Properties of spatial coupling in compressed sensing by Francesco Caltagirone, Lenka Zdeborová
In this paper we address a series of open questions about the construction of spatially coupled measurement matrices in compressed sensing. For hardware implementations one is forced to depart from the limiting regime of parameters in which the proofs of the so-called threshold saturation work. We investigate quantitatively the behavior under finite coupling range, the dependence on the shape of the coupling interaction, and optimization of the so-called seed to minimize distance from optimality. Our analysis explains some of the properties observed empirically in previous works and provides new insight on spatially coupled compressed sensing.
On Convergence of Approximate Message Passing by Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová
Approximate message passing is an iterative algorithm for compressed sensing and related applications. A solid theory about the performance and convergence of the algorithm exists for measurement matrices having iid entries of zero mean. However, it was observed by several authors that for more general matrices the algorithm often encounters convergence problems. In this paper we identify the reason of the non-convergence for measurement matrices with iid entries and non-zero mean in the context of Bayes optimal inference. Finally we demonstrate numerically that when the iterative update is changed from parallel to sequential the convergence is restored.
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