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 |
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Keywords
- Cluster algorithm
- Ising model
- Random spin systems
ASJC Scopus subject areas
- Computer Science Applications
- Physics and Astronomy(all)
Cite this
Application of new Monte Carlo algorithms to random spin systems. / Okabe, Yutaka; Tomita, Yusuke; Yamaguchi, Chiaki.
In: Computer Physics Communications, Vol. 146, No. 1, 15.06.2002, p. 63-68.Research output: Contribution to journal › Article
}
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 -