| Paper: | SPTM-P14.11 | ||
| Session: | Sampling, Extrapolation and Interpolation II | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Sampling, Extrapolation, and Interpolation | ||
| Title: | ROW-ACTION METHODS FOR COMPRESSED SENSING | ||
| Authors: | Suvrit Sra, University of Texas, Austin, United States; Joel Tropp, University of Michigan, Ann Arbor, United States | ||
| Abstract: | \emph{Compressed Sensing} uses a small number of random, linear measurements to acquire a sparse signal. Nonlinear algorithms, such as $\ell_1$ minimization, are used to reconstruct the signal from the measured data. This paper proposes \emph{row-action} methods as a computational approach to solving the $\ell_1$ 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. | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012