An improved approximation guarantee for Prize-Collecting TSP

TL;DR

This paper introduces a new approximation algorithm for the metric prize-collecting TSP that improves the approximation factor from 1.915 to 1.774 by employing a refined LP decomposition technique.

Contribution

It presents the first algorithm achieving an approximation guarantee below 1.8 for PCTSP, significantly narrowing the gap with classical TSP.

Findings

Achieved an approximation ratio of 1.774 for PCTSP.

Developed a refined LP decomposition technique.

Improved the theoretical understanding of PCTSP approximability.

Abstract

We present a new approximation algorithm for the (metric) prize-collecting traveling salesperson problem (PCTSP). In PCTSP, opposed to the classical traveling salesperson problem (TSP), one may not include a vertex of the input graph in the returned tour at the cost of a given vertex-dependent penalty, and the objective is to balance the length of the tour and the incurred penalties for omitted vertices by minimizing the sum of the two. We present an algorithm that achieves an approximation guarantee of $1.774$ with respect to the natural linear programming relaxation of the problem. This significantly reduces the gap between the approximability of classical TSP and PCTSP, beating the previously best known approximation factor of $1.915$. As a key ingredient of our improvement, we present a refined decomposition technique for solutions of the LP relaxation, and show how to leverage components of that decomposition as building blocks for our tours.