dc.creatorBurdakov O.P.
dc.creatorMartinez J.M.
dc.creatorPilotta E.A.
dc.date2002
dc.date2015-06-30T16:40:54Z
dc.date2015-11-26T15:31:46Z
dc.date2015-06-30T16:40:54Z
dc.date2015-11-26T15:31:46Z
dc.date.accessioned2018-03-28T22:40:13Z
dc.date.available2018-03-28T22:40:13Z
dc.identifier
dc.identifierAnnals Of Operations Research. , v. 117, n. 1-4, p. 51 - 70, 2002.
dc.identifier2545330
dc.identifier10.1023/A:1021561204463
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-1642543147&partnerID=40&md5=25eae5f0516204dd71cc3371243f1c33
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/101528
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/101528
dc.identifier2-s2.0-1642543147
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1262308
dc.descriptionA new algorithm for solving smooth large-scale minimization problems with bound constraints is introduced. The way of dealing with active constraints is similar to the one used in some recently introduced quadratic solvers. A limited-memory multipoint symmetric secant method for approximating the Hessian is presented. Positive-definiteness of the Hessian approximation is not enforced. A combination of trust-region and conjugate-gradient approaches is used to explore a useful negative curvature information. Global convergence is proved for a general model algorithm. Results of numerical experiments are presented.
dc.description117
dc.description1-4
dc.description51
dc.description70
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dc.languageen
dc.publisher
dc.relationAnnals of Operations Research
dc.rightsfechado
dc.sourceScopus
dc.titleA Limited-memory Multipoint Symmetric Secant Method For Bound Constrained Optimization
dc.typeActas de congresos


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