NONLOCAL MONTE-CARLO ALGORITHMS FOR STATISTICAL PHYSICS APPLICATIONS

After a brief general overview of Monte Carlo computer simulations in statistical physics, special emphasis is placed on applications to pha se transitions and critical phenomena. Here, standard simulations empl oying local update algorithms are severely hampered by the problem of critical slowing down, that is by strong correlations between successi vely generated data It is shown that this problem can be greatly reduc ed by using nonlocal update techniques such as cluster and multigrid a lgorithms. The general ideas are illustrated for simple lattice spin m odels and Euclidean path integrals. (C) 1998 IMACS/ Elsevier Science B .V.