Preconditioned GMRES methods with incomplete Givens orthogonalization method for large sparse least-squares problems

Authors:
Jun-Feng Yin;Ken Hayami
Affiliations:
National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan and Department of Mathematics, Tongji University, Shanghai 200092, PR China;National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan and Department of Informatics, The Graduate University for Advanced Studies (Sokendai), Japan
Venue:
Journal of Computational and Applied Mathematics
Year:
2009

Citing 5
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Abstract

We propose to precondition the GMRES method by using the incomplete Givens orthogonalization (IGO) method for the solution of large sparse linear least-squares problems. Theoretical analysis shows that the preconditioner satisfies the sufficient condition that can guarantee that the preconditioned GMRES method will never break down and always give the least-squares solution of the original problem. Numerical experiments further confirm that the new preconditioner is efficient. We also find that the IGO preconditioned BA-GMRES method is superior to the corresponding CGLS method for ill-conditioned and singular least-squares problems.