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29 records were found.
Comment: 17 pages, 12 figures, 1 table
Comment: 5 pages, 4 figures, elsarticle
The importance-sampling Monte Carlo algorithm appears to be the universally
optimal solution to the problem of sampling the state space of statistical
mechanical systems according to the relative importance of configurations for
the partition function or thermal averages of interest. While this is true in
terms of its simplicity and universal applicability, the resulting approach
suffers from the presence of temporal correlations of successive samples
naturally implied by the Markov chain underlying the importance-sampling
simulation. In many situations, these autocorrelations are moderate and can be
easily accounted for by an appropriately adapted analysis of simulation data.
They turn out to be a major hurdle, however, in the vicinity of phase
transitions or for systems with complex free-energy landscapes. The critical
slowing down...
Comment: 28 pages, 15 figures, 2 tables, version as published
Comment: 15 pages, 14 figures, one table, submitted to PRE
Comment: 4 pages, RevTEX4, 3 tables, 1 figure
Comment: 16 pages, RevTEX4, 4 figures, 6 tables, published version
Comment: to appear in Eur. Phys. J. Spec. Top. issue "Computer simulations on
GPU"
Using an elaborate set of simulational tools and statistically optimized
methods of data analysis we investigate the scaling behavior of the correlation
lengths of three-dimensional classical O($n$) spin models. Considering
three-dimensional slabs $S^1\times S^1\times\mathbb{R}$, the results over a
wide range of $n$ indicate the validity of special scaling relations involving
universal amplitude ratios that are analogous to results of conformal field
theory for two-dimensional systems. A striking mismatch of the $n\to\infty$
extrapolation of these simulations against analytical calculations is traced
back to a breakdown of the identification of this limit with the spherical
model.
Comment: 6 pages, 2 figures, Europhys. Lett., in print
