Parallel MCMC methods for global optimization

Zhang Lihao (), Ye Zeyang () and Deng Yuefan ()
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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

Monte Carlo Methods and Applications, 2019, vol. 25, issue 3, 227-237

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.

Keywords: Parallel computing; Markov chain Monte Carlo; simulated annealing; global optimization (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/mcma-2019-2043

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