Probability-Changing Cluster Algorithm

Study of Three-Dimensional Ising Model and Percolation Problem

Yusuke Tomita, Yutaka Okabe

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

We present a detailed description of the idea and procedure for the newly proposed Monte Carlo algorithm of tuning the critical point automatically, which is called the probability-changing cluster (PCC) algorithm [Y. Tomita and Y. Okabe: Phys. Rev. Lett. 86 (2001) 572]. Using the PCC algorithm, we investigate the three-dimensional Ising model and the bond percolation problem. We employ a refined finite-size scaling analysis to make estimates of critical point and exponents. With much less efforts, we obtain the results which are consistent with the previous calculations. We argue several directions for the application of the PCC algorithm.

Original languageEnglish
Pages (from-to)1570-1575
Number of pages6
JournalJournal of the Physical Society of Japan
Volume71
Issue number6
DOIs
Publication statusPublished - 2002 Jun
Externally publishedYes

Fingerprint

Ising model
critical point
tuning
exponents
scaling
estimates

Keywords

  • Cluster algorithm
  • Finite-size scaling
  • Ising model
  • Monte Carlo simulation
  • Percolation

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Probability-Changing Cluster Algorithm : Study of Three-Dimensional Ising Model and Percolation Problem. / Tomita, Yusuke; Okabe, Yutaka.

In: Journal of the Physical Society of Japan, Vol. 71, No. 6, 06.2002, p. 1570-1575.

Research output: Contribution to journalArticle

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