Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/7749
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dc.contributor.authorVicente, Luís N.-
dc.date.accessioned2009-02-17T11:18:01Z-
dc.date.available2009-02-17T11:18:01Z-
dc.date.issued2003en_US
dc.identifier.citationOptimization and Engineering. 4:3 (2003) 159-175en_US
dc.identifier.urihttp://hdl.handle.net/10316/7749-
dc.description.abstractThe goal of this paper is to organize some of the mathematical and algorithmic aspects of the space-mapping technique for continuous optimization with expensive function evaluations. First, we consider the mapping from the fine space to the coarse space when the models are vector-valued functions and when the space-mapping (nonlinear) least-squares residual is nonzero. We show how the sensitivities of the space mapping can be used to deal with space-mapping surrogates of the fine model. We derive a framework where it is possible to design globally convergent trust-region methods to minimize such fine-model surrogates.en_US
dc.language.isoengeng
dc.rightsopenAccesseng
dc.titleSpace Mapping: Models, Sensitivities, and Trust-Regions Methodsen_US
dc.typearticleen_US
dc.identifier.doi10.1023/A:1023968629245en_US
item.grantfulltextopen-
item.fulltextCom Texto completo-
item.languageiso639-1en-
crisitem.author.orcid0000-0003-1097-6384-
Appears in Collections:FCTUC Matemática - Artigos em Revistas Internacionais
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