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Computational assessment of distributed decomposition methods for stochastic linear programs

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  • Vladimirou, Hercules

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  • 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.
    • Handle: RePEc:eee:ejores:v:108:y:1998:i:3:p:653-670
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    References listed on IDEAS

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    1. Randall S. Hiller & Jonathan Eckstein, 1993. "Stochastic Dedication: Designing Fixed Income Portfolios Using Massively Parallel Benders Decomposition," Management Science, INFORMS, vol. 39(11), pages 1422-1438, November.
    2. John R. Birge & Liqun Qi, 1988. "Computing Block-Angular Karmarkar Projections with Applications to Stochastic Programming," Management Science, INFORMS, vol. 34(12), pages 1472-1479, December.
    3. M. I. Kusy & W. T. Ziemba, 1986. "A Bank Asset and Liability Management Model," Operations Research, INFORMS, vol. 34(3), pages 356-376, June.
    4. Daniel Bienstock & Jeremy F. Shapiro, 1988. "Optimizing Resource Acquisition Decisions by Stochastic Programming," Management Science, INFORMS, vol. 34(2), pages 215-229, February.
    5. John M. Mulvey & Hercules Vladimirou, 1992. "Stochastic Network Programming for Financial Planning Problems," Management Science, INFORMS, vol. 38(11), pages 1642-1664, November.
    6. László Somlyódy & Roger J.-B. Wets, 1988. "Stochastic Optimization Models for Lake Eutrophication Management," Operations Research, INFORMS, vol. 36(5), pages 660-681, October.
    7. A. Ruszczynski, 1993. "Regularized Decomposition of Stochastic Programs: Algorithmic Techniques and Numerical Results," Working Papers wp93021, International Institute for Applied Systems Analysis.
    8. 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.
    9. 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.
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    Cited by:

    1. Barro, Diana & Canestrelli, Elio, 2005. "Dynamic portfolio optimization: Time decomposition using the Maximum Principle with a scenario approach," European Journal of Operational Research, Elsevier, vol. 163(1), pages 217-229, May.
    2. Unai Aldasoro & Laureano Escudero & María Merino & Juan Monge & Gloria Pérez, 2015. "On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0–1 problems under uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 703-742, October.
    3. Diana Barro & Elio Canestrelli, 2005. "Time and nodal decomposition with implicit non-anticipativity constraints in dynamic portfolio optimization," GE, Growth, Math methods 0510011, University Library of Munich, Germany.
    4. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    5. A. Marín & J. Salmerón, 2001. "A risk function for the stochastic modeling of electric capacity expansion," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(8), pages 662-683, December.
    6. Panos Parpas & Berç Rustem, 2007. "Computational Assessment of Nested Benders and Augmented Lagrangian Decomposition for Mean-Variance Multistage Stochastic Problems," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 239-247, May.

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