Reverse monte carlo method and its implications for generalized cluster algorithms.
We describe a novel switching algorithm based on a "reverse" Monte Carlo method, in which the potential is stochastically modified before the system configuration is moved. This new algorithm facilitates a generalized formulation of cluster-type Monte Carlo methods, and the generalization makes it possible to derive cluster algorithms for systems with both discrete and continuous degrees of freedom. The roughening transition in the sine-Gordon model has been studied with this method, and high-accuracy simulations for system sizes up to 1024(2) were carried out to examine the logarithmic divergence of the surface roughness above the transition temperature, revealing clear evidence for universal scaling of the Kosterlitz-Thouless type.
Mak
C H
CH
Department of Chemistry, University of Southern California, Los Angeles, California 90089-0482, USA.
Sharma
Arun K
AK