Worm Algorithms for Classical Statistical Models

Abstract

We show that high-temperature expansions provide a basis for the novel approach to efficient Monte Carlo simulations. "Worm" algorithms utilize the idea of updating closed-path configurations (produced by high-temperature expansions) through the motion of end points of a disconnected path. An amazing result is that local, Metropolis-type schemes using this approach appear to have dynamical critical exponents close to zero (i.e., their efficiency is comparable to the best cluster methods) as proved by finite-size scaling of the autocorrelation time for various universality classes.