Abstract
This paper describes methods based on the extended symmetric block Lanczos process for computing element-wise estimates of upper and lower bounds for matrix functions of the form V(T)f (A)V, where the matrix A is an element of R-nxn is large, symmetric, and nonsingular, V is an element of R-nxs is a block vector with 1 < s & laquo;& nbsp;& nbsp;n orthonormal columns, and f is a function that is defined on the convex hull of the spectrum of A. Pairs of block Gauss-Laurent and block anti-Gauss-Laurent quadrature rules are defined and applied to determine the desired estimates. The methods presented generalize methods discussed by Fenu et al. (2013), which use (standard) block Krylov subspaces, to allow the application of extended block Krylov subspaces. The latter spaces are the union of a (standard) block Krylov subspace determined by positive powers of A and a block Krylov subspace defined by negative powers of A. Computed examples illustrate the effectiveness of the proposed method. (C) 2021 Elsevier B.V. All rights reserved.
NSTL
Elsevier