Online Edge Coloring: Sharp Thresholds

TL;DR

This paper advances online edge coloring by providing deterministic and randomized algorithms that achieve near-optimal colorings for graphs with high maximum degree, surpassing previous bounds and establishing sharp thresholds.

Contribution

It introduces a deterministic algorithm for $(1 + o(1))\Delta$-coloring when $\Delta = \omega(\log n)$ and a randomized algorithm for the same guarantee when $\Delta = \omega(\sqrt{\log n})$, improving prior results.

Findings

Deterministic algorithm achieves near-optimal coloring for high-degree graphs.

Randomized algorithm surpasses previous bounds for $\Delta = \omega(\sqrt{\log n})$.

Sharp thresholds established for when greedy algorithms can be improved.

Abstract

Vizing's theorem guarantees that every graph with maximum degree $\Delta$ admits an edge coloring using $\Delta + 1$ colors. In online settings - where edges arrive one at a time and must be colored immediately - a simple greedy algorithm uses at most $2\Delta - 1$ colors. Over thirty years ago, Bar-Noy, Motwani, and Naor [IPL'92] proved that this guarantee is optimal among deterministic algorithms when $\Delta = O(\log n)$, and among randomized algorithms when $\Delta = O(\sqrt{\log n})$. While deterministic improvements seemed out of reach, they conjectured that for graphs with $\Delta = \omega(\log n)$, randomized algorithms can achieve $(1 + o(1))\Delta$ edge coloring. This conjecture was recently resolved in the affirmative: a $(1 + o(1))\Delta$-coloring is achievable online using randomization for all graphs with $\Delta = \omega(\log n)$ [BSVW STOC'24]. Our results go further, uncovering two findings not predicted by the original conjecture. First, we give a deterministic online algorithm achieving $(1 + o(1))\Delta$-colorings for all $\Delta = \omega(\log n)$. Second, we give a randomized algorithm achieving $(1 + o(1))\Delta$-colorings already when $\Delta = \omega(\sqrt{\log n})$. Our results establish sharp thresholds for when greedy can be surpassed, and near-optimal guarantees can be achieved - matching the impossibility results of [BNMN IPL'92], both deterministically and randomly.