Phase Transitions of Traveling Salesperson Problems solved with Linear Programming and Cutting Planes

TL;DR

This paper investigates phase transitions in the Euclidean Traveling Salesperson Problem using linear programming and cutting planes, revealing how problem hardness varies with a parameter and applying concepts from physics to characterize these transitions.

Contribution

It introduces a numerical study of phase transitions in TSP instances using LP and cutting planes, connecting computational hardness with physical phase transition concepts.

Findings

Multiple phase transitions observed as problem parameters vary

Scaling assumptions from physics applied to characterize transitions

LP and cutting plane methods reveal easy-hard problem regimes

Abstract

The Traveling Salesperson problem asks for the shortest cyclic tour visiting a set of cities given their pairwise distances and belongs to the NP-hard complexity class, which means that with all known algorithms in the worst case instances are not solveable in polynomial time, i.e., the problem is hard. Though that does not mean, that there are not subsets of the problem which are easy to solve. To examine numerically transitions from an easy to a hard phase, a random ensemble of cities in the Euclidean plane given a parameter {\sigma}, which governs the hardness, is introduced. Here, a linear programming approach together with suitable cutting planes is applied. Such algorithms operate outside the space of feasible solutions and are often used in practical application but rarely studied in physics so far. We observe several transitions. To characterize these transitions, scaling assumptions from continuous phase transitions are applied