Abstract
Inspired by the multicanonical approach to simulations of first-order phase transitions we propose for q-state Potts models a combination of cluster updates with reweighting of the bond configurations in the Fortuin-Kastelein-Swendsen-Wang representation of this model. Numerical tests for the two-dimensional models with q=7, 10 and 20 show that the autocorrelation times of this algorithm grow with the system size V as {tau}{proportional_to}V{sup {alpha}}, where the exponent takes the optimal random walk value of {alpha}{approx}1. ((orig.)).
Citation Formats
Janke, W, and Kappler, S.
Multibondic cluster algorithm.
Netherlands: N. p.,
1995.
Web.
doi:10.1016/0920-5632(95)00408-2.
Janke, W, & Kappler, S.
Multibondic cluster algorithm.
Netherlands.
https://doi.org/10.1016/0920-5632(95)00408-2
Janke, W, and Kappler, S.
1995.
"Multibondic cluster algorithm."
Netherlands.
https://doi.org/10.1016/0920-5632(95)00408-2.
@misc{etde_101028,
title = {Multibondic cluster algorithm}
author = {Janke, W, and Kappler, S}
abstractNote = {Inspired by the multicanonical approach to simulations of first-order phase transitions we propose for q-state Potts models a combination of cluster updates with reweighting of the bond configurations in the Fortuin-Kastelein-Swendsen-Wang representation of this model. Numerical tests for the two-dimensional models with q=7, 10 and 20 show that the autocorrelation times of this algorithm grow with the system size V as {tau}{proportional_to}V{sup {alpha}}, where the exponent takes the optimal random walk value of {alpha}{approx}1. ((orig.)).}
doi = {10.1016/0920-5632(95)00408-2}
journal = []
volume = {42}
journal type = {AC}
place = {Netherlands}
year = {1995}
month = {Apr}
}
title = {Multibondic cluster algorithm}
author = {Janke, W, and Kappler, S}
abstractNote = {Inspired by the multicanonical approach to simulations of first-order phase transitions we propose for q-state Potts models a combination of cluster updates with reweighting of the bond configurations in the Fortuin-Kastelein-Swendsen-Wang representation of this model. Numerical tests for the two-dimensional models with q=7, 10 and 20 show that the autocorrelation times of this algorithm grow with the system size V as {tau}{proportional_to}V{sup {alpha}}, where the exponent takes the optimal random walk value of {alpha}{approx}1. ((orig.)).}
doi = {10.1016/0920-5632(95)00408-2}
journal = []
volume = {42}
journal type = {AC}
place = {Netherlands}
year = {1995}
month = {Apr}
}