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Main Authors: Mukhamedzhanova, Sofya, Sabirov, Bulat, Mukhamedzhanov, Amir
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.22579
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author Mukhamedzhanova, Sofya
Sabirov, Bulat
Mukhamedzhanov, Amir
author_facet Mukhamedzhanova, Sofya
Sabirov, Bulat
Mukhamedzhanov, Amir
contents The q-state Potts model is a fundamental framework in statistical physics and graph theory, with its partition function encoding rich information about spin configurations. The multivariate Tutte polynomial (known as the partition function of the Potts model) can be defined on an arbitrary finite graph $G$ and encodes a lot of important combinatorial information about the graph. As a special case, it contains the familiar Tutte polynomial with two variables and, consequently, its specialization with one variable, such as the chromatic polynomial, the flow polynomial and the reliability polynomial. The main goal of this paper is to present an efficient algorithm for computing the Potts model partition function on SP-graphs (series-parallel graphs) with arbitrary weights. The algorithm for SP-graphs is based on simplifying the graph by replacing several edges with a single edge of equivalent weight, which significantly reduces computational complexity. In this paper, we present a linear-time algorithm for exactly computing the Potts model partition function on series-parallel graphs (SP-graphs).
format Preprint
id arxiv_https___arxiv_org_abs_2507_22579
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Algorithm for computing the partition function of the Potts model for SP-graphs
Mukhamedzhanova, Sofya
Sabirov, Bulat
Mukhamedzhanov, Amir
Combinatorics
Mathematical Physics
The q-state Potts model is a fundamental framework in statistical physics and graph theory, with its partition function encoding rich information about spin configurations. The multivariate Tutte polynomial (known as the partition function of the Potts model) can be defined on an arbitrary finite graph $G$ and encodes a lot of important combinatorial information about the graph. As a special case, it contains the familiar Tutte polynomial with two variables and, consequently, its specialization with one variable, such as the chromatic polynomial, the flow polynomial and the reliability polynomial. The main goal of this paper is to present an efficient algorithm for computing the Potts model partition function on SP-graphs (series-parallel graphs) with arbitrary weights. The algorithm for SP-graphs is based on simplifying the graph by replacing several edges with a single edge of equivalent weight, which significantly reduces computational complexity. In this paper, we present a linear-time algorithm for exactly computing the Potts model partition function on series-parallel graphs (SP-graphs).
title Algorithm for computing the partition function of the Potts model for SP-graphs
topic Combinatorics
Mathematical Physics
url https://arxiv.org/abs/2507.22579