Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):64-72, 2014.
Maximum a posteriori (MAP) inference over discrete Markov random fields is a central task spanning a wide spectrum of real-world applications but known to be NP-hard for general graphs. In this paper, we propose a novel semidefinite relaxation formulation (referred to as SDR) to estimate the MAP assignment. Algorithmically, we develop an accelerated variant of the alternating direction method of multipliers (referred to as SDPAD-LR) that can effectively exploit the special structure of SDR. Encouragingly, the proposed procedure allows solving SDR for large-scale problems, e.g. problems comprising hundreds of thousands of variables with multiple states on a grid graph. Compared with prior SDP solvers, SDPAD-LR is capable of attaining comparable accuracy while exhibiting remarkably improved scalability. This contradicts the commonly held belief that semidefinite relaxation can only been applied on small-scale problems. We have evaluated the performance of SDR on various benchmark datasets including OPENGM2 and PIC. Experimental results demonstrate that for a broad class of problems, SDPAD-LR outperforms state-of-the-art algorithms in producing better MAP assignments.