TY - GEN
T1 - Row-action methods for compressed sensing
AU - Sra, Suvrit
AU - Tropp, Joel A.
PY - 2006
Y1 - 2006
N2 - 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 row-action methods as a computational approach to solving the ℓ1 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.
AB - 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 row-action methods as a computational approach to solving the ℓ1 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.
UR - http://www.scopus.com/inward/record.url?scp=33947698802&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33947698802
SN - 142440469X
SN - 9781424404698
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - III868-III871
BT - 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Y2 - 14 May 2006 through 19 May 2006
ER -