Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 63-68 |
| Number of pages | 6 |
| Journal | Computer Physics Communications |
| Volume | 146 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2002 Jun 15 |
Keywords
- Cluster algorithm
- Ising model
- Random spin systems
ASJC Scopus subject areas
- Hardware and Architecture
- Physics and Astronomy(all)
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Okabe, Y., Tomita, Y., & Yamaguchi, C. (2002). Application of new Monte Carlo algorithms to random spin systems. Computer Physics Communications, 146(1), 63-68. https://doi.org/10.1016/S0010-4655(02)00435-6