Spectral properties of the preconditioned AHSS iteration method for generalized saddle point problems

Detalhes bibliográficos
Autor(a) principal: Huang,Zhuo-Hong
Data de Publicação: 2010
Outros Autores: Huang,Ting-Zhu
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.
id SBMAC-2_81025086fb5d51412ff2272de36991f0
oai_identifier_str oai:scielo:S1807-03022010000200009
network_acronym_str SBMAC-2
network_name_str Computational & Applied Mathematics
repository_id_str
spelling 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
_version_ 1754734890213965824