An algorithmic Vizing's theorem: toward efficient edge-coloring sampling with an optimal number of colors

TL;DR

This paper introduces a randomized algorithm for efficiently sampling proper edge-colorings of graphs with maximum degree , specifically focusing on the case of +1 colors as guaranteed by Vizing's theorem, and explores its theoretical properties.

Contribution

It provides an initial algorithmic approach to sample +1 edge-colorings efficiently, connecting Vizing's theorem with randomized sampling methods.

Findings

The algorithm almost surely produces a proper +1 edge-coloring.

The approach offers an algorithmic interpretation of Vizing's theorem.

Several conjectures are proposed regarding the algorithm's efficiency and uniformity.

Abstract

The problem of sampling edge-colorings of graphs with maximum degree $\Delta$ has received considerable attention and efficient algorithms are available when the number of colors is large enough with respect to $\Delta$. Vizing's theorem guarantees the existence of a $(\Delta+1)$-edge-coloring, raising the natural question of how to efficiently sample such edge-colorings. In this paper, we take an initial step toward addressing this question. Building on the approach of Dotan, Linial, and Peled, we analyze a randomized algorithm for generating random proper $(\Delta+1)$-edge-colorings, which in particular provides an algorithmic interpretation of Vizing's theorem. The idea is to start from an arbitrary non-proper edge-coloring with the desired number of colors and at each step, recolor one edge uniformly at random provided it does not increase the number of conflicting edges (a potential function will count the number of pairs of adjacent edges of the same color). We show that the algorithm almost surely produces a proper $(\Delta+1)$-edge-coloring and propose several conjectures regarding its efficiency and the uniformity of the sampled colorings.