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

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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

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Statistical Mechanics

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http://salesiana.dossiersoluciones.com/handle/123456789/96021

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cond-mat/0401195
 http://arxiv.org/abs/cond-mat/0401195
 Phys. Rev. E 70, 046701 (2004).
 doi:10.1103/PhysRevE.70.046701

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arXiv