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Parallel MCMC methods for global optimization

Author

Listed:
  • Zhang Lihao

    (Applied Mathematics & Statistics, Stony Brook University, Stony Brook11794-3600, USA)

  • Ye Zeyang

    (Applied Mathematics & Statistics, Stony Brook University, Stony Brook11794-3600, USA)

  • Deng Yuefan

    (Applied Mathematics & Statistics, Stony Brook University, Stony Brook11794-3600, USA)

Abstract

We introduce a parallel scheme for simulated annealing, a widely used Markov chain Monte Carlo (MCMC) method for optimization. Our method is constructed and analyzed under the classical framework of MCMC. The benchmark function for optimization is used for validation and verification of the parallel scheme. The experimental results, along with the proof based on statistical theory, provide us with insights into the mechanics of the parallelization of simulated annealing for high parallel efficiency or scalability for large parallel computers.

Suggested Citation

  • Zhang Lihao & Ye Zeyang & Deng Yuefan, 2019. "Parallel MCMC methods for global optimization," Monte Carlo Methods and Applications, De Gruyter, vol. 25(3), pages 227-237, September.
    • Handle: RePEc:bpj:mcmeap:v:25:y:2019:i:3:p:227-237:n:4
    DOI: 10.1515/mcma-2019-2043
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    References listed on IDEAS

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    1. D. Abramson, 1991. "Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms," Management Science, INFORMS, vol. 37(1), pages 98-113, January.
    2. Bruce Hajek, 1988. "Cooling Schedules for Optimal Annealing," Mathematics of Operations Research, INFORMS, vol. 13(2), pages 311-329, May.
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