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 $A$.

References in corpus (2)

• Compressive Phase Retrieval via Generalized Approximate Message Passing
• Sparse Estimation with the Swept Approximated Message-Passing Algorithm

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