Irreversible Markov chain Monte Carlo algorithm for self-avoiding walk

被引：11

作者：

Hu, Hao ^{[1
,2
,3
,4
]}

Chen, Xiaosong ^{[3
]}

Deng, Youjin ^{[1
,2
,3
]}

机构：

[1] Univ Sci & Technol China, Natl Lab Phys Sci Microscale, Hefei 230026, Peoples R China

[2] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China

[3] Chinese Acad Sci, Inst Theoret Phys, State Key Lab Theoret Phys, Beijing 100190, Peoples R China

[4] Nanyang Technol Univ, Sch Chem & Biomed Engn, Singapore 637459, Singapore

来源：

FRONTIERS OF PHYSICS | 2017年 / 12卷 / 01期

基金：

中央高校基本科研业务费专项资金资助; 中国国家自然科学基金;

关键词：

Monte Carlo algorithms; self-avoiding walk; irreversible; balance condition; SIMULATIONS; POLYMERS;

D O I：

10.1007/s11467-016-0646-6

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We formulate an irreversible Markov chain Monte Carlo algorithm for the self-avoiding walk (SAW), which violates the detailed balance condition and satisfies the balance condition. Its performance improves significantly compared to that of the Berretti-Sokal algorithm, which is a variant of the Metropolis-Hastings method. The gained efficiency increases with spatial dimension (D), from approximately 1 0 times in 2D to approximately 4 0 times in 5D. We simulate the SAW on a 5D hyper-cubic lattice with periodic boundary conditions, for a linear system with a size up to L = 128, and confirm that as for the 5D Ising model, the finite-size scaling of the SAW is governed by renormalized exponents, nu* = 2/d and gamma/nu* = d/2. The critical point is determined, which is approximately 8 times more precise than the best available estimate.

引用

页数：8

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