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Efficiency of the Wang-Landau Algorithm: A Simple Test Case

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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Contributor : Tony Lelièvre Connect in order to contact the contributor
Submitted on : Tuesday, July 31, 2012 - 8:19:43 AM
Last modification on : Thursday, January 20, 2022 - 5:29:31 PM

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Gersende Fort, Benjamin Jourdain, Estelle Kuhn, Tony Lelièvre, Gabriel Stoltz. Efficiency of the Wang-Landau Algorithm: A Simple Test Case. Applied Mathematics Research eXpress, Oxford University Press (OUP): Policy H - Oxford Open Option A, 2014, 2014 (2), pp.275-311. ⟨10.1093/amrx/abu003⟩. ⟨hal-00721886⟩



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