An optimal algorithm for minimization of quadratic functions with bounded spectrum subject to separable convex inequality and linear equality constraints

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dc.contributor.author Dostál, Zdeněk
dc.contributor.author Kučera, Radek
dc.date.accessioned 2011-01-31T09:01:43Z
dc.date.available 2011-01-31T09:01:43Z
dc.date.issued 2010
dc.identifier.citation SIAM journal on optimization. 2010, vol. 20, issue 6, p. 2913-2938. en
dc.identifier.issn 1052-6234
dc.identifier.issn 1095-7189
dc.identifier.uri http://hdl.handle.net/10084/83754
dc.description.abstract An, 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.extent 322881 bytes cs
dc.format.mimetype application/pdf cs
dc.language.iso en en
dc.publisher Society for Industrial and Applied Mathematics en
dc.relation.ispartofseries SIAM journal on optimization en
dc.relation.uri http://dx.doi.org/10.1137/090751414 en
dc.rights © SIAM en
dc.subject quadratic function en
dc.subject separable convex constraints en
dc.subject active set en
dc.subject augmented Lagrangian en
dc.subject gradient projections en
dc.subject convergence rate en
dc.subject optimality en
dc.title An optimal algorithm for minimization of quadratic functions with bounded spectrum subject to separable convex inequality and linear equality constraints en
dc.type Article en
dc.identifier.location Není ve fondu ÚK en
dc.identifier.doi 10.1137/090751414
dc.rights.access openAccess
dc.type.version publishedVersion
dc.identifier.wos 000285547100008

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