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arXiv · 1410.2109

Self-Healing Umbrella Sampling: Convergence and efficiency

G. Fort·B. Jourdain·T. Lelievre·G. Stoltz
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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.

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G. Fort, B. Jourdain, T. Lelievre, G. Stoltz. 2014-10-08. Self-Healing Umbrella Sampling: Convergence and efficiency. https://arxiv.org/abs/1410.2109

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