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 11 minimization, are used to reconstruct the signal from the measured data. This paper proposes row-action methods as a computational approach to solving the 11 optimization 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