Fine-Grained Complexity of Computing Degree-Constrained Spanning Trees
Abstract
We investigate the computation of minimum-cost spanning trees satisfying prescribed vertex degree constraints: Given a graph and a constraint function , we ask for a (minimum-cost) spanning tree such that for each vertex , achieves a degree specified by . Specifically, we consider three kinds of constraint functions ordered by their generality -- may either assign each vertex to a list of admissible degrees, an upper bound on the degrees, or a specific degree. Using a combination of novel techniques and state-of-the-art machinery, we obtain an almost-complete overview of the fine-grained complexity of these problems taking into account the most classical graph parameters of the input graph . In particular, we present SETH-tight upper and lower bounds for these problems when parameterized by the pathwidth and cutwidth, an ETH-tight algorithm parameterized by the cliquewidth, and a nearly SETH-tight algorithm parameterized by treewidth. In order to obtain our upper bound for clique-width, we develop a novel technique of double representation through ``requirement shifting''. Using this technique, we also obtain an ETH-tight single-exponential XP algorithm for the Exact Leaf Spanning Tree problem parameterized by clique-width, which settles the final remaining open case for clique-width from the classical Cut and Count of Cygan et al. [FOCS 2011, TALG 2022]. This shows the versatility of our technique and its potential applicability to other problems as well. Additionally, in order to establish our lower and upper bounds we introduce a number of tools which may be of independent interest, including lazy coloring and ``asymptotic'' SETH-based reductions for structural parameters.
Cite
@article{arxiv.2503.15226,
title = {Fine-Grained Complexity of Computing Degree-Constrained Spanning Trees},
author = {Narek Bojikian and Alexander Firbas and Robert Ganian and Hung P. Hoang and Krisztina Szilágyi},
journal= {arXiv preprint arXiv:2503.15226},
year = {2026}
}