A new algorithm of nonlinear conjugate gradient method with strong convergence
Autor(a) principal: | |
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Data de Publicação: | 2008 |
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=S1807-03022008000100006 |
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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Computational & Applied Mathematics |
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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) |
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_ |
1754734890158391296 |