Application of new Monte Carlo algorithms to random spin systems

被引：25

作者：

Okabe, Y ^{[1
]}

Tomita, Y ^{[1
]}

Yamaguchi, C ^{[1
]}

机构：

[1] Tokyo Metropolitan Univ, Dept Phys, Hachioji, Tokyo 1920397, Japan

来源：

COMPUTER PHYSICS COMMUNICATIONS | 2002年 / 146卷 / 01期

关键词：

cluster algorithm; Ising model; random spin systems;

D O I：

10.1016/S0010-4655(02)00435-6

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We explain the idea of the probability-changing cluster (PCC) algorithm, which is an extended version of the Swendsen-Wang algorithm. With this algorithm, we can tune the critical point automatically. We show the effectiveness of the PCC algorithm for the case of the three-dimensional (3D) Ising model. We also apply this new algorithm to the study of the 3D diluted Ising model. Since we tune the critical point of each random sample automatically with the PCC algorithm, we can investigate the sample-dependent critical temperature and the sample average of physical quantities at each critical temperature, systematically. We have also applied another newly proposed algorithm, the Wang-Landau algorithm, to the study of the spin glass problem. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：63 / 68

页数：6

共 27 条

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