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arXiv 1812.02494cond-mat.stat-mech
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带因子场的事件链蒙特卡罗方法

Event-chain Monte Carlo with factor fields

Ze Lei, Werner Krauth, A. C. Maggs

摘要:

本文研究了用事件链蒙特卡罗算法(ECMC)模拟的一维相互作用粒子的动力学。我们认为,以前版本的算法遭受不匹配的因素之间的潜在的不同粒子对(因素),并表明,在1D模型中,这种不匹配是克服因素字段。ECMC与因子字段的动机,在1D中,谐波模型,并验证了Lennard-Jones模型以及硬球。在具有短程相互作用的一维粒子系统中,自相关时间通常与可逆蒙特卡罗动力学的系统尺寸的二次幂成比例,而与常规ECMC和分子动力学的一次幂成比例。我们表明,使用数值模拟,它们只增长与ECMC因子字段的系统大小的平方根。混合时间,约束的时间,达到平衡,从一个任意的初始配置,增长与系统大小的一次幂。

英文摘要:

We study the dynamics of one-dimensional (1D) interacting particles simulated with the event-chain Monte Carlo algorithm (ECMC). We argue that previous versions of the algorithm suffer from a mismatch in the factor potential between different particle pairs (factors) and show that in 1D models, this mismatch is overcome by factor fields. ECMC with factor fields is motivated, in 1D, for the harmonic model, and validated for the Lennard-Jones model as well as for hard spheres. In 1D particle systems with short-range interactions, autocorrelation times generally scale with the second power of the system size for reversible Monte Carlo dynamics, and with its first power for regular ECMC and for molecular-dynamics. We show, using numerical simulations, that they grow only with the square root of the systems size for ECMC with factor fields. Mixing times, which bound the time to reach equilibrium from an arbitrary initial configuration, grow with the first power of the system size.

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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