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arXiv 1701.08415cond-mat.stat-mechmath-phmath.MP
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计算自回避游动临界指数$γ$的无标度Monte Carlo方法

Scale-free Monte Carlo method for calculating the critical exponent $γ$ of self-avoiding walks

Nathan Clisby

摘要:

我们实现了一个无标度版本的枢轴算法,并使用它来采样的三维自避免行走对,有效地计算一个可观察的,对应的概率,对自避免行走保持自避免时,他们被连接的目的。我们研究了这个马尔可夫链的性质,然后用它来找到临界指数$γ$的自我避免行走前所未有的准确性。我们对$γ$的最终估计是$1.15695300(95)$。

英文摘要:

We implement a scale-free version of the pivot algorithm and use it to sample pairs of three-dimensional self-avoiding walks, for the purpose of efficiently calculating an observable that corresponds to the probability that pairs of self-avoiding walks remain self-avoiding when they are concatenated. We study the properties of this Markov chain, and then use it to find the critical exponent $γ$ for self-avoiding walks to unprecedented accuracy. Our final estimate for $γ$ is $1.15695300(95)$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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