Parallel MCMC methods for global optimization

Lihao Zhang, Zeyang Ye, Yuefan Deng

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)227-237
Number of pages11
JournalMonte Carlo Methods and Applications
Issue number3
StatePublished - Sep 1 2019


  • global optimization
  • Markov chain Monte Carlo
  • Parallel computing
  • simulated annealing

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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