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

dc.creatorTrebst, Simon
dc.creatorHuse, David A.
dc.creatorTroyer, Matthias
dc.date2004-01-12
dc.date2004-07-08
dc.date.accessioned2026-07-07T06:22:48Z
dc.date.available2026-07-07T06:22:48Z
dc.descriptionWe 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.
dc.description6 pages, 5 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0401195
dc.identifierhttp://arxiv.org/abs/cond-mat/0401195
dc.identifierPhys. Rev. E 70, 046701 (2004).
dc.identifierdoi:10.1103/PhysRevE.70.046701
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/96021
dc.subjectStatistical Mechanics
dc.titleOptimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations
dc.typetext

Files

Collections