Row-Action Methods for Compressed Sensing
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.
© Copyright 2006 IEEE. Reprinted with permission. [Posted online: 2006-07-24] JAT was supported by NSF DMS Grant No. 0503299.