New formulations and branch-and-cut procedures for the longest induced path problem

TL;DR

This paper introduces two new mathematical formulations and branch-and-cut algorithms for the longest induced path problem, demonstrating superior computational performance over existing methods through extensive experiments.

Contribution

The paper presents two novel formulations with exponential constraints and an effective branch-and-cut approach, outperforming previous state-of-the-art methods in solving the longest induced path problem.

Findings

The 'cec' formulation solves almost all benchmark instances efficiently.

The 'cut' formulation and existing models are polyhedrally equivalent.

New approaches outperform the state-of-the-art in median solution times.

Abstract

Given an undirected graph $G=(V,E)$, the longest induced path problem (LIPP) consists of obtaining a maximum cardinality subset $W\subseteq V$ such that $W$ induces a simple path in $G$. In this paper, we propose two new formulations with an exponential number of constraints for the problem, together with effective branch-and-cut procedures for its solution. While the first formulation (cec) is based on constraints that explicitly eliminate cycles, the second one (cut) ensures connectivity via cutset constraints. We compare, both theoretically and experimentally, the newly proposed approaches with a state-of-the-art formulation recently proposed in the literature. More specifically, we show that the polyhedra defined by formulation cut and that of the formulation available in the literature are the same. Besides, we show that these two formulations are stronger in theory than cec. We also propose a new branch-and-cut procedure using the new formulations. Computational experiments show that the newly proposed formulation cec, although less strong from a theoretical point of view, is the best performing approach as it can solve all but one of the 1065 benchmark instances used in the literature within the given time limit. In addition, our newly proposed approaches outperform the state-of-the-art formulation when it comes to the median times to solve the instances to optimality. Furthermore, we perform extended computational experiments considering more challenging and hard-to-solve larger instances and evaluate the impacts on the results when offering initial feasible solutions (warm starts) to the formulations.