An Arnoldi code for computing selected eigenvalues of sparse, real, unsymmetric matrices

Abstract

Arnoldi methods can be more effective than subspace iteration methods for computing the dominant eigenvalues of a large, sparse, real, unsymmetric matrix. A code, EB12 , for the sparse, unsymmetric eigenvalue problem based on a subspace iteration algorithm, optionally combined with Chebychev acceleration, has recently been described by Duff and Scott and is included in the Harwell Subroutine Library. In this article we consider variants of the method of Arnoldi and discuss the design and development of a code to implement these methods. The new code, which is called EB13 , offers the user the choice of a basic Arnoldi algorithm, an Arnoldi algorithm with Chebychev acceleration, and a Chebychev preconditioned Arnoldi algorithm. Each method is available in blocked and unblocked form. The code may be used to compute either the rightmost eigenvalues, the eigenvalues of largest absolute value, or the eigenvalues of largest imaginary part. The performance of each option in the EB13 package is compared with that of subspace iteration on a range of test problems, and on the basis of the results, advice is offered to the user on the appropriate choice of method. — Author's Abstract