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dc.contributor.authorde Oliveira, W.
dc.date.accessioned2016-06-13T13:12:37Z
dc.date.available2016-06-13T13:12:37Z
dc.date.issued2016-01-01
dc.identifier.issn1134-5764
dc.identifier.urihttp://hdl.handle.net/20.500.11824/130
dc.description.abstractWe propose regularized cutting-plane methods for solving mixed-integer nonlinear programming problems with nonsmooth convex objective and constraint functions. The given methods iteratively search for trial points in certain localizer sets, constructed by employing linearizations of the involved functions. New trial points can be chosen in several ways; for instance, by minimizing a regularized cutting-plane model if functions are costly. When dealing with hard-to-evaluate functions, the goal is to solve the optimization problem by performing as few function evaluations as possible. Numerical experiments comparing the proposed algorithms with classical methods in this area show the effectiveness of our approach.
dc.formatapplication/pdf
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectCutting-plane method
dc.subjectMixed-integer programming
dc.subjectNonsmooth optimization
dc.titleRegularized optimization methods for convex MINLP problems
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1007/s11750-016-0413-4
dc.relation.publisherversionhttp://link.springer.com/article/10.1007%2Fs11750-016-0413-4
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleTOPen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
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