Improvement of preconditioned bi-Lanczos-type algorithms with residual norm minimization for the stable solution of systems of linear equations View Full Text


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Article Info

DATE

2021-09-02

AUTHORS

Shoji Itoh

ABSTRACT

In this paper, improved algorithms are proposed for preconditioned bi-Lanczos-type methods with residual norm minimization for the stable solution of systems of linear equations. In particular, preconditioned algorithms pertaining to the bi-conjugate gradient stabilized method (BiCGStab) and the generalized product-type method based on the BiCG (GPBiCG) have been improved. These algorithms are more stable compared to conventional alternatives. Further, a stopping criterion changeover is proposed for use with these improved algorithms. This results in higher accuracy (lower true relative error) compared to the case where no changeover is done. Numerical results confirm the improvements with respect to the preconditioned BiCGStab, the preconditioned GPBiCG, and stopping criterion changeover. These improvements could potentially be applied to other preconditioned algorithms based on bi-Lanczos-type methods. More... »

PAGES

1-56

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13160-021-00480-0

DOI

http://dx.doi.org/10.1007/s13160-021-00480-0

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https://app.dimensions.ai/details/publication/pub.1140861132


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