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dc.contributor.authorFrimannslund, Lennarteng
dc.contributor.authorSteihaug, Trondeng
dc.date.accessioned2012-05-02T08:01:23Z
dc.date.available2012-05-02T08:01:23Z
dc.date.issued2011eng
dc.PublishedInternational Journal on Advances in Software 2011 4(3-4)en
dc.identifier.urihttps://hdl.handle.net/1956/5783
dc.description.abstractA new derivative-free optimization method for unconstrained optimization of partially separable functions is presented. Using average curvature information computed from sampled function values the method generates an average Hessian-like matrix and uses its eigenvectors as new search directions. Numerical experiments demonstrate that this new derivative free optimization method has the very desirable property of avoiding saddle points. This is illustrated on two test functions and compared to other well known derivative free methods. Further, we compare the efficiency of the new method with two classical derivative methods using a class of testproblems.en_US
dc.language.isoengeng
dc.publisherIARIAen_US
dc.relation.urihttp://www.iariajournals.org/software/tocv4n34.htmlen
dc.relation.urihttp://www.iariajournals.org/software/tocv4n34.htmleng
dc.subjectSparsityeng
dc.subjectDerivative-free optimizationeng
dc.subjectGenerating set searcheng
dc.subjectSaddle pointseng
dc.titleOn a New Method for Derivative Free Optimizationen_US
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright the authors. All rights reserveden_US
dc.subject.nsiVDP::Technology: 500::Information and communication technology: 550en_US


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