期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 101, 期 51, 页码 17571-17575出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0407950101
关键词
algorithms; statistical physics
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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