  The simulation of spin models close to critical points of continuous phase
transitions is heavily impeded by the occurrence of critical slowing down. A
number of cluster algorithms, usually based on the Fortuin-Kasteleyn
representation of the Potts model, and suitable generalizations for
continuous-spin models have been used to increase simulation efficiency. The
first algorithm making use of this representation, suggested by Sweeny in 1983,
has not found widespread adoption due to problems in its implementation.
However, it has been recently shown that it is indeed more efficient in
reducing critical slowing down than the more well-known algorithm due to
Swendsen and Wang. Here, we present an efficient implementation of Sweeny's
approach for the random-cluster model using recent algorithmic advances in
dynamic connectivity algorithms.
