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Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization

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  • A. Futschik
  • G.C. Pflug

Abstract

Approximate solutions for discrete stochastic optimization problems are often obtained via simulation. It is reasonable to complement these solutions by confidence regions for the argmin-set. We address the question, how a certain total number of random draws should be distributed among the set of alternatives. We propose a one-step allocation rule which turns out to be asymptotically optimal in the case of normal errors for two goals: To minimize the costs caused by using only an approximate solution and to minimize the expected size of the confidence sets.

Suggested Citation

  • A. Futschik & G.C. Pflug, 1996. "Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization," Working Papers wp96023, International Institute for Applied Systems Analysis.
    • Handle: RePEc:wop:iasawp:wp96023
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    References listed on IDEAS

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    1. Andrzej Ruszczyński, 1987. "A Linearization Method for Nonsmooth Stochastic Programming Problems," Mathematics of Operations Research, INFORMS, vol. 12(1), pages 32-49, February.
    2. Marguerite Frank & Philip Wolfe, 1956. "An algorithm for quadratic programming," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 95-110, March.
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