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Wang-Landau Algorithm: an adapted random walk to boost convergence

Augustin Chevallier 1 Frédéric Cazals 1
1 ABS - Algorithms, Biology, Structure
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The Wang-Landau (WL) algorithm is a recently developed stochastic algorithm computing densities of states of a physical system. Since its inception, it has been used on a variety of (bio-)physical systems, and in selected cases, its convergence has been proved. The convergence speed of the algorithm is tightly tied to the connectivity properties of the underlying random walk. As such, we propose an efficient random walk that uses geometrical information to circumvent the following inherent difficulties: avoiding overstepping strata, toning down concentration phenomena in high-dimensional spaces, and accommodating multidimensional distribution. Experiments on various models stress the importance of these improvements to make WL effective in challenging cases. Altogether, these improvements make it possible to compute density of states for regions of the phase space of small biomolecules.
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Submitted on : Thursday, February 11, 2021 - 2:34:26 PM
Last modification on : Saturday, February 13, 2021 - 3:29:26 AM

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Augustin Chevallier, Frédéric Cazals. Wang-Landau Algorithm: an adapted random walk to boost convergence. Journal of Computational Physics, Elsevier, 2020, 410, pp.109366. ⟨10.1016/j.jcp.2020.109366⟩. ⟨hal-01919860v4⟩

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