Uncertainty in Artificial Intelligence
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Distributed Anytime MAP Inference
Joop van de Ven, Fabio Ramos
We present a distributed anytime algorithm for performing MAP inference in graphical models. The problem is formulated as a linear programming relaxation over the edges of a graph. The resulting program has a constraint structure that allows application of the Dantzig-Wolfe decomposition principle. Subprograms are defined over individual edges and can be computed in a distributed manner. This accommodates solutions to graphs whose state space does not fit in memory. The decomposition master program is guaranteed to compute the optimal solution in a finite number of iterations, while the solution converges monotonically with each iteration. Formulating the MAP inference problem as a linear program allows additional (global) constraints to be defined; something not possible with message passing algorithms. Experimental results show that our algorithm's solution quality outperforms most current algorithms and it scales well to large problems.
Pages: 708-716
PS Link:
PDF Link: /papers/11/p708-van_de_ven.pdf
@INPROCEEDINGS{van de Ven11,
AUTHOR = "Joop van de Ven and Fabio Ramos",
TITLE = "Distributed Anytime MAP Inference",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "708--716"

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