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On the Regularized Decomposition Method for Two Stage Stochastic Linear Problems

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  • A. Ruszczynski
  • A. Swietanowski

Abstract

A new approach to the regularized decomposition (RD) algorithm for two stage stochastic problems is presented. The RD method combines the ideas of the Dantzig-Wolfe decomposition principle and modern nonsmooth optimization methods. A new subproblem solution method using the primal simplex algorithm for linear programming is proposed and then tested on a number of large scale problems. The new approach makes it possible to use a more general problem formulation and thus allows considerably more freedom when creating the model. The computational results are highly encouraging.

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File URL: http://www.iiasa.ac.at/Publications/Documents/WP-96-014.pdf
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File URL: http://www.iiasa.ac.at/Publications/Documents/WP-96-014.ps
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Bibliographic Info

Paper provided by International Institute for Applied Systems Analysis in its series Working Papers with number wp96014.

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Date of creation: Feb 1996
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Handle: RePEc:wop:iasawp:wp96014

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  1. A. Swietanowski, 1995. "A Penalty Based Simplex Method for Linear Programming," Working Papers wp95005, International Institute for Applied Systems Analysis.
  2. A. Ruszczynski, 1993. "Regularized Decomposition of Stochastic Programs: Algorithmic Techniques and Numerical Results," Working Papers wp93021, International Institute for Applied Systems Analysis.
  3. Birge, John R. & Louveaux, Francois V., 1988. "A multicut algorithm for two-stage stochastic linear programs," European Journal of Operational Research, Elsevier, vol. 34(3), pages 384-392, March.
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