Work

State evolution for general approximate message passing algorithms, with applications to spatial coupling

Year: 2013

Type: article

Abstract: We consider a class of approximated message passing (AMP) algorithms and characterize their high-dimensional behavior in terms of a suitable state evolution recursion. Our proof applies to Gaussian matrices with independent but not necessarily identically distributed entries. It covers—in particular—the analysis of generalized AMP, introduced by Rangan, and of AMP reconstruction in compressed sensing with spatially coupled sensing matrices. The proof technique builds on that of Bayati & Montanari [2], while simplifying and generalizing several steps.

Source: Information and Inference A Journal of the IMA

DOI: 10.1093/imaiai/iat004

Authors: A. Javanmard, A. Montanari

Institution: Stanford University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

OpenAlex ID: W2118902885

FWCI: 14.55

Cites: 31

Cited by: 252

Related to: 9