Abstract
We study the performance of Monte Carlo simulations that samples a broad histogram in energy by determining the mean first passage time to span the entire energy space of d-dimensional Ising-Potts models. For the d = 1, 2,3 Ising model, the mean first passage time τ of flat-histogram Monte Carlo methods with single-spin flip updates, such as the Wang-Landau algorithm or the multicanonical method, scales with the number of spins N = L d as τ ∼ N 2 L z. The exponent z is found to be decrease as the dimensionality d is increased. In the mean field limit of infinite dimensions we find that z vanishes up to a logarithmic correction. We then demonstrate how the flat-histogram algorithms can be improved by two complementary approaches — cluster dynamics and ensemble optimization technique. Both approaches are found to improve the random walk in energy space so that τ ∼ N 2 up to logarithmic corrections for the d = 1, 2 Ising model.
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Wu, Y. et al. (2007). Performance and Improvements of Flat-histogram Monte Carlo Simulations. In: Landau, D.P., Lewis, S.P., Schüttler, HB. (eds) Computer Simulation Studies in Condensed-Matter Physics XVIII. Springer Proceedings in Physics, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32640-3_20
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DOI: https://doi.org/10.1007/978-3-540-32640-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32639-7
Online ISBN: 978-3-540-32640-3
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