Zero-Temperature Limit of a Convergent Algorithm to Minimize the Bethe Free Energy

Details Zero-Temperature Limit of a Convergent Algorithm to Minimize the Bethe Free Energy Zero-Temperature Limit of a Convergent Algorithm to Minimize the Bethe Free Energy

2011-12-22

arxiv.org/abs/1112.5298v1

Computer Science

Computer Vision and Pattern Recognition

Research Report

Scientific paper

After the discovery that fixed points of loopy belief propagation coincide with stationary points of the Bethe free energy, several researchers proposed provably convergent algorithms to directly minimize the Bethe free energy. These algorithms were formulated only for non-zero temperature (thus finding fixed points of the sum-product algorithm) and their possible extension to zero temperature is not obvious. We present the zero-temperature limit of the double-loop algorithm by Heskes, which converges a max-product fixed point. The inner loop of this algorithm is max-sum diffusion. Under certain conditions, the algorithm combines the complementary advantages of the max-product belief propagation and max-sum diffusion (LP relaxation): it yields good approximation of both ground states and max-marginals.

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