Markov chain Monte Carlo method without detailed balance.

We present a specific algorithm that generally satisfies the balance condition without imposing the detailed balance in the Markov chain Monte Carlo. In our algorithm, the average rejection rate is minimized, and even reduced to zero in many relevant cases. The absence of the detailed balance also introduces a net stochastic flow in a configuration space, which further boosts up the convergence. We demonstrate that the autocorrelation time of the Potts model becomes more than 6 times shorter than that by the conventional Metropolis algorithm. Based on the same concept, a bounce-free worm algorithm for generic quantum spin models is formulated as well.

• Publication year

  2010

• Venue

  Physical Review Letters

• Publication date

  2010-07-14

• Fields of study

  Medicine, Physics, Computer Science, Mathematics

• Identifiers
  DOI 10.1103/PhysRevLett.105.120603arXiv 1007.2262PMID 20867621
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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