Approximating Node-Weighted k-MST on Planar Graphs

TL;DR

This paper presents a near-optimal approximation algorithm for the node-weighted k-MST problem on planar graphs, improving previous bounds by leveraging primal-dual and Lagrangian relaxation techniques.

Contribution

It introduces a $(4+ ext{epsilon})$-approximation algorithm for the node-weighted k-MST problem on planar graphs, extending to a broader class of graphs with similar approximation guarantees.

Findings

Achieves a $(4+ ext{epsilon})$-approximation for the problem.

Generalizes to a $(rac{4}{3} imes r + ext{epsilon})$-approximation for certain graph classes.

Provides lower bounds indicating the approximation factor is essentially optimal.

Abstract

We study the problem of finding a minimum weight connected subgraph spanning at least $k$ vertices on planar, node-weighted graphs. We give a $(4+\eps)$-approximation algorithm for this problem. We achieve this by utilizing the recent LMP primal-dual $3$-approximation for the node-weighted prize-collecting Steiner tree problem by Byrka et al (SWAT'16) and adopting an approach by Chudak et al. (Math.\ Prog.\ '04) regarding Lagrangian relaxation for the edge-weighted variant. In particular, we improve the procedure of picking additional vertices (tree merging procedure) given by Sadeghian (2013) by taking a constant number of recursive steps and utilizing the limited guessing procedure of Arora and Karakostas (Math.\ Prog.\ '06). More generally, our approach readily gives a $(\nicefrac{4}{3}\cdot r+\eps)$-approximation on any graph class where the algorithm of Byrka et al.\ for the prize-collecting version gives an $r$-approximation. We argue that this can be interpreted as a generalization of an analogous result by K\"onemann et al. (Algorithmica~'11) for partial cover problems. Together with a lower bound construction by Mestre (STACS'08) for partial cover this implies that our bound is essentially best possible among algorithms that utilize an LMP algorithm for the Lagrangian relaxation as a black box. In addition to that, we argue by a more involved lower bound construction that even using the LMP algorithm by Byrka et al.\ in a \emph{non-black-box} fashion could not beat the factor $\nicefrac{4}{3}\cdot r$ when the tree merging step relies only on the solutions output by the LMP algorithm.