Binary tree summation Monte Carlo method for Potts models
Jian-Sheng Wang, Oner Kozan, Robert H. Swendsen
Abstract
We give a new sampling algorithm for the Potts model based on the Fortuin-Kasteleyn transformation. The method produces independent samples and sums up a large number of configurations for each sweep. The partition function and thermodynamic averages for all values of the temperature can be computed from a single run. We compare the results with exact 2D Ising model.
Create a lesson
Related papers
Droplets obstruct Onsager-type solutions of the Ising model in a magnetic field
Ran Luo, Jiahua Tian
Physical limits to stochastic classifiers
Timur Aslyamov, Massimiliano Esposito
Absolute area under a scaled Brownian motion with resetting
Vicenç Méndez, Javier Cristín
Understanding the Mpemba Effect through a Rule of Thumb
Ran Yaacoby
Beyond the lemon: Spectra of random Lindblad operators with a Hamiltonian component
Mykhailo Hontarenko, Wojciech Tarnowski, Sergey Denisov et al.
Towards synergy between theory and experiments: First-passage processes under resetting with stochastic return protocols
Ashutosh Dubey, Arnab Pal