Real Space Renormalization in Statistical Mechanics
Efi Efrati, Zhe Wang, Amy Kolan, Leo P. Kadanoff

TL;DR
This paper compares two real space renormalization methods in statistical mechanics, analyzing their effectiveness on 2D models like the Ising and Potts models, highlighting differences in accuracy and approach.
Contribution
It provides a detailed comparison of the potential-moving and rewiring renormalization methods, especially regarding fixed points and their performance at low complexity levels.
Findings
Potential-moving method yields reasonable critical indices at low complexity.
Rewiring method's error decreases slowly with increasing complexity.
Fixed points are difficult to implement in the rewiring approach.
Abstract
This paper discusses methods for the construction of approximate real space renormalization transformations in statistical mechanics. In particular, it compares two methods of transformation: the "potential-moving" approach most used in the period 1975-1980 and the "rewiring method" as it has been developed in the last five years. These methods both employ a parameter, called \chi, that measures the complexity of the localized stochastic variable forming the basis of the analysis. Both methods are here exemplified by calculations in terms of fixed points for the smallest possible values of \chi. These calculations describe three models for two-dimensional systems: The Ising model solved by Onsager, the tricritical point of that model, and the three-state Potts model. The older method, often described as lower bound renormalization theory, provides a heuristic method giving reasonably…
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