Bethe Bounds and Approximating the Global Optimum

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Adrian Weller, Tony Jebara ;
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:618-631, 2013.

Abstract

Inference in general Markov random fields (MRFs) is NP-hard, though identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with submodular cost functions is efficiently solvable using graph cuts. Marginal inference, however, even for this restricted class, is #P-hard. Restricting to binary pairwise models, we prove new formulations of derivatives of the Bethe free energy, provide bounds on the derivatives and bracket the locations of stationary points. Several results apply whether the model is associative or not. Applying these to discretized pseudo-marginals in the associative case, we present a polynomial time approximation scheme for global optimization of the Bethe free energy provided the maximum degree ∆=O(\log n), where n is the number of variables. Runtime is guaranteed O(ε^-3/2 n^6 Σ^3/4 Ω^3/2), where Σ=O(∆/n) is the fraction of possible edges present and Ωis a function of MRF parameters. We examine use of the algorithm in practice, demonstrating runtime that is typically much faster, and discuss several extensions.

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