Date of Original Version
Proceedings of the International Workshop on Optimization for Machine Learning (OPT)
Abstract or Description
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 proposed algorithm can efficiently handle problems with (possibly global) structural constraints. The experimental results reported testify for the state-of-the-art performance of the proposed approach.
Proceedings of the International Workshop on Optimization for Machine Learning (OPT).