Swendsen–Wang algorithm: Difference between revisions

Revision as of 19:51, 3 June 2012

Revision as of 15:47, 17 September 2013

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{{Notability|date=March 2009}}

The '''Swendsen–Wang algorithm''' is an [[algorithm]] for [[Monte Carlo simulation]] of the [[Ising model]] in which the entire sample is divided into equal-spin clusters. Each cluster is then assigned a new random spin value. Compare the [[Wolff algorithm]].

It has been generalized by Barbu and Zhu (2005) to sampling arbitrary probabilities by viewing it as a [[Metropolis–Hastings algorithm]] and computing the acceptance probability of the proposed Monte Carlo move.

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*[http://www-fcs.acs.i.kyoto-u.ac.jp/~harada/monte-en.html]

{{DEFAULTSORT:Swendsen-Wang algorithm}}

[[Category:Monte Carlo methods]]

[[Category:Statistical mechanics]]