Self-Healing Umbrella Sampling: Convergence and efficiency
  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. 
G. Fort (LTCI, CNRS and Telecom Paris Tech), B. Jourdain, T. Lelievre   and G. Stoltz (CERMICS, Ecole des Ponts and INRIA Ro...
math.PR cond-mat.stat-mech stat.CO