Bethe-ADMM for tree decomposition based parallel MAP inference

Qiang Fu, Huahua Wang, Arindam Banerjee

Research output: Contribution to conferencePaperpeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages222-231
Number of pages10
StatePublished - Nov 28 2013
Event29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, United States
Duration: Jul 11 2013Jul 15 2013

Other

Other29th Conference on Uncertainty in Artificial Intelligence, UAI 2013
CountryUnited States
CityBellevue, WA
Period7/11/137/15/13

Fingerprint Dive into the research topics of 'Bethe-ADMM for tree decomposition based parallel MAP inference'. Together they form a unique fingerprint.

Cite this