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Journal Articles Statistics and Computing Year : 2017

Self-Healing Umbrella Sampling: Convergence and efficiency

Abstract

The Self-Healing Umbrella Sampling (SHUS) algorithm is an adaptive biasing algorithm which has been proposed to efficiently sample a multimodal probability measure. We show that this method can be seen as a variant of the well-known Wang-Landau algorithm. Adapting results on the convergence of the Wang-Landau algorithm, we prove the convergence of the SHUS algorithm. We also compare the two methods in terms of efficiency. We finally propose a modification of the SHUS algorithm in order to increase its efficiency, and exhibit some similarities of SHUS with the well-tempered metadynamics method.

Dates and versions

hal-01073201 , version 1 (09-10-2014)

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Gersende Fort, Benjamin Jourdain, Tony Lelièvre, Gabriel Stoltz. Self-Healing Umbrella Sampling: Convergence and efficiency. Statistics and Computing, 2017, 27 (1), pp.147-168. ⟨10.1007/s11222-015-9613-2⟩. ⟨hal-01073201⟩
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