CoSaMP
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. 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 called CoSaMP 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 $O(Nlog ^{2}N)$, where $N$ is the length of the signal.
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References in zbMATH (referenced in 125 articles )
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- Satpathi, Siddhartha; Chakraborty, Mrityunjoy: On the number of iterations for convergence of CoSaMP and subspace pursuit algorithms (2017)
- Stanković, Ljubiša; Daković, Miloš; Vujović, Stefan: Reconstruction of sparse signals in impulsive disturbance environments (2017)
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- Zhu, Zhihui; Wakin, Michael B.: Approximating sampled sinusoids and multiband signals using multiband modulated DPSS dictionaries (2017)