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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorTian, Ben_US
dc.creatorLi, Den_US
dc.creatorYang, Xen_US
dc.date.accessioned2016-12-19T08:54:16Z-
dc.date.available2016-12-19T08:54:16Z-
dc.identifier.issn1055-6788en_US
dc.identifier.urihttp://hdl.handle.net/10397/60984-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2016 Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Optimization Methods and Software on 24 Feb 2016 (published online), available at: http://www.tandfonline.com/10.1080/10556788.2016.1146266en_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.spage775en_US
dc.identifier.epage790en_US
dc.identifier.volume31en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1080/10556788.2016.1146266en_US
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.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationOptimization methods and software, 2016, v. 31, no. 4, p. 775-790en_US
dcterms.isPartOfOptimization methods and softwareen_US
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 journalen_US
dc.description.validate201804_a bcmaen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberAMA-0567-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextPolyUen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6619447-
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