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dc.contributor.authorRoche, Angelina
dc.date.accessioned2018-02-16T10:52:59Z
dc.date.available2018-02-16T10:52:59Z
dc.date.issued2018
dc.identifier.issn0943-4062
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17403
dc.language.isoenen
dc.subjectResponse Surface Methodologyen
dc.subjectDesign of Experimentsen
dc.subjectFunctional data analysisen
dc.subjectBlack-box optimizationen
dc.subject.ddc519en
dc.titleLocal Optimization of Black-Box Function with High or Infinite-Dimensional Inputsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenBlack-box optimization problems when the input space is a high-dimensional space or a function space appear in more and more applications. In this context, the methods available for finite-dimensional data do not apply. The aim is then to propose a general method for optimization involving dimension reduction techniques. Different dimension reduction basis are considered (including data-driven basis). The methodology is illustrated on simulated functional data. The choice of the different parameters, in particular the dimension of the approximation space, is discussed. The method is finally applied to a problem of nuclear safety.en
dc.relation.isversionofjnlnameComputational Statistics
dc.relation.isversionofjnlvol33en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2018-03
dc.relation.isversionofjnlpages467–485en
dc.relation.isversionofdoi10.1007/s00180-017-0751-1en
dc.relation.isversionofjnlpublisherPhysica-Verlen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2018-02-16T10:50:53Z
hal.person.labIds60$$$2088


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