Prize-Collecting Steiner Tree: A 1.79 Approximation

TL;DR

This paper presents a significant improvement in approximating the Prize-Collecting Steiner Tree problem, reducing the approximation factor from 1.9672 to 1.7994 using a novel iterative method.

Contribution

Introduces a new iterative approach that achieves a 1.7994 approximation for PCST, surpassing the long-standing 2-approximation barrier.

Findings

Achieved a 1.7994 approximation ratio for PCST.

Surpassed the previous 15-year-old approximation barrier.

Demonstrated effectiveness of the iterative approach in combinatorial optimization.

Abstract

Prize-Collecting Steiner Tree (PCST) is a generalization of the Steiner Tree problem, a fundamental problem in computer science. In the classic Steiner Tree problem, we aim to connect a set of vertices known as terminals using the minimum-weight tree in a given weighted graph. In this generalized version, each vertex has a penalty, and there is flexibility to decide whether to connect each vertex or pay its associated penalty, making the problem more realistic and practical. Both the Steiner Tree problem and its Prize-Collecting version had long-standing $2$-approximation algorithms, matching the integrality gap of the natural LP formulations for both. This barrier for both problems has been surpassed, with algorithms achieving approximation factors below $2$. While research on the Steiner Tree problem has led to a series of reductions in the approximation ratio below $2$, culminating in a $\ln(4)+\epsilon$ approximation by Byrka, Grandoni, Rothvo{\ss}, and Sanit\`a, the Prize-Collecting version has not seen improvements in the past 15 years since the work of Archer, Bateni, Hajiaghayi, and Karloff, which reduced the approximation factor for this problem from $2$ to $1.9672$. Interestingly, even the Prize-Collecting TSP approximation, which was first improved below $2$ in the same paper, has seen several advancements since then. In this paper, we reduce the approximation factor for the PCST problem substantially to 1.7994 via a novel iterative approach.