Swendsen-Wang Algorithm

The Swendsen-Wang algorithm is a Monte Carlo simulation method introduced by Robert Swendsen and Jian-Sheng Wang in 1987 at Carnegie Mellon University, described as the first non-local, cluster-based algorithm for simulating large systems near a critical point. It was designed for the Ising and Potts models of statistical mechanics, where it addresses critical slowing down, the tendency of standard simulations to need exponentially more computation as a system approaches a phase transition. Rather than flipping individual spins, the algorithm builds on a random-cluster representation of the spin system, identifying clusters of connected spins and flipping entire clusters at once, which sharply reduces the dynamical critical exponent, for example from about 2.125 to about 0.35 in the two-dimensional Ising model, and makes large-scale simulation near criticality practical. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/