Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations
统计力学
2007-05-23 v3
摘要
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.
引用
@article{arxiv.cond-mat/0401195,
title = {Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations},
author = {Simon Trebst and David A. Huse and Matthias Troyer},
journal= {arXiv preprint arXiv:cond-mat/0401195},
year = {2007}
}
备注
6 pages, 5 figures