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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: | 27 Aug 2021 17:14 |
| URI: | https://pure.iiasa.ac.at/3791 |
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