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• W2131522300 abstract "Open AccessOpen Access licenseAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Go to SectionOpen AccessOpen Access license HomeStochastic SystemsVol. 1, No. 2 Subsampling Algorithms for Semidefinite ProgrammingAlexandre d’AspremontAlexandre d’AspremontPublished Online:7 Sep 2011https://doi.org/10.1287/10-SSY018AbstractWe derive a stochastic gradient algorithm for semidefinite optimization using randomization techniques. The algorithm uses subsampling to reduce the computational cost of each iteration and the subsampling ratio explicitly controls granularity, i.e. the tradeoff between cost per iteration and total number of iterations. Furthermore, the total computational cost is directly proportional to the complexity (i.e. rank) of the solution. We study numerical performance on some large-scale problems arising in statistical learning. Previous Back to Top Next FiguresReferencesRelatedInformation Volume 1, Issue 2December 2011Pages 209-436 Article Information Metrics Downloaded 85 times in the past 12 months Information Received:November 01, 2010Published Online:September 07, 2011 Copyright © 2011, The author(s)Cite asAlexandre d’Aspremont (2011) Subsampling Algorithms for Semidefinite Programming. Stochastic Systems 1(2):274-305. https://doi.org/10.1287/10-SSY018 KeywordsSemidefinite programmingstochastic optimizationsubsamplingPDF download" @default.
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• W2131522300 title "Subsampling algorithms for semidefinite programming" @default.
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