Speed-up of Monte Carlo simulations by sampling of rejected states

AUTOR(ES)

Frenkel, Daan

FONTE

National Academy of Sciences

RESUMO

The Markov chain Monte Carlo method is an important tool to estimate the average properties of systems with a very large number of accessible states. This technique is used extensively in fields ranging from physics to genetics and economics. The rejection of trial configurations is a central ingredient in existing Markov chain Monte Carlo simulations. I argue that the efficiency of Monte Carlo simulations can be enhanced, sometimes dramatically, by properly sampling configurations that are normally rejected. This “waste-recycling” of microstates is useful in sampling schemes in which only one of a large set of trial configurations is accepted. It differs fundamentally from schemes that extract information about the density of macrostates from virtual Monte Carlo moves. As a simple illustration, I show that the method greatly improves the calculation of the order-parameter distribution of a two-dimensional Ising model. This method should enhance the efficiency of parallel Monte Carlo simulations significantly.

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