A framework of the harmonic Arnoldi method for evaluating $\varphi$-functions with applications to exponential integrators

TL;DR

This paper introduces a harmonic Arnoldi framework for efficiently computing $\

Contribution

It develops a harmonic Arnoldi method for $\

Findings

The new algorithm computes multiple $\

It outperforms existing methods in numerical experiments.

The approach effectively handles residuals and restarts in $\

Abstract

In recent years, a great deal of attention has been focused on numerically solving exponential integrators. The important ingredient to the implementation of exponential integrators is the efficient and accurate evaluation of the so called $\varphi$-functions on a given vector. The Krylov subspace method is an important technique for this problem. For this type of method, however, restarts become essential for the sake of storage requirements or due to the growing computational complexity of evaluating the matrix function on a Hessenberg matrix of growing size. Another problem in computing $\varphi$-functions is the lack of a clear residual notion. The contribution of this work is threefold. First, we introduce a framework of the harmonic Arnoldi method for $\varphi$-functions, which is based on the residual and the oblique projection technique. Second, we establish the relationship between the harmonic Arnoldi approximation and the classical Arnoldi approximation, and compare the harmonic Arnoldi method with the Arnoldi method from a theoretical point of view. Third, we apply the thick-restarting strategy to the harmonic Arnoldi method, and propose a thick-restated harmonic Arnoldi algorithm for evaluating $\varphi$-functions. An advantage of the new algorithm is that we can compute several $\varphi$-functions simultaneously in the same search subspace. We show the merit of augmenting approximate eigenvectors in the search subspace, and give insight into the relationship between the error and the residual of $\varphi$-functions. Numerical experiments show the superiority of our new algorithm over many state-of-the-art algorithms for the computation of $\varphi$-functions.