We describe a novel switching algorithm based on a ``reverse'' Monte Carlomethod, in which the potential is stochastically modified before the systemconfiguration is moved. This new algorithm facilitates a generalizedformulation of cluster-type Monte Carlo methods, and the generalization makesit possible to derive cluster algorithms for systems with both discrete andcontinuous degrees of freedom. The roughening transition in the sine-Gordonmodel has been studied with this method, and high-accuracy simulations forsystem sizes up to $1024^2$ were carried out to examine the logarithmicdivergence of the surface roughness above the transition temperature, revealingclear evidence for universal scaling of the Kosterlitz-Thouless type.
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