Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints

Detalhes bibliográficos
Autor(a) principal: Gugat,Martin
Data de Publicação: 2009
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Computational & Applied Mathematics
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022009000200006
Resumo: A Lavrentiev prox-regularization method for optimal control problems with point-wise state constraints is introduced where both the objective function and the constraints are regularized. The convergence of the controls generated by the iterative Lavrentiev prox-regularization algorithm is studied. For a sequence of regularization parameters that converges to zero, strong convergence of the generated control sequence to the optimal control is proved. Due to the proxcharacter of the proposed regularization, the feasibility of the iterates for a given parameter can be improved compared with the non-prox Lavrentiev-Regularization.
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spelling Lavrentiev-prox-regularization for optimal controlof PDEs with state constraintsoptimal controlpointwise state constraintsprox regularizationLavrentiev regularizationpde constrained optimizationconvergencefeasibilityA Lavrentiev prox-regularization method for optimal control problems with point-wise state constraints is introduced where both the objective function and the constraints are regularized. The convergence of the controls generated by the iterative Lavrentiev prox-regularization algorithm is studied. For a sequence of regularization parameters that converges to zero, strong convergence of the generated control sequence to the optimal control is proved. Due to the proxcharacter of the proposed regularization, the feasibility of the iterates for a given parameter can be improved compared with the non-prox Lavrentiev-Regularization.Sociedade Brasileira de Matemática Aplicada e Computacional2009-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022009000200006Computational & Applied Mathematics v.28 n.2 2009reponame:Computational & Applied Mathematicsinstname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)instacron:SBMAC10.1590/S1807-03022009000200006info:eu-repo/semantics/openAccessGugat,Martineng2009-07-08T00:00:00Zoai:scielo:S1807-03022009000200006Revistahttps://www.scielo.br/j/cam/ONGhttps://old.scielo.br/oai/scielo-oai.php||sbmac@sbmac.org.br1807-03022238-3603opendoar:2009-07-08T00:00Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)false
dc.title.none.fl_str_mv Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
title Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
spellingShingle Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
Gugat,Martin
optimal control
pointwise state constraints
prox regularization
Lavrentiev regularization
pde constrained optimization
convergence
feasibility
title_short Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
title_full Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
title_fullStr Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
title_full_unstemmed Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
title_sort Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints
author Gugat,Martin
author_facet Gugat,Martin
author_role author
dc.contributor.author.fl_str_mv Gugat,Martin
dc.subject.por.fl_str_mv optimal control
pointwise state constraints
prox regularization
Lavrentiev regularization
pde constrained optimization
convergence
feasibility
topic optimal control
pointwise state constraints
prox regularization
Lavrentiev regularization
pde constrained optimization
convergence
feasibility
description A Lavrentiev prox-regularization method for optimal control problems with point-wise state constraints is introduced where both the objective function and the constraints are regularized. The convergence of the controls generated by the iterative Lavrentiev prox-regularization algorithm is studied. For a sequence of regularization parameters that converges to zero, strong convergence of the generated control sequence to the optimal control is proved. Due to the proxcharacter of the proposed regularization, the feasibility of the iterates for a given parameter can be improved compared with the non-prox Lavrentiev-Regularization.
publishDate 2009
dc.date.none.fl_str_mv 2009-01-01
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=S1807-03022009000200006
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022009000200006
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1807-03022009000200006
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.28 n.2 2009
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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