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

Simon Trebst, David A. Huse, Matthias Troyer

Research output: Contribution to journalArticlepeer-review

115 Scopus citations


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 [Formula presented] for both the ferromagnetic and the fully frustrated two-dimensional Ising model with [Formula presented] spins. Our algorithm thereby substantially outperforms flat-histogram methods such as the Wang-Landau algorithm.

Original languageEnglish (US)
Pages (from-to)5
Number of pages1
JournalPhysical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Issue number4
StatePublished - 2004

All Science Journal Classification (ASJC) codes

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


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