Zobrazit minimální záznam

dc.contributor.authorDostál, Zdeněk
dc.contributor.authorKučera, Radek
dc.date.accessioned2011-01-31T09:01:43Z
dc.date.available2011-01-31T09:01:43Z
dc.date.issued2010
dc.identifier.citationSIAM journal on optimization. 2010, vol. 20, issue 6, p. 2913-2938.en
dc.identifier.issn1052-6234
dc.identifier.issn1095-7189
dc.identifier.urihttp://hdl.handle.net/10084/83754
dc.description.abstractAn, in a sense, optimal algorithm for minimization of quadratic functions subject to separable convex inequality and linear equality constraints is presented. Its unique feature is an error bound in terms of bounds on the spectrum of the Hessian of the cost function. If applied to a class of problems with the spectrum of the Hessians in a given positive interval, the algorithm can find approximate solutions in a uniformly bounded number of simple iterations, such as matrix-vector multiplications. Moreover, if the class of problems admits a sparse representation of the Hessian, it simply follows that the cost of the solution is proportional to the number of unknowns. Theoretical results are illustrated by numerical experiments.en
dc.format.extent322881 bytescs
dc.format.mimetypeapplication/pdfcs
dc.language.isoenen
dc.publisherSociety for Industrial and Applied Mathematicsen
dc.relation.ispartofseriesSIAM journal on optimizationen
dc.relation.urihttp://dx.doi.org/10.1137/090751414en
dc.rights© SIAMen
dc.subjectquadratic functionen
dc.subjectseparable convex constraintsen
dc.subjectactive seten
dc.subjectaugmented Lagrangianen
dc.subjectgradient projectionsen
dc.subjectconvergence rateen
dc.subjectoptimalityen
dc.titleAn optimal algorithm for minimization of quadratic functions with bounded spectrum subject to separable convex inequality and linear equality constraintsen
dc.typearticleen
dc.identifier.locationNení ve fondu ÚKen
dc.identifier.doi10.1137/090751414
dc.rights.accessopenAccess
dc.type.versionpublishedVersion
dc.identifier.wos000285547100008


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Zobrazit minimální záznam