Better Learning-Augmented Spanning Tree Algorithms via Metric Forest Completion

TL;DR

This paper introduces a generalized learning-augmented algorithm for approximate minimum spanning trees in metric spaces, improving approximation factors and computational efficiency through strategic edge selection and theoretical analysis.

Contribution

It develops a new method interpolating between existing algorithms, reducing approximation factors for metric MST and providing better instance-specific results.

Findings

Improved approximation factor from 2.62 to 2 for MFC.

Enhanced approximation from (2γ+1) to 2γ for metric MST.

Experimental results validate theoretical improvements.

Abstract

We present improved learning-augmented algorithms for finding an approximate minimum spanning tree (MST) for points in an arbitrary metric space. Our work follows a recent framework called metric forest completion (MFC), where the learned input is a forest that must be given additional edges to form a full spanning tree. Veldt et al. (2025) showed that optimally completing the forest takes $\Omega(n^2)$ time, but designed a 2.62-approximation for MFC with subquadratic complexity. The same method is a $(2\gamma + 1)$-approximation for the original MST problem, where $\gamma \geq 1$ is a quality parameter for the initial forest. We introduce a generalized method that interpolates between this prior algorithm and an optimal $\Omega(n^2)$-time MFC algorithm. Our approach considers only edges incident to a growing number of strategically chosen ``representative'' points. One corollary of our analysis is to improve the approximation factor of the previous algorithm from 2.62 for MFC and $(2\gamma+1)$ for metric MST to 2 and $2\gamma$ respectively. We prove this is tight for worst-case instances, but we still obtain better instance-specific approximations using our generalized method. We complement our theoretical results with a thorough experimental evaluation.