title: Optimal modification factor and convergence of the Wang-Landau algorithm.
abstract: We propose a strategy to achieve the fastest convergence in the Wang-Landau algorithm with varying modification factors. With this strategy, the convergence of a simulation is at least as good as the conventional Monte Carlo algorithm, i.e., the statistical error vanishes as 1/sqrt t, where t is a normalized time of the simulation. However, we also prove that the error cannot vanish faster than 1/t . Our findings are consistent with the 1/t Wang-Landau algorithm discovered recently, and we argue that one needs external information in the simulation to beat the conventional Monte Carlo algorithm.
authors.affiliation: Quantitative Research, J. P. Morgan Chase and Company, 12th Floor, 277 Park Ave., New York, New York 10017, USA. chenggang.x.zhou@jpmorgan.com
authors.forename: Chenggang
authors.initials: C
authors.lastname: Zhou
authors.forename: Jia
authors.initials: J
authors.lastname: Su