TY - GEN

T1 - Convergent message-passing algorithms for inference over general graphs with convex free energies

AU - Hazan, Tamir

AU - Shashua, Amnon

PY - 2008

Y1 - 2008

N2 - 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 fixed-points 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.

AB - 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 fixed-points 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.

UR - http://www.scopus.com/inward/record.url?scp=78651455153&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78651455153

SN - 0974903949

SN - 9780974903941

T3 - Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

SP - 264

EP - 273

BT - Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

T2 - 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Y2 - 9 July 2008 through 12 July 2008

ER -