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dc.contributor.authorAyuso Dios, Blanca
dc.contributor.authorBarker, A. T.
dc.contributor.authorVassilevski, P. S.
dc.date.accessioned2015-05-25T08:33:54Z
dc.date.available2015-05-25T08:33:54Z
dc.date.issued2014-01
dc.identifier.citationA Combined Preconditioning Strategy for Nonsymmetric Systems 2014, 36 (6):A2533 SIAM Journal on Scientific Computing
dc.identifier.issn1064-8275
dc.identifier.issn1095-7197
dc.identifier.doi10.1137/120888946
dc.identifier.urihttp://hdl.handle.net/10754/555651
dc.description.abstractWe present and analyze a class of nonsymmetric preconditioners within a normal (weighted least-squares) matrix form for use in GMRES to solve nonsymmetric matrix problems that typically arise in finite element discretizations. An example of the additive Schwarz method applied to nonsymmetric but definite matrices is presented for which the abstract assumptions are verified. A variable preconditioner, combining the original nonsymmetric one and a weighted least-squares version of it, is shown to be convergent and provides a viable strategy for using nonsymmetric preconditioners in practice. Numerical results are included to assess the theory and the performance of the proposed preconditioners.
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.relation.urlhttp://epubs.siam.org/doi/abs/10.1137/120888946
dc.relation.urlhttp://arxiv.org/abs/1208.4544
dc.rightsArchived with thanks to SIAM Journal on Scientific Computing
dc.subjectpreconditioning
dc.subjectnonsymmetric matrices
dc.subjectnormal matrix form
dc.subjectadditive Schwarz method
dc.titleA Combined Preconditioning Strategy for Nonsymmetric Systems
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalSIAM Journal on Scientific Computing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDipartimento di Matematica, Universit` a di Bologna, Piazza di Porta San Donato 5, I-40127 Bologna, Italy
dc.contributor.institutionCenter for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94550
dc.identifier.arxividarXiv:1208.4544
kaust.personAyuso Dios, Blanca
refterms.dateFOA2018-06-13T14:52:33Z


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