The two-dimensional Potts models were simulated with the use of a Monte Carlo method. This study is unique in that we simulated the weighted percolation clusters first described by Kasteleyn and Fortuin in 1969. We describe an auxiliary data structure which enables us to determine the connectedness of large clusters very efficiently. Our simulation of the percolation problem was found not to be affected by a critical slowing down. Critical exponents were found by studying the size and shape of large clusters. Our results agree with previous studies done on integer values of $q$, and with the conjecture of den Nijs.
The two-dimensional Potts models were simulated with the use of a Monte Carlo method. This study is unique in that we simulated the weighted percolation clusters first described by Kasteleyn and Fortuin in 1969. We describe an auxiliary data structure which enables us to determine the connectedness of large clusters very efficiently. Our simulation of the percolation problem was found not to be affected by a critical slowing down. Critical exponents were found by studying the size and shape of large clusters. Our results agree with previous studies done on integer values of q, and with the conjecture of den Nijs.
We propose a site random-cluster model by introducing an additional cluster weight in the partition function of the traditional site percolation. To simulate the model on a square lattice, we combine the color-assignation and the Swendsen-Wang methods to design a highly efficient cluster algorithm with a small critical slowing-down phenomenon. To verify whether or not it is consistent with the bond random-cluster model, we measure several quantities, such as the wrapping probability Re, the percolating cluster density P∞, and the magnetic susceptibility per site χp, as well as two exponents, such as the thermal exponent yt and the fractal dimension yh of the percolating cluster. We find that for different exponents of cluster weight q=1.5, 2, 2.5, 3, 3.5, and 4, the numerical estimation of the exponents yt and yh are consistent with the theoretical values. The universalities of the site random-cluster model and the bond random-cluster model are completely identical. For larger values of q, we find obvious signatures of the first-order percolation transition by the histograms and the hysteresis loops of percolating cluster density and the energy per site. Our results are helpful for the understanding of the percolation of traditional statistical models.
We present a Monte Carlo study of the backbone and the shortest-path exponents of the two-dimensional Q-state Potts model in the Fortuin-Kasteleyn bond representation. We first use cluster algorithms to simulate the critical Potts model on the square lattice and obtain the backbone exponents dB=1.7320(3) and 1.794(2) for Q=2,3, respectively. However, for large Q, the study suffers from serious critical slowing down and slowly converging finite-size corrections. To overcome these difficulties, we consider the O(n) loop model on the honeycomb lattice in the densely packed phase, which is regarded to correspond to the critical Potts model with Q=n2. With a highly efficient cluster algorithm, we determine from domains enclosed by the loops dB=1.64339(5),1.73227(8),1.7938(3),1.8384(5),1.8753(6) for Q=1,2,3,2+3,4, respectively, and dmin=1.0945(2),1.0675(3),1.0475(3),1.0322(4) for Q=2,3,2+3,4, respectively. Our estimates significantly improve over the existing results for both dB and dmin. Finally, by studying finite-size corrections in backbone-related quantities, we conjecture an exact formula as a function of n for the leading correction exponent.
This talk will review the history of both cluster and histogram methods for Monte Carlo simulations. Cluster methods are based on the famous exact mapping by Fortuin and Kasteleyn from general Potts models onto a percolation representation. I will discuss the Swendsen‐Wang algorithm, as well as its improvement and extension to more general spin models by Wolff. The Replica Monte Carlo method further extended cluster simulations to deal with frustrated systems. The history of histograms is quite extensive, and can only be summarized briefly in this talk. It goes back at least to work by Salsburg et al. in 1959. Since then, it has been forgotten and rediscovered several times. The modern use of the method has exploited its ability to efficiently determine the location and height of peaks in various quantities, which is of prime importance in the analysis of critical phenomena. The extensions of this approach to the multiple histogram method and multicanonical ensembles have allowed information to be obtained over a broad range of parameters. Histogram simulations and analyses have become standard techniques in Monte Carlo simulations.
I present a generalization of the Newman-Ziff algorithm, a standard technique in numerical percolation, to the random cluster model. The new method is straightfoward to implement, works for real q>0, and is completely free of the critical slowdown problem. By using the method to sample the critical polynomial, accurate estimates of Potts critical points can be found using relatively small lattice sizes. Furthermore, results for all values of q can be found at once within a single simulation. I demonstrate its utility here by computing critical points for non-integer values of q on the square lattice to compare with the exact solution, and on the non-planar square matching lattice.
The fractal dimensions of the largest clusters of like spins connected by nearest-neighbor bonds in the critical two-dimensional Q-state Potts model have been determined via Monte Carlo simulations to be df=1.946(3), 1.922(5), and 1.909(8) for Q=2, 3, and 4, respectively. These dimensions are close to the magnetic tricritical exponents of the (Q-1)-state dilute Potts model (d-x=187/96=1.947..., 77/40=1.925, and 40/21=1.904... for Q-1=1, 2, and 3, respectively). The critical Q-state model and the tricritical (Q-1)-state model are related to a two-component Potts lattice gas; this model generates percolative susceptibilities of critical Potts clusters, and suggests a renormalization-group argument for df=d-x.