Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Computational & Applied Mathematics |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022010000200009 |
Resumo: | In this paper, we study the distribution on the eigenvalues of the preconditioned matrices that arise in solving two-by-two block non-Hermitian positive semidefinite linear systems by use of the accelerated Hermitian and skew-Hermitian splitting iteration methods. According to theoretical analysis, we prove that all eigenvalues of the preconditioned matrices are very clustered with any positive iteration parameters α and β; especially, when the iteration parameters α and β approximate to 1, all eigenvalues approach 1. We also prove that the real parts of all eigenvalues of the preconditioned matrices are positive, i.e., the preconditioned matrix is positive stable. Numerical experiments show the correctness and feasibility of the theoretical analysis. Mathematical subject classification: 65F10, 65N22, 65F50. |
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Computational & Applied Mathematics |
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Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problemsPAHSSgeneralized saddle point problemsplitting iteration methodpositive stableIn this paper, we study the distribution on the eigenvalues of the preconditioned matrices that arise in solving two-by-two block non-Hermitian positive semidefinite linear systems by use of the accelerated Hermitian and skew-Hermitian splitting iteration methods. According to theoretical analysis, we prove that all eigenvalues of the preconditioned matrices are very clustered with any positive iteration parameters α and β; especially, when the iteration parameters α and β approximate to 1, all eigenvalues approach 1. We also prove that the real parts of all eigenvalues of the preconditioned matrices are positive, i.e., the preconditioned matrix is positive stable. Numerical experiments show the correctness and feasibility of the theoretical analysis. Mathematical subject classification: 65F10, 65N22, 65F50.Sociedade Brasileira de Matemática Aplicada e Computacional2010-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022010000200009Computational & Applied Mathematics v.29 n.2 2010reponame:Computational & Applied Mathematicsinstname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)instacron:SBMAC10.1590/S1807-03022010000200009info:eu-repo/semantics/openAccessHuang,Zhuo-HongHuang,Ting-Zhueng2010-07-22T00:00:00Zoai:scielo:S1807-03022010000200009Revistahttps://www.scielo.br/j/cam/ONGhttps://old.scielo.br/oai/scielo-oai.php||sbmac@sbmac.org.br1807-03022238-3603opendoar:2010-07-22T00:00Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)false |
dc.title.none.fl_str_mv |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems |
title |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems |
spellingShingle |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems Huang,Zhuo-Hong PAHSS generalized saddle point problem splitting iteration method positive stable |
title_short |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems |
title_full |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems |
title_fullStr |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems |
title_full_unstemmed |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems |
title_sort |
Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems |
author |
Huang,Zhuo-Hong |
author_facet |
Huang,Zhuo-Hong Huang,Ting-Zhu |
author_role |
author |
author2 |
Huang,Ting-Zhu |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Huang,Zhuo-Hong Huang,Ting-Zhu |
dc.subject.por.fl_str_mv |
PAHSS generalized saddle point problem splitting iteration method positive stable |
topic |
PAHSS generalized saddle point problem splitting iteration method positive stable |
description |
In this paper, we study the distribution on the eigenvalues of the preconditioned matrices that arise in solving two-by-two block non-Hermitian positive semidefinite linear systems by use of the accelerated Hermitian and skew-Hermitian splitting iteration methods. According to theoretical analysis, we prove that all eigenvalues of the preconditioned matrices are very clustered with any positive iteration parameters α and β; especially, when the iteration parameters α and β approximate to 1, all eigenvalues approach 1. We also prove that the real parts of all eigenvalues of the preconditioned matrices are positive, i.e., the preconditioned matrix is positive stable. Numerical experiments show the correctness and feasibility of the theoretical analysis. Mathematical subject classification: 65F10, 65N22, 65F50. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022010000200009 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022010000200009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1807-03022010000200009 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
dc.source.none.fl_str_mv |
Computational & Applied Mathematics v.29 n.2 2010 reponame:Computational & Applied Mathematics instname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC) instacron:SBMAC |
instname_str |
Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC) |
instacron_str |
SBMAC |
institution |
SBMAC |
reponame_str |
Computational & Applied Mathematics |
collection |
Computational & Applied Mathematics |
repository.name.fl_str_mv |
Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC) |
repository.mail.fl_str_mv |
||sbmac@sbmac.org.br |
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1754734890213965824 |