Regularized Decomposition of Stochastic Programs: Algorithmic Techniques and Numerical Results

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/3806 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/3806 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/3806 archive 351 disk0/00/00/37/91 2016-01-15 02:02:54 2021-08-27 17:14:29 2016-01-15 02:02:54 monograph show 2 Ruszczynski A. 1475 Regularized Decomposition of Stochastic Programs: Algorithmic Techniques and Numerical Results pub iis_met prog_opt A finitely convergent non-simplex method for large scale structured linear programming problems arising in stochastic programming is presented. The method combines the ideas of the Dantzig-Wolfe decomposition principle and modern nonsmooth optimization methods. Algorithmic techniques taking advantage of properties of stochastic programs are described and numerical results for large real world problems reported. 1993-04 published WP-93-021 WP-93-021 10 1544 public working_paper IIASA, Laxenburg, Austria 29 FALSE info:eu-repo/semantics/book