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

نویسندگان

  • Simon Trebst
  • David A Huse
  • Matthias Troyer
چکیده

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 ln N](2) ) for both the ferromagnetic and the fully frustrated two-dimensional Ising model with N spins. Our algorithm thereby substantially outperforms flat-histogram methods such as the Wang-Landau algorithm.

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عنوان ژورنال:
  • Physical review. E, Statistical, nonlinear, and soft matter physics

دوره 70 4 Pt 2  شماره 

صفحات  -

تاریخ انتشار 2004