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arXiv 1602.01671cond-mat.stat-mechphysics.comp-ph
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自回避行走的不可逆马尔可夫链蒙特卡罗算法

Irreversible Markov chain Monte Carlo algorithm for self-avoiding walk

Hao Hu, Xiaosong Chen, Youjin Deng

摘要:

针对违反细致平衡条件且满足平衡条件的自回避行走问题,提出了一种不可逆马尔可夫链蒙特卡罗算法。它的性能显着提高相比,Berretti-Sokal算法,这是一个变种的大都会黑斯廷斯方法。所获得的效率随着空间维度(D)而增加,从2D中的大约$10$倍增加到5D中的大约$40$倍。我们模拟了SAW的5D超立方晶格周期性的边界条件下,一个系统的线性尺寸高达$L=128$,并确认,作为5D伊辛模型,SAW的有限尺寸标度是由重整化指数$ν^*=2/d$和$γ/ν^*=d/2$。确定临界点,其比最佳可用估计值精确约$8$倍。

英文摘要:

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 the spatial dimension (D), from approximately $10$ times in 2D to approximately $40$ times in 5D. We simulate the SAW on a 5D hypercubic lattice with periodic boundary conditions, for a system with a linear 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 $ν^*=2/d$ and $γ/ν^*=d/2$. The critical point is determined, which is approximately $8$ times more precise than the best available estimate.

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