Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5882
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dc.contributorDepartment of Applied Mathematics-
dc.creatorZhou, W-
dc.creatorChen, X-
dc.date.accessioned2014-12-11T08:24:24Z-
dc.date.available2014-12-11T08:24:24Z-
dc.identifier.issn1052-6234-
dc.identifier.urihttp://hdl.handle.net/10397/5882-
dc.language.isoenen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.rights© 2010 Society for Industrial and Applied Mathematicsen_US
dc.subjectNonlinear least squaresen_US
dc.subjectGauss–Newton methoden_US
dc.subjectBFGS methoden_US
dc.subjectStructured quasi-Newton methoden_US
dc.subjectGlobal convergenceen_US
dc.subjectQuadratic convergenceen_US
dc.titleGlobal convergence of a new hybrid Gauss-Newton structured BFGS method for nonlinear least squares problemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2422-
dc.identifier.epage2441-
dc.identifier.volume20-
dc.identifier.issue5-
dc.identifier.doi10.1137/090748470-
dcterms.abstractIn this paper, we propose a hybrid Gauss–Newton structured BFGS method with a new update formula and a new switch criterion for the iterative matrix to solve nonlinear least squares problems. We approximate the second term in the Hessian by a positive definite BFGS matrix. Under suitable conditions, global convergence of the proposed method with a backtracking line search is established. Moreover, the proposed method automatically reduces to the Gauss–Newton method for zero residual problems and the structured BFGS method for nonzero residual problems in a neighborhood of an accumulation point. A locally quadratic convergence rate for zero residual problems and a locally superlinear convergence rate for nonzero residual problems are obtained for the proposed method. Some numerical results are given to compare the proposed method with some existing methods.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSIAM journal on optimization, 2010, v. 20, no. 5, p. 2422–2441-
dcterms.isPartOfSIAM Journal on optimization-
dcterms.issued2010-
dc.identifier.isiWOS:000280992000014-
dc.identifier.scopus2-s2.0-77956065888-
dc.identifier.eissn1095-7189-
dc.identifier.rosgroupidr47576-
dc.description.ros2009-2010 > 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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