Parallel MCMC methods for global optimization

Lihao Zhang; Zeyang Ye; Yuefan Deng

doi:10.1515/mcma-2019-2043

Parallel MCMC methods for global optimization

Lihao Zhang
, Zeyang Ye
, Yuefan Deng

Applied Mathematics and Statistics

Stony Brook University

Stony Brook University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English
Pages (from-to)	227-237
Number of pages	11
Journal	Monte Carlo Methods and Applications
Volume	25
Issue number	3
DOIs	https://doi.org/10.1515/mcma-2019-2043
State	Published - Sep 1 2019

Keywords

Markov chain Monte Carlo
Parallel computing
global optimization
simulated annealing

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10.1515/mcma-2019-2043

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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.",

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AU - Deng, Yuefan

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N2 - 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.

AB - 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.

KW - Markov chain Monte Carlo

KW - Parallel computing

KW - global optimization

KW - simulated annealing

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JO - Monte Carlo Methods and Applications

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ER -

Parallel MCMC methods for global optimization

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Keywords

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