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Random sampling of colourings of sparse random graphs with a constant number of colours

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 Added by Charilaos Efthymiou
 Publication date 2008
and research's language is English




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In this work we present a simple and efficient algorithm which, with high probability, provides an almost uniform sample from the set of proper k-colourings on an instance of a sparse random graph G(n,d/n), where k=k(d) is a sufficiently large constant. Our algorithm is not based on the Markov Chain Monte Carlo method (M.C.M.C.). Instead, we provide a novel proof of correctness of our Algorithm that is based on interesting spatial mixing properties of colourings of G(n,d/n). Our result improves upon previous results (based on M.C.M.C.) that required a number of colours growing unboundedly with n.



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