Perfectly sampling k ≥ (8/3 + o (1))Δ-colorings in graphs
Vishesh Jaina, Ashwin Sahb, Mehtaab S. Sawhneyb
- aSimons Institute for the Theory of Computing Berkeley, USA
- bMassachusetts Institute of Technology, USA
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Abstract
We present a randomized algorithm which takes as input an undirected graph G on n vertices with maximum degree Δ, and a number of colors k ≥ (8/3 + oΔ(1))Δ, and returns – in expected time Õ(nΔ2logk) – a proper k-coloring of G distributed perfectly uniformly on the set of all proper k-colorings of G. Notably, our sampler breaks the barrier at k = 3Δ encountered in recent work of Bhandari and Chakraborty [STOC 2020]. We also discuss how our methods may be modified to relax the restriction on k to k ≥ (8/3 − є0)Δ for an absolute constant є0 > 0. As in the work of Bhandari and Chakraborty, and the pioneering work of Huber [STOC 1998], our sampler is based on Coupling from the Past [Propp&Wilson, Random Struct. Algorithms, 1995] and the bounding chain method [Huber, STOC 1998; H'aggstr'om& Nelander, Scand. J. Statist., 1999]. Our innovations include a novel bounding chain routine inspired by Jerrum’s analysis of the Glauber dynamics [Random Struct. Algorithms, 1995], as well as a preconditioning routine for bounding chains which uses the algorithmic Lovász Local Lemma [Moser&Tardos, J.ACM, 2010].
TL;DRA randomized algorithm which takes as input an undirected graph G on n vertices with maximum degree Δ, and a number of colors k ≥ (8/3 + oΔ(1))Δ, and returns – in expected time Õ(nΔ2logk) – a proper k-coloring of G distributed perfectly uniformly on the set of all Proper k-colorings of G. (Semantic Scholar)