Convergence and efficiency of adaptive importance sampling techniques with partial biasing

TL;DR

This paper introduces a generalized adaptive importance sampling method that improves sampling efficiency in multimodal distributions by partial biasing and enhanced set penalization, with proven convergence and demonstrated benefits.

Contribution

It extends Self Healing Umbrella Sampling with a new updating strategy and partial biasing, improving escape from metastable states and reducing estimator variance.

Findings

The algorithm converges under specified conditions.

Partial biasing increases sampling efficiency.

Numerical tests show improved performance over existing methods.

Abstract

We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which is an adaptive importance technique useful to sample multimodal target distributions. The importance function is based on the weights (namely the relative probabilities) of disjoint sets which form a partition of the space. These weights are unknown but are learnt on the fly yielding an adaptive algorithm. In the context of computational statistical physics, the logarithm of these weights is, up to a multiplicative constant, the free energy, and the discrete valued function defining the partition is called the collective variable. The algorithm falls into the general class of Wang-Landau type methods, and is a generalization of the original Self Healing Umbrella Sampling method in two ways: (i) the updating strategy leads to a larger penalization strength of already visited sets in order to escape more quickly from metastable states, and (ii) the target distribution is biased using only a fraction of the free energy, in order to increase the effective sample size and reduce the variance of importance sampling estimators. The algorithm can also be seen as a generalization of well-tempered metadynamics. We prove the convergence of the algorithm and analyze numerically its efficiency on a toy example.