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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.

Suggested Citation

  • A. Ruszczynski & A. Swietanowski, 1996. "On the Regularized Decomposition Method for Two Stage Stochastic Linear Problems," Working Papers wp96014, International Institute for Applied Systems Analysis.
    • Handle: RePEc:wop:iasawp:wp96014
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    References listed on IDEAS

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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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    Cited by:

    1. Vladimirou, Hercules, 1998. "Computational assessment of distributed decomposition methods for stochastic linear programs," European Journal of Operational Research, Elsevier, vol. 108(3), pages 653-670, August.

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