Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm

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 $A$.
Type de document :
Pré-publication, Document de travail
2015
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https://hal-cea.archives-ouvertes.fr/cea-01140721
Contributeur : Emmanuelle De Laborderie <>
Soumis le : jeudi 9 avril 2015 - 12:12:46
Dernière modification le : mercredi 23 janvier 2019 - 14:39:05

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  • HAL Id : cea-01140721, version 1
  • ARXIV : 1412.2005

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Jeremy Vila, Philip Schniter, Sundeep Rangan, Florent Krzakala, Lenka Zdeborova. Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm. 2015. 〈cea-01140721〉

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