Open Access. Powered by Scholars. Published by Universities.®
Algorithms; Approximation; Basis pursuit; Compressed sensing; Orthogonal matching pursuit; Restricted isometry property; Signal recovery; Sparse approximation; Uncertainty principle
Articles 1 - 1 of 1
Full-Text Articles in Mathematics
Cosamp: Iterative Signal Recovery From Incomplete And Inaccurate Samples, Deanna Needell, J. A. Tropp
Cosamp: Iterative Signal Recovery From Incomplete And Inaccurate Samples, Deanna Needell, J. A. Tropp
CMC Faculty Publications and Research
Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery algorithm calledCoSaMP that delivers the same guarantees as the best optimization-based approaches. Moreover, this algorithm offers rigorous bounds on computational cost and storage. It is likely to be extremely efficient for practical problems because it requires only matrix–vector multiplies with the sampling matrix. For compressible signals, the running time is just O(Nlog2N), where N is the length …