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Shift-invert Arnoldi's method with preconditioned iterative solves


Reference:

Freitag, M. A. and Spence, A., 2010. Shift-invert Arnoldi's method with preconditioned iterative solves. SIAM Journal On Matrix Analysis and Applications (SIMAX), 31 (3), pp. 942-969.

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Official URL:

http://dx.doi.org/10.1137/080716281

Abstract

We consider the computation of a few eigenvectors and corresponding eigenvalues of a large sparse nonsymmetric matrix using shift-invert Arnoldi's method with and without implicit restarts. For the inner iterations we use preconditioned GMRES as the inexact iterative solver. The costs of the solves are measured by the number of inner iterations needed by the iterative solver at each outer step of the algorithm. We first extend the relaxation strategy developed by Simoncini [SIAM J. Numer. Anal., 43 (2005), pp. 1155-1174] to implicitly restarted Arnoldi's method, which yields an improvement in the overall costs of the method. Secondly, we apply a new preconditioning strategy to the inner solver. We show that small rank changes to the preconditioner can produce significant savings in the total number of iterations. The combination of the new preconditioner with the relaxation strategy in implicitly restarted Arnoldi produces enhancement in the overall costs of around 50 percent in the examples considered here. Numerical experiments illustrate the theory throughout the paper.

Details

Item Type Articles
CreatorsFreitag, M. A.and Spence, A.
DOI10.1137/080716281
DepartmentsFaculty of Science > Mathematical Sciences
RefereedYes
StatusPublished
ID Code17159

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