Simple Length-Constrained Minimum Spanning Trees

TL;DR

This paper presents a simple randomized approach to approximate length-constrained minimum spanning trees, improving previous methods by reducing complexity and allowing a tradeoff between approximation quality and diameter loss.

Contribution

The authors introduce a straightforward sampling method that matches prior results without complex computations and enables adjustable approximation and diameter bounds.

Findings

Recovers previous approximation results with simpler methods.

Provides a tradeoff between approximation ratio and diameter loss.

Achieves poly-time polylogarithmic approximation with minimal diameter increase.

Abstract

In the length-constrained minimum spanning tree (MST) problem, we are given an $n$-node edge-weighted graph $G$ and a length constraint $h \geq 1$. Our goal is to find a spanning tree of $G$ whose diameter is at most $h$ with minimum weight. Prior work of Marathe et al.\ gave a poly-time algorithm which repeatedly computes maximum cardinality matchings of minimum weight to output a spanning tree whose weight is $O(\log n)$-approximate with diameter $O(\log n)\cdot h$. In this work, we show that a simple random sampling approach recovers the results of Marathe et al. -- no computation of min-weight max-matchings needed! Furthermore, the simplicity of our approach allows us to tradeoff between the approximation factor and the loss in diameter: we show that for any $\epsilon \geq 1/\operatorname{poly}(n)$, one can output a spanning tree whose weight is $O(n^\epsilon / \epsilon)$-approximate with diameter $O(1/\epsilon)\cdot h$ with high probability in poly-time. This immediately gives the first poly-time $\operatorname{poly}(\log n)$-approximation for length-constrained MST whose loss in diameter is $o(\log n)$.