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Scalable parallel computations forlarge-scale stochastic programming


  • H. Vladimirou
  • S.A. Zenios


Stochastic programming provides an effective framework for addressing decision problemsunder uncertainty in diverse fields. Stochastic programs incorporate many possiblecontingencies so as to proactively account for randomness in their input data; thus, theyinevitably lead to very large optimization programs. Consequently, efficient algorithms thatcan exploit the capabilities of advanced computing technologies ‐ including multiprocessorcomputers ‐ become imperative to solve large‐scale stochastic programs. This paper surveysthe state‐of‐the‐art in parallel algorithms for stochastic programming. Algorithms are reviewed,classified and compared. Qualitative comparisons are based on the applicability, scope, easeof implementation, robustness and reliability of each algorithm, while quantitative comparisonsare based on the computational performance of algorithmic implementations onmultiprocessor systems. Emphasis is placed on the potential of parallel algorithms to solvelarge‐scale stochastic programs. Copyright Kluwer Academic Publishers 1999

Suggested Citation

  • H. Vladimirou & S.A. Zenios, 1999. "Scalable parallel computations forlarge-scale stochastic programming," Annals of Operations Research, Springer, vol. 90(0), pages 87-129, January.
  • Handle: RePEc:spr:annopr:v:90:y:1999:i:0:p:87-129:10.1023/a:1018977102079
    DOI: 10.1023/A:1018977102079

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    Cited by:

    1. Jacek Gondzio & Andreas Grothey, 2007. "Parallel interior-point solver for structured quadratic programs: Application to financial planning problems," Annals of Operations Research, Springer, vol. 152(1), pages 319-339, July.
    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. Topaloglou, Nikolas & Vladimirou, Hercules & Zenios, Stavros A., 2008. "A dynamic stochastic programming model for international portfolio management," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1501-1524, March.
    4. Aldasoro, Unai & Escudero, Laureano F. & Merino, María & Pérez, Gloria, 2017. "A parallel Branch-and-Fix Coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0–1 problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 590-606.
    5. Miles Lubin & J. Hall & Cosmin Petra & Mihai Anitescu, 2013. "Parallel distributed-memory simplex for large-scale stochastic LP problems," Computational Optimization and Applications, Springer, vol. 55(3), pages 571-596, July.

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