Kapfer Krauth 2013
From Werner KRAUTH
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| - | '''S. C. Kapfer and W. Krauth''' | + | S. C. Kapfer and W. Krauth |
| - | '''''Sampling from a polytope and hard-disk Monte Carlo''''' ''' arXiv 1301.4901''' | + | ''Sampling from a polytope and hard-disk Monte Carlo'' J. Phys.: Conf. Ser. 454 012031 (2013) Open Access |
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| [http://arxiv.org/pdf/1301.4901v1 Electronic version (from arXiv, original version)] | [http://arxiv.org/pdf/1301.4901v1 Electronic version (from arXiv, original version)] | ||
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| [http://dx.doi.org/10.1088/1742-6596/454/1/012031 Final version (open access, available to everyone)] | [http://dx.doi.org/10.1088/1742-6596/454/1/012031 Final version (open access, available to everyone)] | ||
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Current revision
S. C. Kapfer and W. Krauth Sampling from a polytope and hard-disk Monte Carlo J. Phys.: Conf. Ser. 454 012031 (2013) Open Access
Contents |
Paper
Abstract The hard-disk problem, the statics and the dynamics of equal two-dimensional hard spheres in a periodic box, has had a profound influence on statistical and computational physics. Markov-chain Monte Carlo and molecular dynamics were first discussed for this model. Here we reformulate hard-disk Monte Carlo algorithms in terms of another classic problem, namely the sampling from a polytope. Local Markov-chain Monte Carlo, as proposed by Metropolis et al. in 1953, appears as a sequence of random walks in high-dimensional polytopes, while the moves of the more powerful event-chain algorithm correspond to molecular dynamics evolution. We determine the convergence properties of Monte Carlo methods in a special invariant polytope associated with hard-disk configurations, and the implications for convergence of hard-disk sampling. Finally, we discuss parallelization strategies for event-chain Monte Carlo and present results for a multicore implementation.
Electronic version (from arXiv, original version)
Final version (open access, available to everyone)

