Solving systems of algebraic linear equations is among the most frequent problems in
scientific computing. It appears in many areas like physics, engineering, chemistry, biology …

This paper introduces the orthogonal rational approximation (ORA) algorithm for rational
function approximation of transfer functions, based on data available from simulations or …

We consider linear ill-conditioned operator equations in a Hilbert space setting. Motivated by
the aggregation method, we consider approximate solutions constructed from linear …

The numerical computation of a matrix function such as exp (-t A) V, where A is an n× n large
and sparse matrix, V is an n× p block with p≪ n, and t> 0 arises in various applications …

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 …

Some Krylov subspace methods for approximating the action of matrix functions are
presented in this paper. The main idea of these techniques is to project the approximation …

This paper is concerned with the approximation of matrix functionals of the form w T f (A) v,
where A∈ ℝ n× n A∈R^n*n is a large nonsymmetric matrix, w, v∈ ℝ nw,v∈R^n, and f is a …

In many applications it is important to understand the sensitivity of eigenvalues of a matrix
polynomial to perturbations of the polynomial. The sensitivity commonly is described by …

In this thesis we develop efficient numerical methods for the approximation of matrix
functionals of the form F (A):= w^ Tf (A) v, where A is a large symmetric or nonsymmetric …

The numerical computation of matrix functions such as $ f (A) V $, where $ A $ is an $
n\times n $ large and sparse square matrix, $ V $ is an $ n\times p $ block with $ p\ll n $ and …