Row-Action Methods for Compressed Sensing

Abstract

Compressed Sensing uses a small number of random, linear measurements to acquire a sparse signal. Nonlinear algorithms, such as l1minimization, are used to reconstruct the signal from the measured data. This paper proposes row-action methods as a computational approach to solving the l1optimization problem. This paper presents a specific row-action method and provides extensive empirical evidence that it is an effective technique for signal reconstruction. This approach offers several advantages over interior-point methods, including minimal storage and computational requirements, scalability, and robustness.

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