Hybrid approximate message passing
Files
Accepted manuscript
Date
2017-09-01
Authors
Rangan, Sundeep
Fletcher, Alyson K.
Goyal, Vivek K.
Byrne, Evan
Schniter, Philip
Version
OA Version
Citation
Sundeep Rangan, Alyson K Fletcher, Vivek K Goyal, Evan Byrne, Philip Schniter. 2017. "Hybrid Approximate Message Passing." IEEE TRANSACTIONS ON SIGNAL PROCESSING, Volume 65, Issue 17, pp. 4577 - 4592 (16).
Abstract
Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing interactions through aggregates of small, linearizable couplings of variables. AMP approximations based on the Central Limit Theorem can be readily applied to aggregates of many weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (HyGAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance--complexity trade-off can be achieved. Group sparsity and multinomial logistic regression problems are studied as examples of the proposed methodology.