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Published July 24, 2006 | public
Book Section - Chapter Open

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.

Additional Information

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


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August 22, 2023
August 22, 2023