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An Asymptotically Optimal Set Approach for Simulation Optimization

Author

Listed:
  • Liujia Hu

    (Quantitative Advisory Service, Ernst and Young LLP, New York, New York 10036)

  • Sigrún Andradóttir

    (Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

We propose an asymptotically optimal set (AOS) approach for solving stochastic optimization problems with discrete or continuous feasible regions. Our AOS approach is a framework for designing provably convergent algorithms that are adaptive in seeking new points and in resampling or discarding already sampled points. The framework is an improvement over the adaptive search with resampling (ASR) method for stochastic optimization in that it spends less effort on inferior points and uses a more robust estimate of the optimal solution. We present conditions guaranteeing that the AOS approach is globally convergent and will eventually discard suboptimal sampled points with probability one, compare the algorithms, and analyze when (additional) resampling (beyond the minimum) is desirable. Our theoretical results show that AOS has stronger performance guarantees than ASR. Our numerical results suggest that AOS makes substantial improvements over ASR, especially for difficult problems with large numbers of local optima.

Suggested Citation

  • Liujia Hu & Sigrún Andradóttir, 2019. "An Asymptotically Optimal Set Approach for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 31(1), pages 21-39, February.
    • Handle: RePEc:inm:orijoc:v:31:y:2019:i:1:p:21-39
    DOI: 10.1287/ijoc.2018.0811
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    References listed on IDEAS

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