Rugged Metropolis Sampling with Simultaneous Updating of Two Dynamical Variables

Abstract

The Rugged Metropolis (RM) algorithm is a biased updating scheme, which aims at directly hitting the most likely configurations in a rugged free energy landscape. Details of the one-variable (RM$_1$) implementation of this algorithm are presented. This is followed by an extension to simultaneous updating of two dynamical variables (RM$_2$). In a test with Met-Enkephalin in vacuum RM$_2$ improves conventional Metropolis simulations by a factor of about four. Correlations between three or more dihedral angles appear to prevent larger improvements at low temperatures. We also investigate a multi-hit Metropolis scheme, which spends more CPU time on variables with large autocorrelation times.