Self-Healing Umbrella Sampling: Convergence and efficiency
Probability
2014-10-09 v1 Statistical Mechanics
Computation
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.
Keywords
Cite
@article{arxiv.1410.2109,
title = {Self-Healing Umbrella Sampling: Convergence and efficiency},
author = {G. Fort and B. Jourdain and T. Lelievre and G. Stoltz},
journal= {arXiv preprint arXiv:1410.2109},
year = {2014}
}