In matrix theory, Sylvester's formula or Sylvester's matrix theorem (named after J. J. Sylvester) or Lagrange−Sylvester interpolation expresses an analytic function of a matrix as a polynomial in , in terms of the eigenvalues and eigenvectors of . It states that where the are the eigenvalues of , and the matrices are the corresponding Frobenius covariants of , which are (projection) matrix Lagrange polynomials of . Conditions Sylvester's formula applies for any diagonalizable matrix with distinct eigenvalues, 1, …, λk, and any function defined on some subset of the complex numbers such that is well defined. The last condition means that every eigenvalue is in the domain of , and that every eigenvalue with multiplicity i > 1 is in the interior of the domain, with being () times differentiable at . Example Consider the two-by-two matrix: This matrix has two eigenvalues, 5 and −2. Its Frobenius covariants are Sylvester's formula then amounts to For instance, if is defined by , then Sylvester's formula expresses the matrix inverse as Generalization Sylvester's formula is only valid for diagonalizable matrices; an extension due to Arthur Buchheim, based on Hermite interpolating polynomials, covers the general case: , where . A concise form is further given by Hans Schwerdtfeger, , where i are the corresponding Frobenius covariants of Special case If a matrix is both Hermitian and unitary, then it can only have eigenvalues of , and therefore , where is the projector onto the subspace with eigenvalue +1, and is the projector onto the subspace with eigenvalue ; By the completeness of the eigenbasis, . Therefore, for any analytic function , In particular, and . See also Adjugate matrix Holomorphic functional calculus Resolvent formalism References F.R. Gantmacher, The Theory of Matrices v I (Chelsea Publishing, NY, 1960) , pp 101-103 Matrix theory