Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/60984
DC FieldValueLanguage
dc.contributorDepartment of Applied Mathematics-
dc.creatorTian, B-
dc.creatorLi, D-
dc.creatorYang, XQ-
dc.date.accessioned2016-12-19T08:54:16Z-
dc.date.available2016-12-19T08:54:16Z-
dc.identifier.issn1055-6788-
dc.identifier.urihttp://hdl.handle.net/10397/60984-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectExponential convergence rateen_US
dc.subjectImplicit complementarity problemsen_US
dc.subjectLower order penalty methoden_US
dc.subjectTrust-region Gauss–Newton methoden_US
dc.titleAn unconstrained differentiable penalty method for implicit complementarity problemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage775-
dc.identifier.epage790-
dc.identifier.volume31-
dc.identifier.issue4-
dc.identifier.doi10.1080/10556788.2016.1146266-
dcterms.abstractIn this paper, we introduce an unconstrained differentiable penalty method for solving implicit complementarity problems, which has an exponential convergence rate under the assumption of a uniform ξ-P-function. Instead of solving the unconstrained penalized equations directly, we consider a corresponding unconstrained optimization problem and apply the trust-region Gauss–Newton method to solve it. We prove that the local solution of the unconstrained optimization problem identifies that of the complementarity problems under monotone assumptions. We carry out numerical experiments on the test problems from MCPLIB, and show that the proposed method is efficient and robust.-
dcterms.bibliographicCitationOptimization methods and software, 2016, v. 31, no. 4, p. 775-790-
dcterms.isPartOfOptimization methods and software-
dcterms.issued2016-
dc.identifier.isiWOS:000377145400007-
dc.identifier.scopus2-s2.0-84959047367-
dc.identifier.ros2016000197-
dc.identifier.rosgroupid2016000196-
dc.description.ros2016-2017 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201804_a bcma-
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