Application of new Monte Carlo algorithms to random spin systems

Yutaka Okabe, Yusuke Tomita, Chiaki Yamaguchi

Research output: Contribution to journalArticle

24 Citations (Scopus)

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 languageEnglish
Pages (from-to)63-68
Number of pages6
JournalComputer Physics Communications
Volume146
Issue number1
DOIs
Publication statusPublished - 2002 Jun 15
Externally publishedYes

Fingerprint

Ising model
critical point
critical temperature
Spin glass
spin glass
Temperature

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 journalArticle

Okabe, Yutaka ; Tomita, Yusuke ; Yamaguchi, Chiaki. / Application of new Monte Carlo algorithms to random spin systems. In: Computer Physics Communications. 2002 ; Vol. 146, No. 1. pp. 63-68.
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