From Rosenbluth Sampling to PERM - rare event sampling with stochastic growth algorithms

TL;DR

This paper reviews the evolution of stochastic growth algorithms, particularly the Rosenbluth method and its enhancements, to achieve efficient uniform sampling of complex polymer configurations in statistical mechanics.

Contribution

It introduces advanced modifications to the Rosenbluth algorithm, resulting in a powerful method for uniform sampling of polymer systems where previous algorithms struggled.

Findings

Enhanced algorithms successfully sample extreme polymer configurations.

Applicable to diverse polymer models including surface interactions and protein folding.

Demonstrates improved performance over existing methods.

Abstract

We discuss uniform sampling algorithms that are based on stochastic growth methods, using sampling of extreme configurations of polymers in simple lattice models as a motivation. We shall show how a series of clever enhancements to a fifty-odd year old algorithm, the Rosenbluth method, led to a cutting-edge algorithm capable of uniform sampling of equilibrium statistical mechanical systems of polymers in situations where competing algorithms failed to perform well. Examples range from collapsed homo-polymers near sticky surfaces to models of protein folding.