The statistical mechanics of combinatorial optimization problems with site disorder

TL;DR

This paper applies a novel statistical mechanics approach to analyze combinatorial optimization problems with site disorder, including the traveling salesman problem, providing insights without relying on the replica method.

Contribution

It introduces a formalism for quenched calculations that avoids the replica method and matches Monte Carlo results, extending to higher dimensions and offering a new analytical tool.

Findings

Agreement with Monte Carlo simulations

Reproduction of an exact result for 2D TSP

Potential for generalization to higher dimensions

Abstract

We study the statistical mechanics of a class of problems whose phase space is the set of permutations of an ensemble of quenched random positions. Specific examples analyzed are the finite temperature traveling salesman problem on several different domains and various problems in one dimension such as the so called descent problem. We first motivate our method by analyzing these problems using the annealed approximation, then the limit of a large number of points we develop a formalism to carry out the quenched calculation. This formalism does not require the replica method and its predictions are found to agree with Monte Carlo simulations. In addition our method reproduces an exact mathematical result for the Maximum traveling salesman problem in two dimensions and suggests its generalization to higher dimensions. The general approach may provide an alternative method to study certain systems with quenched disorder.