### 抄録

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.

元の言語 | English |
---|---|

ページ（範囲） | 63-68 |

ページ数 | 6 |

ジャーナル | Computer Physics Communications |

巻 | 146 |

発行部数 | 1 |

DOI | |

出版物ステータス | Published - 2002 6 15 |

外部発表 | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science Applications
- Physics and Astronomy(all)

### これを引用

*Computer Physics Communications*,

*146*(1), 63-68. https://doi.org/10.1016/S0010-4655(02)00435-6

**Application of new Monte Carlo algorithms to random spin systems.** / Okabe, Yutaka; Tomita, Yusuke; Yamaguchi, Chiaki.

研究成果: Article

*Computer Physics Communications*, 巻. 146, 番号 1, pp. 63-68. https://doi.org/10.1016/S0010-4655(02)00435-6

}

TY - JOUR

T1 - Application of new Monte Carlo algorithms to random spin systems

AU - Okabe, Yutaka

AU - Tomita, Yusuke

AU - Yamaguchi, Chiaki

PY - 2002/6/15

Y1 - 2002/6/15

N2 - 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.

AB - 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.

KW - Cluster algorithm

KW - Ising model

KW - Random spin systems

UR - http://www.scopus.com/inward/record.url?scp=0037097444&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037097444&partnerID=8YFLogxK

U2 - 10.1016/S0010-4655(02)00435-6

DO - 10.1016/S0010-4655(02)00435-6

M3 - Article

AN - SCOPUS:0037097444

VL - 146

SP - 63

EP - 68

JO - Computer Physics Communications

JF - Computer Physics Communications

SN - 0010-4655

IS - 1

ER -