Irreversible Monte Carlo algorithms for efficient sampling

Konstantin S. Turitsyn, Michael Chertkov, Marija Vucelja

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

Equilibrium systems evolve according to Detailed Balance (DB). This principle guided the development of Monte Carlo sampling techniques, of which the MetropolisHastings (MH) algorithm is the famous representative. It is also known that DB is sufficient but not necessary. We construct irreversible deformation of a given reversible algorithm capable of dramatic improvement of sampling from known distribution. Our transformation modifies transition rates keeping the structure of transitions intact. To illustrate the general scheme we design an Irreversible version of MetropolisHastings (IMH) and test it on an example of a spin cluster. Standard MH for the model suffers from critical slowdown, while IMH is free from critical slowdown.

Original languageEnglish (US)
Pages (from-to)410-414
Number of pages5
JournalPhysica D: Nonlinear Phenomena
Volume240
Issue number4-5
DOIs
StatePublished - Feb 15 2011
Externally publishedYes

Keywords

  • MCMC algorithms
  • Mixing
  • Monte Carlo methods

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Condensed Matter Physics
  • Applied Mathematics

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