Efficiency of the Wang-Landau algorithm: a simple test case
type: Item
instance of: scholarly article
zbMATH Open document ID: 1317.65010
author: Benjamin Jourdain
author: Estelle Kuhn
author: Tony Lelièvre
author: Gabriel Stoltz
author: Gersende Fort
publication date: 2014-11-05T00:00:00Z
full work available at URL: https://arxiv.org/abs/1310.6550
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 62P35
Mathematics Subject Classification ID: 65C40
Mathematics Subject Classification ID: 60J22
Mathematics Subject Classification ID: 82B80
zbMATH DE Number: 6365876
zbMATH Keywords: Markov chain Monte Carlo
zbMATH Keywords: adaptive importance sampling
zbMATH Keywords: Metropolis-Hastings algorithm
zbMATH Keywords: Wang-Landau algorithm
zbMATH Keywords: numerical result
zbMATH Keywords: computational statistical physics
MaRDI profile type: Publication
OpenAlex ID: W2095714768
arXiv ID: 1310.6550
DOI: 10.1093/AMRX/ABU003
title: Efficiency of the Wang-Landau algorithm: a simple test case
published in: AMRX. Applied Mathematics Research eXpress
review text: The Wang-Landau algorithm is an adaptive importance Markov chain Monte Carlo (MCMC) technique based on a single trajectory interacting with its own past. The authors analyze the efficiency of this algorithm in escaping metastable states. Analytic results are presented for a toy model with only three states (two metastable states and one intermediate state which is visited with a low probability). Numerical results are discussed for a 2D-model from computational statistical physics. It is shown that in these examples the exit times from metastable states are much smaller for the Wang-Landau algorithm than for the standard Metropolis-Hastings algorithm.
reviewed by: R. E. Maiboroda
description: scientific article; zbMATH DE number 6365876