Correlation decay and deterministic FPTAS for counting list-colorings of a graph

We propose a deterministic algorithm for approximately counting the number of list colorings of a graph. Under the assumption that the graph is triangle free, the size of every list is at least αΔ, where α is an arbitrary constant bigger than α = 2.8432 ..., the solution of αe-1/α = 2, and α is the maximum degree of the graph, we obtain the following results. For the case when the size of the each list is a large constant, we show the existence of a deterministic FPTAS for computing the total number of list colorings. The same deterministic algorithm has complexity 2O(log2 n) without any assumptions on the sizes of the lists, where n is the size of the instance. Our results are not based on the most powerful existing counting technique - rapidly mixing Markov chain method. Rather we build upon concepts from statistical physics, in particular, the decay of correlation phenomena and its implication for the uniqueness of Gibbs measures in infinite graphs. This approach was proposed in two recent papers [BG06] and [Wei05]. The principle insight of the present work is that the correlation decay property can be established with respect to certain computation tree, as opposed to the conventional correlation decay property which is typically established with respect to graph theoretic neighborhoods of a given node. This allows truncation of computation at a logarithmic depth in order to obtain polynomial accuracy in polynomial time. While the analysis conducted in this paper is limited to the problem of counting list colorings, the proposed algorithm can be extended to an arbitrary constraint satisfaction problem in a straightforward way.