Approximate Tree Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees
Abstract
Finding a minimum spanning tree (MST) for points in an arbitrary metric space is a fundamental primitive for hierarchical clustering and many other ML tasks, but this takes time to even approximate. We introduce a framework for metric MSTs that first (1) finds a forest of disconnected components using practical heuristics, and then (2) finds a small weight set of edges to connect disjoint components of the forest into a spanning tree. We prove that optimally solving the second step still takes time, but we provide a subquadratic 2.62-approximation algorithm. In the spirit of learning-augmented algorithms, we then show that if the forest found in step (1) overlaps with an optimal MST, we can approximate the original MST problem in subquadratic time, where the approximation factor depends on a measure of overlap. In practice, we find nearly optimal spanning trees for a wide range of metrics, while being orders of magnitude faster than exact algorithms.
Cite
@article{arxiv.2502.12993,
title = {Approximate Tree Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees},
author = {Nate Veldt and Thomas Stanley and Benjamin W. Priest and Trevor Steil and Keita Iwabuchi and T. S. Jayram and Geoffrey Sanders},
journal= {arXiv preprint arXiv:2502.12993},
year = {2025}
}