Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies
arXiv:1206.3262
Abstract
Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixedpoints of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP fails to converge in many cases of interest. Moreover, the Bethe free energy is non-convex for graphical models with cycles thus introducing great difficulty in deriving efficient algorithms for finding local minima of the free energy for general graphs. In this paper we introduce two efficient BP-like algorithms, one sequential and the other parallel, that are guaranteed to converge to the global minimum, for any graph, over the class of energies known as "convex free energies". In addition, we propose an efficient heuristic for setting the parameters of the convex free energy based on the structure of the graph.
Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)
References in corpus (3)
Cited by in corpus (11)
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- Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference
- Zero-Temperature Limit of a Convergent Algorithm to Minimize the Bethe Free Energy
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