Nonreversible Markov Chain Monte Carlo Algorithm for Efficient Generation of Self-Avoiding Walks
Keyword(s):
We introduce an efficient nonreversible Markov chain Monte Carlo algorithm to generate self-avoiding walks with a variable endpoint. In two dimensions, the new algorithm slightly outperforms the two-move nonreversible Berretti-Sokal algorithm introduced by H. Hu, X. Chen, and Y. Deng, while for three-dimensional walks, it is 3–5 times faster. The new algorithm introduces nonreversible Markov chains that obey global balance and allow for three types of elementary moves on the existing self-avoiding walk: shorten, extend or alter conformation without changing the length of the walk.
2018 ◽
Vol 12
(12)
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pp. 1535-1542
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Keyword(s):
1998 ◽
Vol 93
(443)
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pp. 1055-1067
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Keyword(s):
2016 ◽
Vol 9
(9)
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pp. 3213-3229
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Keyword(s):
2008 ◽
pp. 1156-1156