Adaptive damping and mean removal for the generalized approximate message passing algorithm

Jeremy P. Vila; Philip Schniter; Sundeep Rangan; Florent Krzakala; Lenka Zdeborová

ICASSP 2015accepted0 citations

Adaptive damping and mean removal for the generalized approximate message passing algorithm

Jeremy P. Vila, Philip Schniter, Sundeep Rangan, Florent Krzakala, Lenka Zdeborová

Abstract

The generalized approximate message passing (GAMP) algorithm is an efficient method of MAP or approximate-MMSE estimation of x observed from a noisy version of the transform coefficients z = Ax. In fact, for large zero-mean i.i.d sub-Gaussian A, GAMP is characterized by a state evolution whose fixed points, when unique, are optimal. For generic A, however, GAMP may diverge. In this paper, we propose adaptive-damping and mean-removal strategies that aim to prevent divergence. Numerical results demonstrate significantly enhanced robustness to non-zero-mean, rank-deficient, column-correlated, and ill-conditioned A.

BibTeX

@inproceedings{icassp2015_adaptivedampinga,
  title = {Adaptive damping and mean removal for the generalized approximate message passing algorithm},
  author = {Jeremy P. Vila and Philip Schniter and Sundeep Rangan and Florent Krzakala and Lenka Zdeborová},
  booktitle = {ICASSP 2015},
  year = {2015}
}