The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
Randomly coloring sparse random graphs with fewer colors than the maximum degree
Random Structures & Algorithms
Random sampling of colourings of sparse random graphs with a constant number of colours
Theoretical Computer Science
Algorithmic Barriers from Phase Transitions
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Catching the k-NAESAT threshold
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Hi-index | 0.00 |
Approximate random k-colouring of a graph G = (V, E) is a very well studied problem in computer science and statistical physics. It amounts to constructing a k-colouring of G which is distributed close to Gibbs distribution, i.e. the uniform distribution over all the k-colourings of G. Here, we deal with the problem when the underlying graph is an instance of Erdős-Rényi random graph G(n, p), where p = d/n and d is fixed. We propose a novel efficient algorithm for approximate random k-colouring with the following properties: given an instance of G(n, d/n) and for any k ≥ (2 + ε)d, it returns a k-colouring distributed within total variation distance n−Ω(1) from the Gibbs distribution, with probability 1 − n−Ω(1). What we propose is neither a MCMC algorithm nor some algorithm inspired by the message passing heuristics that were introduced by statistical physicists. Our algorithm is of combinatorial nature. It is based on a rather simple recursion which reduces the random k-colouring of G(n, d/n) to random k-colouring simpler subgraphs first. The lower bound on the number of colours for our algorithm to run in polynomial time is significantly smaller than the corresponding bounds we have for any previous algorithm.