期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 16, 期 4, 页码 2124-2138出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.9b01135
关键词
-
资金
- EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems (EPSRC) [EP/L015854/1]
- EPSRC [EP/R013012/1]
- ERC [757850 BioNet]
We present here two irreversible Markov chain Monte Carlo algorithms for general discrete state systems. One of the algorithms is based on the random-scan Gibbs sampler for discrete states and the other on its improved version, the Metropolized-Gibbs sampler. The algorithms we present incorporate the lifting framework with skewed detailed balance condition and construct irreversible Markov chains that satisfy the balance condition. We have applied our algorithms to 1D 4-state Potts model. The integrated autocorrelation times for magnetization and energy density indicate a reduction of the dynamical scaling exponent from z approximate to 1 to z.c approximate to 1/2. In addition, we have generalized an irreversible Metropolis-Hastings algorithm with skewed detailed balance, initially introduced by Turitsyn et al. [Physica D 2011, 240, 410] for the mean field Ising model, to be now readily applicable to classical spin systems in general; application to 1D 4-state potts model incicate a sequre root reduction of the mixing time at high temperatures.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据