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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/66314
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dc.contributor.authorAndreani, R.-
dc.contributor.authorMartínez, J. M.-
dc.contributor.authorSvaiter, B. F.-
dc.date.accessioned2014-05-27T11:19:58Z-
dc.date.accessioned2016-10-25T18:16:40Z-
dc.date.available2014-05-27T11:19:58Z-
dc.date.available2016-10-25T18:16:40Z-
dc.date.issued2000-12-01-
dc.identifierhttp://dx.doi.org/10.1080/01630560008816976-
dc.identifier.citationNumerical Functional Analysis and Optimization, v. 21, n. 5-6, p. 589-600, 2000.-
dc.identifier.issn0163-0563-
dc.identifier.urihttp://hdl.handle.net/11449/66314-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/66314-
dc.description.abstractA variational inequality problem (VIP) satisfying a constraint qualification can be reduced to a mixed complementarity problem (MCP). Monotonicity of the VIP implies that the MCP is also monotone. Introducing regularizing perturbations, a sequence of strictly monotone mixed complementarity problems is generated. It is shown that, if the original problem is solvable, the sequence of computable inexact solutions of the strictly monotone MCP's is bounded and every accumulation point is a solution. Under an additional condition on the precision used for solving each subproblem, the sequence converges to the minimum norm solution of the MCP. Copyright © 2000 by Marcel Dekker, Inc.en
dc.format.extent589-600-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectComplementarity-
dc.subjectInexact solutions-
dc.subjectMinimization algorithms-
dc.subjectPerturbations-
dc.subjectReformulation-
dc.subjectVariational inequalities-
dc.subjectAlgorithms-
dc.subjectConstraint theory-
dc.subjectConvergence of numerical methods-
dc.subjectMathematical operators-
dc.subjectOptimization-
dc.subjectPerturbation techniques-
dc.subjectMixed complementarity problems-
dc.subjectMonotone operators-
dc.subjectVariational inequality problem-
dc.subjectVariational techniques-
dc.titleOn the regularization of mixed complementarity problemsen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.contributor.institutionIMPA-
dc.description.affiliationDepartment of Computer Science and Statistics University of the State of S. Paulo (UNESP), C.P. 136, CEP 15054-000, Sao Jose Rio Preto-SP-
dc.description.affiliationDepartment of Applied Mathematics IMECC-UNICAMP University of Camp-inas, CP 6065, 13081-970 Campinas SP-
dc.description.affiliationIMPA-
dc.description.affiliationUnespDepartment of Computer Science and Statistics University of the State of S. Paulo (UNESP), C.P. 136, CEP 15054-000, Sao Jose Rio Preto-SP-
dc.identifier.doi10.1080/01630560008816976-
dc.identifier.wosWOS:000089189600004-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofNumerical Functional Analysis and Optimization-
dc.identifier.scopus2-s2.0-0342521589-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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