Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations

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

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Amir Globerson, Tommi Jaakkola


We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to max-product but unlike max-product it always converges, and can be proven to find the exact MAP solution in various settings. The algorithm is derived via block coordinate descent in a dual of the LP relaxation of MAP, but does not require any tunable parameters such as step size or tree weights. We also describe a generalization of the method to cluster based potentials. The new method is tested on synthetic and real-world problems, and compares favorably with previous approaches.