Abstract
This paper concentrates on applying reordering algorithms as a preprocessing step of a restarted Generalized Minimal Residual (GMRES for short) solver preconditioned by three ILU-type preconditioners. This paper investigates the effect of 13 orderings on the convergence of the preconditioned GMRES solver restarted every 50 steps when applied to nine real large-scale nonsymmetric and not positive definite matrices. Specifically, this paper shows the most promising combination of preconditioners and reordering for each linear system used.
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CITATION STYLE
de Oliveira, S. L. G., Carvalho, C., & Osthoff, C. (2020). The Influence of Reordering Algorithms on the Convergence of a Preconditioned Restarted GMRES Method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12249 LNCS, pp. 19–32). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58799-4_2
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