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.
|Original language||English (US)|
|Number of pages||10|
|State||Published - Nov 28 2013|
|Event||29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, United States|
Duration: Jul 11 2013 → Jul 15 2013
|Other||29th Conference on Uncertainty in Artificial Intelligence, UAI 2013|
|Period||7/11/13 → 7/15/13|