Irreversible Monte Carlo algorithms for efficient sampling
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.
 Publication:

Physica D Nonlinear Phenomena
 Pub Date:
 February 2011
 DOI:
 10.1016/j.physd.2010.10.003
 arXiv:
 arXiv:0809.0916
 Bibcode:
 2011PhyD..240..410T
 Keywords:

 Condensed Matter  Statistical Mechanics;
 Computer Science  Information Theory;
 Mathematics  Probability;
 Statistics  Applications
 EPrint:
 4 pages, 2 figures