Parallelization of Markov Chain Generation and its Application to the Multicanonical Method
نویسندگان
چکیده
The MCMC (Markov Chain Monte-Carlo) method [1] has played an important role in study of complex systems with many degrees of freedom. For example, MCMC has been applied to various many-body problems such as proteins [2], spin systems [3], and lattice gauge theory [4]. Although the method has achieved great success, there are systems where Monte-Carlo sampling does not work due to local minima of energy functions. For example, in analysis of protein folding, Monte-Carlo random walks are trapped in narrow regions of energy space because there are so many local minima. For large proteins, it is a hard problem to obtain sufficiently large number of statistical samples because it takes very long time for a complicated conformation to escape from a local minimum in energy space.
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