A Derivative-free Nonmonotone Line-search Technique For Unconstrained Optimization

dc.creatorDiniz-Ehrhardt M.A.
dc.creatorMartinez J.M.
dc.creatorRaydan M.
dc.date2008
dc.date2015-06-30T19:31:24Z
dc.date2015-11-26T14:45:18Z
dc.date2015-06-30T19:31:24Z
dc.date2015-11-26T14:45:18Z
dc.date.accessioned2018-03-28T21:54:28Z
dc.date.available2018-03-28T21:54:28Z
dc.identifier
dc.identifierJournal Of Computational And Applied Mathematics. , v. 219, n. 2, p. 383 - 397, 2008.
dc.identifier3770427
dc.identifier10.1016/j.cam.2007.07.017
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-46749084262&partnerID=40&md5=719f960e0576e0ba1385a3c5d5f615a4
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/106588
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/106588
dc.identifier2-s2.0-46749084262
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1252430
dc.descriptionA tolerant derivative-free nonmonotone line-search technique is proposed and analyzed. Several consecutive increases in the objective function and also nondescent directions are admitted for unconstrained minimization. To exemplify the power of this new line search we describe a direct search algorithm in which the directions are chosen randomly. The convergence properties of this random method rely exclusively on the line-search technique. We present numerical experiments, to illustrate the advantages of using a derivative-free nonmonotone globalization strategy, with approximated-gradient type methods and also with the inverse SR1 update that could produce nondescent directions. In all cases we use a local variation finite differences approximation to the gradient. © 2007 Elsevier B.V. All rights reserved.
dc.description219
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dc.languageen
dc.publisher
dc.relationJournal of Computational and Applied Mathematics
dc.rightsfechado
dc.sourceScopus
dc.titleA Derivative-free Nonmonotone Line-search Technique For Unconstrained Optimization
dc.typeArtículos de revistas


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