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Convergence of the Wang-Landau algorithm

Authors: Gersende Fort, Benjamin Jourdain, Estelle Kuhn, Tony Lelièvre and Gabriel Stoltz
Journal: Math. Comp. 84 (2015), 2297-2327
MSC (2010): Primary 65C05, 60J05, 82C80
Published electronically: March 23, 2015
MathSciNet review: 3356027
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Abstract: We analyze the convergence properties of the Wang-Landau algorithm. This sampling method belongs to the general class of adaptive importance sampling strategies which use the free energy along a chosen reaction coordinate as a bias. Such algorithms are very helpful to enhance the sampling properties of Markov Chain Monte Carlo algorithms, when the dynamics is metastable. We prove the convergence of the Wang-Landau algorithm and an associated central limit theorem.

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Additional Information

Gersende Fort
Affiliation: LTCI, CNRS & Telecom ParisTech, 46 rue Barrault, 75634 Paris Cedex 13, France

Benjamin Jourdain
Affiliation: Université Paris-Est, CERMICS (ENPC), INRIA, 6-8 Avenue Blaise Pascal, F-77455 Marne-la-Vallée

Estelle Kuhn
Affiliation: INRA Unité MIA, Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France

Tony Lelièvre
Affiliation: Université Paris-Est, CERMICS (ENPC), INRIA, 6-8 Avenue Blaise Pascal, F-77455 Marne-la-Vallée

Gabriel Stoltz
Affiliation: Université Paris-Est, CERMICS (ENPC), INRIA, 6-8 Avenue Blaise Pascal, F-77455 Marne-la-Vallée

Received by editor(s): July 30, 2012
Received by editor(s) in revised form: September 26, 2013
Published electronically: March 23, 2015
Additional Notes: This work was supported by the French National Research Agency under the grants ANR-09-BLAN-0216-01 (MEGAS) and ANR-08-BLAN-0218 (BigMC)
Article copyright: © Copyright 2015 American Mathematical Society

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