Swendsen–Wang algorithm: Difference between revisions

Revision as of 20:54, 18 December 2016

Revision as of 21:03, 18 December 2016

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The '''Swendsen–Wang algorithm''' is the first non-local [[algorithm]] for [[Monte Carlo simulation]] for large systems near criticality.

The original algorithm was designed for the Ising and Potts models, and later it was generalized to other systems as well, such as the XY model by [[Wolff algorithm]] and particles of fluids. A key ingredient of the method is based on the representation of the Ising or [[Potts model|Potts]] model through percolation models of connecting bonds due to Fortuin and Kasteleyn.

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.