Nonreversible Markov Chain Monte Carlo Algorithm for Efficient Generation of Self-Avoiding Walks
Nonreversible Markov Chain Monte Carlo Algorithm for Efficient Generation of Self-Avoiding Walks
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DOI：
10.3389/fphy.2021.782156
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发表时间：
2021-07
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影响因子：
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通讯作者：
Hanqing Zhao;M. Vucelja
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文献类型：
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作者：
Hanqing Zhao;M. Vucelja
我们引入了一种有效的不可逆马尔可夫链蒙特卡罗算法来生成具有可变端点的自回避游走。在二维中，新算法稍微优于 H.Hu、X.Chen 和 Y.Deng 提出的两步不可逆 Berretti-Sokal 算法，而对于三维行走，它的速度快了 3-5 倍。新算法引入了不可逆马尔可夫链，该链服从全局平衡，并允许在现有的自回避步行上进行三种类型的基本移动：缩短、延伸或改变构象而不改变步行的长度。
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.