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Message passing algorithms for compressed sensing: II. analysis and validation

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3 Author(s)
David L. Donoho ; Department of Statistics, Stanford University, USA ; Arian Maleki ; Andrea Montanari

In a recent paper, the authors proposed a new class of low-complexity iterative thresholding algorithms for reconstructing sparse signals from a small set of linear measurements [1]. The new algorithms are broadly referred to as AMP, for approximate message passing. This is the second of two conference papers describing the derivation of these algorithms, connection with related literature, extensions of original framework, and new empirical evidence. This paper describes the state evolution formalism for analyzing these algorithms, and some of the conclusions that can be drawn from this formalism. We carried out extensive numerical simulations to confirm these predictions. We present here a few representative results.

Published in:

Information Theory (ITW 2010, Cairo), 2010 IEEE Information Theory Workshop on

Date of Conference:

6-8 Jan. 2010