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dc.contributor.authorRoche, Jean-Rodolphe
dc.contributor.authorHerskovits, José
dc.contributor.authorBazán, Elmer
dc.contributor.authorZuniga, Andrés
dc.date.accessioned2020-06-12T12:26:20Z
dc.date.available2020-06-12T12:26:20Z
dc.date.issued2017
dc.identifier.issn1615-1488
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20885
dc.language.isoenen
dc.subjectNonlinear optimizationen
dc.subjectSemidefinite programmingen
dc.subjectFeasible directionsen
dc.subjectInterior-point methodsen
dc.subjectStructural optimizationen
dc.subject.ddc515en
dc.titleA feasible direction algorithm for general nonlinear semidefinite programmingen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper deals with nonlinear smooth optimization problems with equality and inequality constraints, as well as semidefinite constraints on nonlinear symmetric matrix-valued functions. A new semidefinite programming algorithm that takes advantage of the structure of the matrix constraints is presented. This one is relevant in applications where the matrices have a favorable structure, as in the case when finite element models are employed. FDIPA_GSDP is then obtained by integration of this new method with the well known Feasible Direction Interior Point Algorithm for nonlinear smooth optimization, FDIPA. FDIPA_GSDP makes iterations in the primal and dual variables to solve the first order optimality conditions. Given an initial feasible point with respect to the inequality constraints, FDIPA_GSDP generates a feasible descent sequence, converging to a local solution of the problem. At each iteration a feasible descent direction is computed by merely solving two linear systems with the same matrix. A line search along this direction looks for a new feasible point with a lower objective. Global convergence to stationary points is proved. Some structural optimization test problems were solved very efficiently, without need of parameters tuning.en
dc.relation.isversionofjnlnameStructural and Multidisciplinary Optimization
dc.relation.isversionofjnlvol55en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2017
dc.relation.isversionofjnlpages1261-1279en
dc.relation.isversionofdoi10.1007/s00158-016-1564-5en
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelAnalyseen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-06-12T12:22:13Z
hal.person.labIds211251
hal.person.labIds83486
hal.person.labIds83486
hal.person.labIds60


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