SINGLE-CLUSTER MONTE-CARLO DYNAMICS FOR THE ISING-MODEL

We present an extensive study of a new Monte Carlo acceleration algorithm introduced by Wolff for the Ising model. It differs from the Swendsen-Wang algorithm by growing and flipping single clusters at a random seed. In general, it is more efficient than Swendsen-Wang dynamics for d>2, giving zero critical slowing down in the upper critical dimension. Monte Carlo simulations give dynamical critical exponents zw=0.33±0.05 and 0.44+0.10 in d=2 and 3, respectively, and numbers consistent with zw=0 in d=4 and mean-field theory. We present scaling arguments which indicate that the Wolff mechanism for decorrelation differs substantially from Swendsen-Wang despite the apparent similarities of the two methods. © 1990 Plenum Publishing Corporation.