A new algorithm of nonlinear conjugate gradient method with strong convergence

Detalhes bibliográficos
Autor(a) principal: Shi,Zhen-Jun
Data de Publicação: 2008
Outros Autores: Guo,Jinhua
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Computational & Applied Mathematics
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022008000100006
Resumo: The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of nonlinear conjugate gradient method with strong convergence for unconstrained minimization problems. The new algorithm can generate an adequate trust region radius automatically at each iteration and has global convergence and linear convergence rate under some mild conditions. Numerical results show that the new algorithm is efficient in practical computation and superior to other similar methods in many situations.
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spelling A new algorithm of nonlinear conjugate gradient method with strong convergenceunconstrained optimizationnonlinear conjugate gradient methodglobal convergencelinear convergence rateThe nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of nonlinear conjugate gradient method with strong convergence for unconstrained minimization problems. The new algorithm can generate an adequate trust region radius automatically at each iteration and has global convergence and linear convergence rate under some mild conditions. Numerical results show that the new algorithm is efficient in practical computation and superior to other similar methods in many situations.Sociedade Brasileira de Matemática Aplicada e Computacional2008-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022008000100006Computational & Applied Mathematics v.27 n.1 2008reponame:Computational & Applied Mathematicsinstname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)instacron:SBMACinfo:eu-repo/semantics/openAccessShi,Zhen-JunGuo,Jinhuaeng2008-04-02T00:00:00Zoai:scielo:S1807-03022008000100006Revistahttps://www.scielo.br/j/cam/ONGhttps://old.scielo.br/oai/scielo-oai.php||sbmac@sbmac.org.br1807-03022238-3603opendoar:2008-04-02T00:00Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)false
dc.title.none.fl_str_mv A new algorithm of nonlinear conjugate gradient method with strong convergence
title A new algorithm of nonlinear conjugate gradient method with strong convergence
spellingShingle A new algorithm of nonlinear conjugate gradient method with strong convergence
Shi,Zhen-Jun
unconstrained optimization
nonlinear conjugate gradient method
global convergence
linear convergence rate
title_short A new algorithm of nonlinear conjugate gradient method with strong convergence
title_full A new algorithm of nonlinear conjugate gradient method with strong convergence
title_fullStr A new algorithm of nonlinear conjugate gradient method with strong convergence
title_full_unstemmed A new algorithm of nonlinear conjugate gradient method with strong convergence
title_sort A new algorithm of nonlinear conjugate gradient method with strong convergence
author Shi,Zhen-Jun
author_facet Shi,Zhen-Jun
Guo,Jinhua
author_role author
author2 Guo,Jinhua
author2_role author
dc.contributor.author.fl_str_mv Shi,Zhen-Jun
Guo,Jinhua
dc.subject.por.fl_str_mv unconstrained optimization
nonlinear conjugate gradient method
global convergence
linear convergence rate
topic unconstrained optimization
nonlinear conjugate gradient method
global convergence
linear convergence rate
description The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of nonlinear conjugate gradient method with strong convergence for unconstrained minimization problems. The new algorithm can generate an adequate trust region radius automatically at each iteration and has global convergence and linear convergence rate under some mild conditions. Numerical results show that the new algorithm is efficient in practical computation and superior to other similar methods in many situations.
publishDate 2008
dc.date.none.fl_str_mv 2008-01-01
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=S1807-03022008000100006
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022008000100006
dc.language.iso.fl_str_mv eng
language eng
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.27 n.1 2008
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)
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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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