Chanal Krauth 2008

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C. Chanal, W. Krauth ''Renormalization group approach to exact sampling'' Physical Review Letters 100 060601 (2008) C. Chanal, W. Krauth ''Renormalization group approach to exact sampling'' Physical Review Letters 100 060601 (2008)
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'''Abstract:''' In this Letter, we use a general renormalization-group algorithm to '''Abstract:''' In this Letter, we use a general renormalization-group algorithm to
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[http://arxiv.org/pdf/0707.4117v2 Electronic version (from arXiv)] [http://arxiv.org/pdf/0707.4117v2 Electronic version (from arXiv)]
 +[http://link.aps.org/doi/10.1103/PhysRevLett.100.060601 Original paper in Physical Review Letters (requires subscription)]
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 +The subject of this paper was again treated in ...
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 +A Python version of the algorithm developed in this paper was was presented in the [[Chanal_Krauth_2010 |2010 follow-up paper]]
[[Category:Publication]] [[Category:2008]] [[Category:Algorithm]] [[Category:Two dimensions]] [[Category:Publication]] [[Category:2008]] [[Category:Algorithm]] [[Category:Two dimensions]]
[[Category:Perfect sampling]] [[Category:Perfect sampling]]

Revision as of 09:17, 4 January 2012

C. Chanal, W. Krauth Renormalization group approach to exact sampling Physical Review Letters 100 060601 (2008)


Contents

Paper

Abstract: In this Letter, we use a general renormalization-group algorithm to implement Propp and Wilson's "coupling from the past" approach to complex physical systems. Our algorithm follows the evolution of the entire configuration space under the Markov chain Monte Carlo dynamics from parts of the configurations (patches) on increasing length scales, and it allows us to generate "exact samples" of the Boltzmann distribution, which are rigorously proven to be uncorrelated with the initial condition. We validate our approach in the two-dimensional Ising spin glass on lattices of size 64 x 64.

Electronic version (from arXiv) Original paper in Physical Review Letters (requires subscription)

Context

The subject of this paper was again treated in ...

Algorithm

A Python version of the algorithm developed in this paper was was presented in the 2010 follow-up paper

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