Regularized Decomposition of Stochastic Programs: Algorithmic Techniques and Numerical Results

Ruszczynski A (1993). Regularized Decomposition of Stochastic Programs: Algorithmic Techniques and Numerical Results. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-93-021

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Abstract

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.

Item Type: Monograph (IIASA Working Paper)
Research Programs: Optimization under Uncertainty (OPT)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:02
Last Modified: 22 Jul 2016 21:49
URI: http://pure.iiasa.ac.at/3791

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