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dc.contributor.authorBeddiaf, S.
dc.date.accessioned2009-09-07T10:48:50Z
dc.date.available2009-09-07T10:48:50Z
dc.date.issued2009
dc.identifier.citationBeddiaf , S 2009 , ' Continuous steepest descent path for traversing non-convex regions ' , Advanced Modeling and Optimization , vol. 11 , no. 1 , pp. 3-24 .
dc.identifier.issn1841-4311
dc.identifier.otherPURE: 153537
dc.identifier.otherPURE UUID: fdbac709-602a-4878-9353-ac295d65df8c
dc.identifier.otherdspace: 2299/3826
dc.identifier.urihttp://hdl.handle.net/2299/3826
dc.descriptionOriginal article can be found at: http://www.ici.ro/camo/journal/jamo.htm
dc.description.abstractThis paper revisits the ideas of seeking unconstrained minima by following a continuous steepest descent path (CSDP). We are especially interested in the merits of such an approach in regions where the objective function is non-convex and Newton-like methods become ineffective. The paper combines ODE-trajectory following with trust-region ideas to give an algorithm which performs curvilinear searches on each iteration. Progress along the CSDP is governed both by the decrease in function value and measures of the accuracy of a local quadratic model. Experience with a prototype implementation of the algorithm is promising and it is shown to be competitive with more conventional line search and trust region approaches. In particular, it is also shown to perform well in comparison with the, superficially similar, gradient-flow method proposed by Behrman.en
dc.language.isoeng
dc.relation.ispartofAdvanced Modeling and Optimization
dc.rightsOpen
dc.titleContinuous steepest descent path for traversing non-convex regionsen
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Physics, Astronomy and Mathematics
dcterms.dateAccepted2009
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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