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Row-Action Methods for Compressed Sensing

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons76142

Sra,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Sra, S. (2006). Row-Action Methods for Compressed Sensing. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 868-871.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D1ED-C
Zusammenfassung
Compressed Sensing uses a small number of random, linear measurements to acquire a sparse signal. Nonlinear algorithms, such as l1 minimization, are used to reconstruct the signal from the measured data. This paper proposes rowaction methods as a computational approach to solving the l1 optimization problem. This paper presents a specific rowaction 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.