Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/89442
DC FieldValueLanguage
dc.contributor.authorGratton, Serge-
dc.contributor.authorRoyer, Clément W-
dc.contributor.authorVicente, Luís Nunes-
dc.contributor.authorZhang, Zaikun-
dc.date.accessioned2020-06-02T16:02:40Z-
dc.date.available2020-06-02T16:02:40Z-
dc.date.issued2019-01-
dc.identifier.urihttps://hdl.handle.net/10316/89442-
dc.description.abstractDirect search is a methodology for derivative-free optimization whose iterations are characterized by evaluating the objective function using a set of polling directions. In deterministic direct search applied to smooth objectives, these directions must somehow conform to the geometry of the feasible region, and typically consist of positive generators of approximate tangent cones (which then renders the corresponding methods globally convergent in the linearly constrained case). One knows however from the unconstrained case that randomly generating the polling directions leads to better complexity bounds as well as to gains in numerical efficiency, and it becomes then natural to consider random generation also in the presence of constraints. In this paper, we study a class of direct-search methods based on sufficient decrease for solving smooth linearly constrained problems where the polling directions are randomly generated (in approximate tangent cones). The random polling directions must satisfy probabilistic feasible descent, a concept which reduces to probabilistic descent in the absence of constraints. Such a property is instrumental in establishing almost-sure global convergence and worst-case complexity bounds with overwhelming probability. Numerical results show that the randomization of the polling directions can be beneficial over standard approaches with deterministic guarantees, as it is suggested by the respective worst-case complexity bounds.pt
dc.language.isoengpt
dc.publisherSpringer Verlagpt
dc.relationCMUC-UID/MAT/00324/2013pt
dc.rightsembargoedAccesspt
dc.subjectDerivative-free optimization; Direct-search methods; Bound constraints; Linear constraints; Feasible descent; Probabilistic feasible descent; Worst-case complexitypt
dc.titleDirect search based on probabilistic feasible descent for bound and linearly constrained problemspt
dc.typearticle-
degois.publication.firstPage525pt
degois.publication.lastPage559pt
degois.publication.titleComputational Optimization and Applicationspt
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s10589-019-00062-4pt
dc.peerreviewedyespt
dc.identifier.doi10.1007/s10589-019-00062-4pt
degois.publication.volume72pt
dc.date.embargo2020-01-01*
uc.date.periodoEmbargo365pt
item.fulltextCom Texto completo-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.grantfulltextopen-
item.cerifentitytypePublications-
crisitem.author.orcid0000-0002-5021-2357-
crisitem.author.orcid0000-0003-1097-6384-
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais
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