Cooled and Relaxed Survey Propagation for MRFs

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Hai Chieu, Wee Lee, Yee Teh


We describe a new algorithm, Relaxed Survey Propagation (RSP), for finding MAP configurations in Markov random fields. We compare its performance with state-of-the-art algorithms including the max-product belief propagation, its se- quential tree-reweighted variant, residual (sum-product) belief propagation, and tree-structured expectation propagation. We show that it outperforms all ap- proaches for Ising models with mixed couplings, as well as on a web person disambiguation task formulated as a supervised clustering problem.