A surrogate management framework using rigorous trust-region steps
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/45703 https://doi.org/10.1080/10556788.2012.719508 |
Resumo: | Surrogate models are frequently used in the optimization engineering community as convenient approaches to deal with functions for which evaluations are expensive or noisy, or lack convexity. These methodologies do not typically guarantee any type of convergence under reasonable assumptions. In this article, we will show how to incorporate the use of surrogate models, heuristics, or any other process of attempting a function value decrease in trust-region algorithms for unconstrained derivative-free optimization, in a way that global convergence of the latter algorithms to stationary points is retained. Our approach follows the lines of search/poll direct-search methods and corresponding surrogate management frameworks, both in algorithmic design and in the form of organizing the convergence theory. |
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A surrogate management framework using rigorous trust-region stepsSurrogate models are frequently used in the optimization engineering community as convenient approaches to deal with functions for which evaluations are expensive or noisy, or lack convexity. These methodologies do not typically guarantee any type of convergence under reasonable assumptions. In this article, we will show how to incorporate the use of surrogate models, heuristics, or any other process of attempting a function value decrease in trust-region algorithms for unconstrained derivative-free optimization, in a way that global convergence of the latter algorithms to stationary points is retained. Our approach follows the lines of search/poll direct-search methods and corresponding surrogate management frameworks, both in algorithmic design and in the form of organizing the convergence theory.Taylor & Francis2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/45703http://hdl.handle.net/10316/45703https://doi.org/10.1080/10556788.2012.719508enghttps://doi.org/10.1080/10556788.2012.719508Gratton, S.Vicente, Luís Nunesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2021-09-03T11:11:48Zoai:estudogeral.uc.pt:10316/45703Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:53:25.680055Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A surrogate management framework using rigorous trust-region steps |
title |
A surrogate management framework using rigorous trust-region steps |
spellingShingle |
A surrogate management framework using rigorous trust-region steps Gratton, S. |
title_short |
A surrogate management framework using rigorous trust-region steps |
title_full |
A surrogate management framework using rigorous trust-region steps |
title_fullStr |
A surrogate management framework using rigorous trust-region steps |
title_full_unstemmed |
A surrogate management framework using rigorous trust-region steps |
title_sort |
A surrogate management framework using rigorous trust-region steps |
author |
Gratton, S. |
author_facet |
Gratton, S. Vicente, Luís Nunes |
author_role |
author |
author2 |
Vicente, Luís Nunes |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Gratton, S. Vicente, Luís Nunes |
description |
Surrogate models are frequently used in the optimization engineering community as convenient approaches to deal with functions for which evaluations are expensive or noisy, or lack convexity. These methodologies do not typically guarantee any type of convergence under reasonable assumptions. In this article, we will show how to incorporate the use of surrogate models, heuristics, or any other process of attempting a function value decrease in trust-region algorithms for unconstrained derivative-free optimization, in a way that global convergence of the latter algorithms to stationary points is retained. Our approach follows the lines of search/poll direct-search methods and corresponding surrogate management frameworks, both in algorithmic design and in the form of organizing the convergence theory. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/45703 http://hdl.handle.net/10316/45703 https://doi.org/10.1080/10556788.2012.719508 |
url |
http://hdl.handle.net/10316/45703 https://doi.org/10.1080/10556788.2012.719508 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://doi.org/10.1080/10556788.2012.719508 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Taylor & Francis |
publisher.none.fl_str_mv |
Taylor & Francis |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133821159866368 |