The CrMES scheme as an alternative to importance sampling: The tail regime of the order-parameter distribution

A. Malakis, Nikolaos G. Fytas

Research output: Contribution to journalArticle

Abstract

We review the recently developed critical minimum energy-subspace (CrMES) technique. This scheme produces an immense optimization of popular algorithms, such as the Wang–Landau (WL) and broad histogram methods, by predicting the essential part of the energy space necessary for the estimation of the critical behavior and provides a new route of critical exponent estimation. A powerful and efficient CrMES entropic sampling scheme is proposed as an alternative to the traditional importance sampling methods. Utilizing the WL random walk process in the dominant energy subspace (CrMES-WL sampling) and using the WL approximation of the density of states and appropriate microcanonical estimators we determine the magnetic properties of the 2D Ising model. Updating energy, magnetization (E,M) histograms during the high-level WL-iterations, we provide a comprehensive alternative scheme to the Metropolis algorithm and by applying this procedure we present a convincing analysis for the far tail regime of the order-parameter probability distribution.
Original languageEnglish
Pages (from-to)197–202
JournalPhysica A: Statistical Mechanics and its Applications
Volume365
Issue number1
DOIs
Publication statusPublished - 2 Feb 2006

Fingerprint

Importance Sampling
Order Parameter
Tail
sampling
Subspace
Alternatives
Energy
histograms
Histogram
energy
Metropolis Algorithm
Magnetic Properties
Sampling Methods
Density of States
Critical Behavior
random walk
estimators
Magnetization
Critical Exponents
Ising model

Bibliographical note

The full text is currently unavailable on the repository.

Keywords

  • Wang–Landau sampling
  • Critical minimum energy subspace
  • Tail regime

Cite this

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AB - We review the recently developed critical minimum energy-subspace (CrMES) technique. This scheme produces an immense optimization of popular algorithms, such as the Wang–Landau (WL) and broad histogram methods, by predicting the essential part of the energy space necessary for the estimation of the critical behavior and provides a new route of critical exponent estimation. A powerful and efficient CrMES entropic sampling scheme is proposed as an alternative to the traditional importance sampling methods. Utilizing the WL random walk process in the dominant energy subspace (CrMES-WL sampling) and using the WL approximation of the density of states and appropriate microcanonical estimators we determine the magnetic properties of the 2D Ising model. Updating energy, magnetization (E,M) histograms during the high-level WL-iterations, we provide a comprehensive alternative scheme to the Metropolis algorithm and by applying this procedure we present a convincing analysis for the far tail regime of the order-parameter probability distribution.

KW - Wang–Landau sampling

KW - Critical minimum energy subspace

KW - Tail regime

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