Probability-Changing Cluster Algorithm: Study of Three-Dimensional Ising Model and Percolation Problem

Yusuke Tomita; Yutaka Okabe

doi:10.1143/JPSJ.71.1570

Probability-Changing Cluster Algorithm: Study of Three-Dimensional Ising Model and Percolation Problem

Yusuke Tomita, Yutaka Okabe

Research output: Contribution to journal › Article › peer-review

4 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 language	English
Pages (from-to)	1570-1575
Number of pages	6
Journal	journal of the physical society of japan
Volume	71
Issue number	6
DOIs	https://doi.org/10.1143/JPSJ.71.1570
Publication status	Published - 2002 Jun
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

Physics and Astronomy(all)

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10.1143/JPSJ.71.1570

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title = "Probability-Changing Cluster Algorithm: Study of Three-Dimensional Ising Model and Percolation Problem",

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.",

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author = "Yusuke Tomita and Yutaka Okabe",

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AU - Tomita, Yusuke

AU - Okabe, Yutaka

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N2 - 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.

AB - 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.

KW - Cluster algorithm

KW - Finite-size scaling

KW - Ising model

KW - Monte Carlo simulation

KW - Percolation

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Probability-Changing Cluster Algorithm: Study of Three-Dimensional Ising Model and Percolation Problem

Abstract

Keywords

ASJC Scopus subject areas

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