.7.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.2" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.3", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"7.84.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.10.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.48.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.9.4" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"14.0.6+3", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.5.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+13", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.1.1+4", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2022.10.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.21+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.9.2", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.9.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"5.10.1+6", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"14.0.6+3", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.10.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.0.5+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+6", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"15.0.7+10", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.6.4+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+14", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.0+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.1.10", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.23+2", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.10.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.10.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.10.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.2.1+1", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"15.0.7+10", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.10.1" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+6", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"15.0.7+10", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.6.4+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+14", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.0+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.1.10", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.23+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.10.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.10.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.10.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.2.1+1", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"15.0.7+10", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.10.3" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.1", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), nothing, UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.2.1+6", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"15.0.7+10", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), nothing, UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.4.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.6.4+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+14", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.5.0+5", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.0+1", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.2+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.1.10", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.23+4", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.10.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.10.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2") => StdlibInfo( "Statistics", UUID("10745b16-79ce-11e8-11f9-7d13ad32a3b2"), v"1.10.0", UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.2.1+1", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.4+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"15.0.7+10", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.52.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.11.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.2", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), v"1.11.0", UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), v"1.11.0", UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), v"1.11.0", UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), v"1.11.0", UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), v"1.11.0", UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), v"1.11.0", UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.3.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), v"1.11.0", UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011") => StdlibInfo( "JuliaSyntaxHighlighting", UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"16.0.6+4", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), v"1.11.0", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.6.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.7.2+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+16", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.7.2+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), v"1.11.0", UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), v"1.11.0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), v"1.11.0", UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), v"1.11.0", UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.6+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), v"1.11.0", UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2023.12.12", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.27+1", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.42.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.11.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb")], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), v"1.11.0", UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), v"1.11.0", UUID[], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), v"1.11.0", UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), v"1.11.0", UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), v"1.11.0", UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b") => StdlibInfo( "StyledStrings", UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), v"1.11.0", UUID[], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.6.0+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), v"1.11.0", UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.2.13+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"16.0.6+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.8.0+1", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.59.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.4.0+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), v"1.12.0" => Dict{UUID,StdlibInfo}( UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f") => StdlibInfo( "ArgTools", UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f"), v"1.1.2", UUID[], UUID[], ), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33") => StdlibInfo( "Artifacts", UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), v"1.11.0", UUID[], UUID[], ), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), v"1.11.0", UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), v"1.11.0", UUID[], UUID[], ), UUID("e66e0078-7015-5450-92f7-15fbd957f2ae") => StdlibInfo( "CompilerSupportLibraries_jll", UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), v"1.1.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), v"1.11.0", UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6") => StdlibInfo( "Downloads", UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), v"1.6.0", UUID[UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), v"1.11.0", UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), v"1.11.0", UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("781609d7-10c4-51f6-84f2-b8444358ff6d") => StdlibInfo( "GMP_jll", UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), v"6.3.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), v"1.11.0", UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011") => StdlibInfo( "JuliaSyntaxHighlighting", UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b")], UUID[], ), UUID("d55e3150-da41-5e91-b323-ecfd1eec6109") => StdlibInfo( "LLD_jll", UUID("d55e3150-da41-5e91-b323-ecfd1eec6109"), v"17.0.6+4", UUID[UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9") => StdlibInfo( "LLVMLibUnwind_jll", UUID("47c5dbc3-30ba-59ef-96a6-123e260183d9"), v"12.0.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3") => StdlibInfo( "LazyArtifacts", UUID("4af54fe1-eca0-43a8-85a7-787d91b784e3"), v"1.11.0", UUID[UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21") => StdlibInfo( "LibCURL", UUID("b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"), v"0.6.4", UUID[UUID("14a3606d-f60d-562e-9121-12d972cd8159"), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0")], UUID[], ), UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0") => StdlibInfo( "LibCURL_jll", UUID("deac9b47-8bc7-5906-a0fe-35ac56dc84c0"), v"8.6.0+0", UUID[UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("83775a58-1f1d-513f-b197-d71354ab007a"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5") => StdlibInfo( "LibGit2_jll", UUID("e37daf67-58a4-590a-8e99-b0245dd2ffc5"), v"1.8.0+0", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("29816b5a-b9ab-546f-933c-edad1886dfa8") => StdlibInfo( "LibSSH2_jll", UUID("29816b5a-b9ab-546f-933c-edad1886dfa8"), v"1.11.0+1", UUID[UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("183b4373-6708-53ba-ad28-60e28bb38547") => StdlibInfo( "LibUV_jll", UUID("183b4373-6708-53ba-ad28-60e28bb38547"), v"2.0.1+16", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3") => StdlibInfo( "LibUnwind_jll", UUID("745a5e78-f969-53e9-954f-d19f2f74f4e3"), v"1.7.2+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), v"1.11.0", UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), v"1.11.0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), v"1.11.0", UUID[], UUID[], ), UUID("3a97d323-0669-5f0c-9066-3539efd106a3") => StdlibInfo( "MPFR_jll", UUID("3a97d323-0669-5f0c-9066-3539efd106a3"), v"4.2.1+0", UUID[UUID("781609d7-10c4-51f6-84f2-b8444358ff6d"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011")], UUID[], ), UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1") => StdlibInfo( "MbedTLS_jll", UUID("c8ffd9c3-330d-5841-b78e-0817d7145fa1"), v"2.28.6+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), v"1.11.0", UUID[], UUID[], ), UUID("14a3606d-f60d-562e-9121-12d972cd8159") => StdlibInfo( "MozillaCACerts_jll", UUID("14a3606d-f60d-562e-9121-12d972cd8159"), v"2024.3.11", UUID[], UUID[], ), UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908") => StdlibInfo( "NetworkOptions", UUID("ca575930-c2e3-43a9-ace4-1e988b2c1908"), v"1.2.0", UUID[], UUID[], ), UUID("4536629a-c528-5b80-bd46-f80d51c5b363") => StdlibInfo( "OpenBLAS_jll", UUID("4536629a-c528-5b80-bd46-f80d51c5b363"), v"0.3.27+1", UUID[UUID("e66e0078-7015-5450-92f7-15fbd957f2ae"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("05823500-19ac-5b8b-9628-191a04bc5112") => StdlibInfo( "OpenLibm_jll", UUID("05823500-19ac-5b8b-9628-191a04bc5112"), v"0.8.1+2", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15") => StdlibInfo( "PCRE2_jll", UUID("efcefdf7-47ab-520b-bdef-62a2eaa19f15"), v"10.43.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.12.0", UUID[UUID("f43a241f-c20a-4ad4-852c-f6b1247861c6"), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33"), UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76")], UUID[UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb")], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), v"1.11.0", UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), v"1.11.0", UUID[], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), v"1.11.0", UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce")], UUID[], ), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce") => StdlibInfo( "SHA", UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), v"0.7.0", UUID[], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), v"1.11.0", UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), v"1.11.0", UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), v"1.12.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c")], UUID[], ), UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b") => StdlibInfo( "StyledStrings", UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b"), v"1.11.0", UUID[], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c") => StdlibInfo( "SuiteSparse_jll", UUID("bea87d4a-7f5b-5778-9afe-8cc45184846c"), v"7.6.1+0", UUID[UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76") => StdlibInfo( "TOML", UUID("fa267f1f-6049-4f14-aa54-33bafae1ed76"), v"1.0.3", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a")], UUID[], ), UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e") => StdlibInfo( "Tar", UUID("a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"), v"1.10.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("0dad84c5-d112-42e6-8d28-ef12dabb789f")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), v"1.11.0", UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), v"1.11.0", UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), v"1.11.0", UUID[], UUID[], ), UUID("83775a58-1f1d-513f-b197-d71354ab007a") => StdlibInfo( "Zlib_jll", UUID("83775a58-1f1d-513f-b197-d71354ab007a"), v"1.3.1+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36") => StdlibInfo( "dSFMT_jll", UUID("05ff407c-b0c1-5878-9df8-858cc2e60c36"), v"2.2.5+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a") => StdlibInfo( "libLLVM_jll", UUID("8f36deef-c2a5-5394-99ed-8e07531fb29a"), v"17.0.6+4", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850b90-86db-534c-a0d3-1478176c7d93") => StdlibInfo( "libblastrampoline_jll", UUID("8e850b90-86db-534c-a0d3-1478176c7d93"), v"5.9.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("8e850ede-7688-5339-a07c-302acd2aaf8d") => StdlibInfo( "nghttp2_jll", UUID("8e850ede-7688-5339-a07c-302acd2aaf8d"), v"1.60.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0") => StdlibInfo( "p7zip_jll", UUID("3f19e933-33d8-53b3-aaab-bd5110c3b7a0"), v"17.5.0+0", UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), UUID("56f22d72-fd6d-98f1-02f0-08ddc0907c33")], UUID[], ), ), ] # Next, we also embed a list of stdlibs that must _always_ be treated as stdlibs, # because they cannot be resolved in the registry; they have only ever existed within # the Julia stdlib source tree, and because of that, trying to resolve them will fail. const UNREGISTERED_STDLIBS =Dict{UUID,StdlibInfo}( UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f") => StdlibInfo( "Base64", UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"), nothing, UUID[], UUID[], ), UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc") => StdlibInfo( "CRC32c", UUID("8bf52ea8-c179-5cab-976a-9e18b702a9bc"), nothing, UUID[], UUID[], ), UUID("ade2ca70-3891-5945-98fb-dc099432e06a") => StdlibInfo( "Dates", UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b") => StdlibInfo( "Distributed", UUID("8ba89e20-285c-5b6f-9357-94700520ee1b"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("6462fe0b-24de-5631-8697-dd941f90decc")], UUID[], ), UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee") => StdlibInfo( "FileWatching", UUID("7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"), nothing, UUID[], UUID[], ), UUID("9fa8497b-333b-5362-9e8d-4d0656e87820") => StdlibInfo( "Future", UUID("9fa8497b-333b-5362-9e8d-4d0656e87820"), nothing, UUID[UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240") => StdlibInfo( "InteractiveUtils", UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), nothing, UUID[UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011") => StdlibInfo( "JuliaSyntaxHighlighting", UUID("dc6e5ff7-fb65-4e79-a425-ec3bc9c03011"), v"1.11.0", UUID[UUID("f489334b-da3d-4c2e-b8f0-e476e12c162b")], UUID[], ), UUID("76f85450-5226-5b5a-8eaa-529ad045b433") => StdlibInfo( "LibGit2", UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), nothing, UUID[], UUID[], ), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb") => StdlibInfo( "Libdl", UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb"), nothing, UUID[], UUID[], ), UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e") => StdlibInfo( "LinearAlgebra", UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), nothing, UUID[UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("56ddb016-857b-54e1-b83d-db4d58db5568") => StdlibInfo( "Logging", UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), nothing, UUID[], UUID[], ), UUID("d6f4376e-aef5-505a-96c1-9c027394607a") => StdlibInfo( "Markdown", UUID("d6f4376e-aef5-505a-96c1-9c027394607a"), nothing, UUID[UUID("2a0f44e3-6c83-55bd-87e4-b1978d98bd5f")], UUID[], ), UUID("a63ad114-7e13-5084-954f-fe012c677804") => StdlibInfo( "Mmap", UUID("a63ad114-7e13-5084-954f-fe012c677804"), nothing, UUID[], UUID[], ), UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f") => StdlibInfo( "Pkg", UUID("44cfe95a-1eb2-52ea-b672-e2afdf69b78f"), v"1.1.2", UUID[UUID("ade2ca70-3891-5945-98fb-dc099432e06a"), UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("76f85450-5226-5b5a-8eaa-529ad045b433"), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("de0858da-6303-5e67-8744-51eddeeeb8d7") => StdlibInfo( "Printf", UUID("de0858da-6303-5e67-8744-51eddeeeb8d7"), nothing, UUID[UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5")], UUID[], ), UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79") => StdlibInfo( "Profile", UUID("9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"), nothing, UUID[UUID("de0858da-6303-5e67-8744-51eddeeeb8d7")], UUID[], ), UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb") => StdlibInfo( "REPL", UUID("3fa0cd96-eef1-5676-8a61-b3b8758bbffb"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("6462fe0b-24de-5631-8697-dd941f90decc"), UUID("d6f4376e-aef5-505a-96c1-9c027394607a")], UUID[], ), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c") => StdlibInfo( "Random", UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b")], UUID[], ), UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b") => StdlibInfo( "Serialization", UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), nothing, UUID[], UUID[], ), UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383") => StdlibInfo( "SharedArrays", UUID("1a1011a3-84de-559e-8e89-a11a2f7dc383"), nothing, UUID[UUID("9e88b42a-f829-5b0c-bbe9-9e923198166b"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("a63ad114-7e13-5084-954f-fe012c677804"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("6462fe0b-24de-5631-8697-dd941f90decc") => StdlibInfo( "Sockets", UUID("6462fe0b-24de-5631-8697-dd941f90decc"), nothing, UUID[], UUID[], ), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf") => StdlibInfo( "SparseArrays", UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9") => StdlibInfo( "SuiteSparse", UUID("4607b0f0-06f3-5cda-b6b1-a6196a1729e9"), nothing, UUID[UUID("37e2e46d-f89d-539d-b4ee-838fcccc9c8e"), UUID("2f01184e-e22b-5df5-ae63-d93ebab69eaf"), UUID("8f399da3-3557-5675-b5ff-fb832c97cbdb")], UUID[], ), UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40") => StdlibInfo( "Test", UUID("8dfed614-e22c-5e08-85e1-65c5234f0b40"), nothing, UUID[UUID("b77e0a4c-d291-57a0-90e8-8db25a27a240"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c"), UUID("56ddb016-857b-54e1-b83d-db4d58db5568"), UUID("8ba89e20-285c-5b6f-9357-94700520ee1b")], UUID[], ), UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4") => StdlibInfo( "UUIDs", UUID("cf7118a7-6976-5b1a-9a39-7adc72f591a4"), nothing, UUID[UUID("ea8e919c-243c-51af-8825-aaa63cd721ce"), UUID("9a3f8284-a2c9-5f02-9a11-845980a1fd5c")], UUID[], ), UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5") => StdlibInfo( "Unicode", UUID("4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"), nothing, UUID[], UUID[], ), )

HistoricalStdlibVersions

https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git

[ "MIT" ]

2.0.0

d50c73e4abd8f7c58eb76a8884dfd531fa8dac81

code

100

import HistoricalStdlibVersions import Pkg using Test @test !isempty(Pkg.Types.STDLIBS_BY_VERSION)

HistoricalStdlibVersions

https://github.com/JuliaPackaging/HistoricalStdlibVersions.jl.git