Convergence of the Wang-Landau algorithm

DOI10.1090/S0025-5718-2015-02952-4zbMath1317.65011arXiv1207.6880MaRDI QIDQ5264128

Benjamin Jourdain, Estelle Kuhn, Gabriel Stoltz, Tony Lelièvre, Gersende Fort

Publication date: 20 July 2015

Published in: Mathematics of Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1207.6880

Mathematics Subject Classification ID

Related Items (13)

Monte Carlo Approximation of Bayes Factors via Mixing With Surrogate Distributions ⋮ Convergence and efficiency of adaptive importance sampling techniques with partial biasing ⋮ An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization ⋮ Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities ⋮ Properties of the stochastic approximation EM algorithm with mini-batch sampling ⋮ Multicanonical MCMC for sampling rare events: an illustrative review ⋮ Convergence of metadynamics: discussion of the adiabatic hypothesis ⋮ Self-healing umbrella sampling: convergence and efficiency ⋮ Partial differential equations and stochastic methods in molecular dynamics ⋮ Central limit theorems for stochastic approximation with controlled Markov chain dynamics ⋮ Safe adaptive importance sampling: a mixture approach ⋮ Wang-Landau algorithm: an adapted random walk to boost convergence ⋮ Stochastic proximal-gradient algorithms for penalized mixed models

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