On $b$-Matching and Fully-Dynamic Maximum $k$-Edge Coloring

TL;DR

This paper introduces new algorithms for the dynamic maximum $k$-edge coloring problem, leveraging advances in $b$-matching, and provides approximation guarantees with efficient update times.

Contribution

It presents three novel algorithms for dynamic maximum $k$-edge coloring, building on recent $b$-matching techniques, with improved approximation ratios and update times.

Findings

Achieves a $(2+ ext{epsilon})rac{k+1}{k}$-approximation with polylogarithmic update time.

Provides a $(8+ ext{epsilon})rac{3k+3}{3k-1}$-approximation with polylogarithmic update time.

Introduces a greedy algorithm with $O( ext{Delta}+k)$ update time and a 2.16-approximation factor.

Abstract

Given a graph $G$ that is modified by a sequence of edge insertions and deletions, we study the Maximum $k$-Edge Coloring problem Having access to $k$ colors, how can we color as many edges of $G$ as possible such that no two adjacent edges share the same color? While this problem is different from simply maintaining a $b$-matching with $b=k$, the two problems are closely related: a maximum $k$-matching always contains a $\frac{k+1}k$-approximate maximum $k$-edge coloring. However, maximum $b$-matching can be solved efficiently in the static setting, whereas the Maximum $k$-Edge Coloring problem is NP-hard and even APX-hard for $k \ge 2$. We present new results on both problems: For $b$-matching, we show a new integrality gap result and for the case where $b$ is a constant, we adapt Wajc's matching sparsification scheme~[STOC20]. Using these as basis, we give three new algorithms for the dynamic Maximum $k$-Edge Coloring problem: Our MatchO algorithm builds on the dynamic $(2+\epsilon)$-approximation algorithm of Bhattacharya, Gupta, and Mohan~[ESA17] for $b$-matching and achieves a $(2+\epsilon)\frac{k+1} k$-approximation in $O(poly(\log n, \epsilon^{-1}))$ update time against an oblivious adversary. Our MatchA algorithm builds on the dynamic $8$-approximation algorithm by Bhattacharya, Henzinger, and Italiano~[SODA15] for fractional $b$-matching and achieves a $(8+\epsilon)\frac{3k+3}{3k-1}$-approximation in $O(poly(\log n, \epsilon^{-1}))$ update time against an adaptive adversary. Moreover, our reductions use the dynamic $b$-matching algorithm as a black box, so any future improvement in the approximation ratio for dynamic $b$-matching will automatically translate into a better approximation ratio for our algorithms. Finally, we present a greedy algorithm that runs in $O(\Delta+k)$ update time, while guaranteeing a $2.16$~approximation factor.