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Stopping Rules for the Stochastic Nested Partitions Method

Author

Listed:
  • Leyuan Shi

    (University of Wisconsin)

  • Sigurdur O´lafsson

    (Iowa State University)

Abstract

We have recently developed a global optimization methodology for solving combinatorial problems with either deterministic or stochastic performance functions. This method, the Nested Partitions (NP) method has been shown to generate a Markov chain and with probability one to converge to a global optimum. In this paper, we study the rate of convergence of the method through the use of Markov Chain Monte Carlo (MCMC) methods, and use this to derive stopping rules that can be applied during simulation-based optimization. A numerical example serves to illustrate the feasibility of our approach.

Suggested Citation

  • Leyuan Shi & Sigurdur O´lafsson, 2000. "Stopping Rules for the Stochastic Nested Partitions Method," Methodology and Computing in Applied Probability, Springer, vol. 2(1), pages 37-58, April.
    • Handle: RePEc:spr:metcap:v:2:y:2000:i:1:d:10.1023_a:1010055101140
    DOI: 10.1023/A:1010055101140
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    Cited by:

    1. Michael Macgregor Perry, 2021. "Fisheries Management in Congested Waters: A Game-Theoretic Assessment of the East China Sea," Papers 2110.13966, arXiv.org, revised Feb 2022.

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