Bethe-ADMM for Tree Decomposition based Parallel MAP Inference
Abstract
We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based proximal function, which makes each subproblem in ADMM easy to solve in parallel using the sum-product algorithm. We rigorously prove global convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on both synthetic and real datasets to illustrate its effectiveness. Further, the parallel Bethe-ADMM is shown to scale almost linearly with increasing number of cores.
Cite
@article{arxiv.1309.6829,
title = {Bethe-ADMM for Tree Decomposition based Parallel MAP Inference},
author = {Qiang Fu and Huahua Wang and Arindam Banerjee},
journal= {arXiv preprint arXiv:1309.6829},
year = {2013}
}
Comments
Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)