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Augmenting Dual Decomposition for MAP Inference Andre F. T. Martins
 

Summary: Augmenting Dual Decomposition for MAP Inference
Andr´e F. T. Martins
Noah A. Smith
Eric P. Xing

School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
Pedro M. Q. Aguiar

Instituto de Sistemas e Rob´otica,
Instituto Superior T´ecnico, Lisboa, Portugal
M´ario A. T. Figueiredo

Instituto de Telecomunicac¸~oes,
Instituto Superior T´ecnico, Lisboa, Portugal
Abstract
In this paper, we propose combining augmented Lagrangian optimization with
the dual decomposition method to obtain a fast algorithm for approximate MAP
(maximum a posteriori) inference on factor graphs. We also show how the pro-
posed algorithm can efficiently handle problems with (possibly global) structural
constraints. The experimental results reported testify for the state-of-the-art per-

  

Source: Aguiar, Pedro M. Q. - Institute for Systems and Robotics (Lisbon)

 

Collections: Engineering