The Complexity of Approximating a Bethe Equilibrium

Computer Science – Artificial Intelligence

Scientific paper

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Scientific paper

This paper resolves a common complexity issue in the Bethe approximation of statistical physics and the sum-product Belief Propagation (BP) algorithm of artificial intelligence. The Bethe approximation reduces the problem of computing the partition function in a graphical model to that of solving a set of non-linear equations, so-called the Bethe equation. On the other hand, the BP algorithm is a popular heuristic method for estimating marginal distributions in a graphical model. Although they are inspired and developed from different directions, Yedidia, Freeman and Weiss (2004) established a close connection: the BP algorithm solves the Bethe equation if it converges (however, it often does not). This naturally motivates the following important question to understand their limitations and empirical successes: the Bethe equation is computationally easy to solve? We present a message passing algorithm solving the Bethe equation in polynomial number of operations for arbitrary binary graphical models of n variables where the maximum degree in the underlying graph is O(log n). Our algorithm, an alternative to BP fixing its convergence issue, is the first fully polynomial-time approximation scheme for the BP fixed point computation in such a large class of graphical models, while the approximate fixed point computation is known to be (PPAD-)hard in general. We believe that our technique is of broader interest to understand the computational complexity of the cavity method in statistical physics.

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