### 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 |

Externally published | Yes |

### Fingerprint

### Keywords

- Cluster algorithm
- Ising model
- Random spin systems

### ASJC Scopus subject areas

- Computer Science Applications
- Physics and Astronomy(all)

### Cite this

*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.

Research output: Contribution to journal › Article

*Computer Physics Communications*, vol. 146, no. 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 -