On the Lanczos Method for Computing Some Matrix Functions - Ying Gu; Hari Mohan Srivastava; Xiaolan Liu (Axioms)
The study of matrix functions is highly significant and has important applications in control theory, quantum mechanics, signal processing, and machine learning. Previous work has mainly focused on how to use the Krylov-type method to efficiently calculate matrix functions <semantics>f(A)</semantics><semantics>β</semantics> and <semantics>βTf(A)</semantics><semantics>β</semantics> when A is symmetric. In this paper, we mainly illustrate the convergence using the polynomial approximation theory for the case where A is symmetric positive definite. Numerical results illustrate the effectiveness of our theoretical results.