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

Simon Trebst, David A. Huse, Matthias Troyer

Research output: Contribution to journalArticlepeer-review

116 Scopus citations


An adaptive algorithm which optimize the statistical-mechanical ensemble in a generalized broad-histogram Monte Carlo simulation to maximize the system's rate of round trips in total energy was presented. It was found that the scaling of the mean round-trip time from the ground state to the maximum entropy state for this local-update method was O([NInN])2) for both the ferromagnetic and the fully frustrated two-dimensional Ising model with N spins. It was shown that the algorithm substantially outperforms flat-histogram methods such as the Wang-Landau algorithm. This algorithm was widely applicable to study the equilibrium behavior of complex system, such as glasses, dense fluids or polymers.

Original languageEnglish (US)
Article number046701
Pages (from-to)046701-1-046701-5
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Issue number4 2
StatePublished - Oct 2004

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability


Dive into the research topics of 'Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations'. Together they form a unique fingerprint.

Cite this