Year: 2013
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Fu, Qiang, Wang, Huahua, Banerjee, Arindam
Institution: University of Minnesota
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: RI: Small: Statistical Modeling of Dynamic Covariance Matrices, G6671297155, Collaborative Research: Understanding Climate Change: A Data Driven Approach, CAREER: Combinatorial Online Learning and its Applications, III-COR-Small: Multi-Relational Data Clustering with Probabilistic Mixture Models
OpenAlex ID: W2197670072