Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6108
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dc.contributorDepartment of Applied Mathematics-
dc.creatorZhang, C-
dc.creatorChen, X-
dc.date.accessioned2014-12-11T08:28:21Z-
dc.date.available2014-12-11T08:28:21Z-
dc.identifier.issn1052-6234-
dc.identifier.urihttp://hdl.handle.net/10397/6108-
dc.language.isoenen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.rights© 2009 Society for Industrial and Applied Mathematicsen_US
dc.subjectSmoothing projected gradient methoden_US
dc.subjectNonsmoothen_US
dc.subjectNonconvexen_US
dc.subjectonstrained optimizationen_US
dc.subjectStochastic linear complementarity problemen_US
dc.subjectImage restorationen_US
dc.titleSmoothing projected gradient method and its application to stochastic linear complementarity problemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage627-
dc.identifier.epage649-
dc.identifier.volume20-
dc.identifier.issue2-
dc.identifier.doi10.1137/070702187-
dcterms.abstractA smoothing projected gradient (SPG) method is proposed for the minimization problem on a closed convex set, where the objective function is locally Lipschitz continuous but nonconvex, nondifferentiable. We show that any accumulation point generated by the SPG method is a stationary point associated with the smoothing function used in the method, which is a Clarke stationary point in many applications. We apply the SPG method to the stochastic linear complementarity problem (SLCP) and image restoration problems. We study the stationary point defined by the directional derivative and provide necessary and sufficient conditions for a local minimizer of the expected residual minimization (ERM) formulation of SLCP. Preliminary numerical experiments using the SPG method for solving randomly generated SLCP and image restoration problems of large sizes show that the SPG method is promising.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSIAM journal on optimization, 2009, v. 20, no. 2, p. 627-649-
dcterms.isPartOfSIAM journal on optimization-
dcterms.issued2009-
dc.identifier.isiWOS:000268859300003-
dc.identifier.scopus2-s2.0-70450260767-
dc.identifier.eissn1095-7189-
dc.identifier.rosgroupidr40656-
dc.description.ros2008-2009 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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