Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations

Abstract

We present an adaptive algorithm which optimizes the statistical-mechanical ensemble in a generalized broad-histogram Monte Carlo simulation to maximize the system's rate of round trips in total energy. The scaling of the mean round-trip time from the ground state to the maximum entropy state for this local-update method is found to be O([N log N]^2) for both the ferromagnetic and the fully frustrated 2D Ising model with N spins. Our new algorithm thereby substantially outperforms flat-histogram methods such as the Wang-Landau algorithm.

6 pages, 5 figures

Cited by in corpus (4)

• Entropic sampling via Wang-Landau random walks in dominant energy subspaces
• Optimized ensemble Monte Carlo simulations of dense Lennard-Jones fluids
• Overcoming the critical slowing down of flat-histogram Monte Carlo simulations: Cluster updates and optimized broad-histogram ensembles
• Analytical study of tunneling times in flat histogram Monte Carlo