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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  9. Flinth, Axel; Kutyniok, Gitta: PROMP: a sparse recovery approach to lattice-valued signals (2018)
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  13. Mathelin, Lionel; Kasper, Kévin; Abou-Kandil, Hisham: Observable dictionary learning for high-dimensional statistical inference (2018)
  14. Saab, Rayan; Wang, Rongrong; Yılmaz, Özgür: Quantization of compressive samples with stable and robust recovery (2018)
  15. Shen, Jie; Li, Ping: A tight bound of hard thresholding (2018)
  16. Tang, Sunli; Fernandez-Granda, Carlos; Lannuzel, Sylvain; Bernstein, Brett; Lattanzi, Riccardo; Cloos, Martijn; Knoll, Florian; Assländer, Jakob: Multicompartment magnetic resonance fingerprinting (2018)
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  18. Yuan, Xiao-Tong; Li, Ping; Zhang, Tong: Gradient hard thresholding pursuit (2018)
  19. Zhu, Zhihui; Li, Gang; Ding, Jiajun; Li, Qiuwei; He, Xiongxiong: On collaborative compressive sensing systems: the framework, design, and algorithm (2018)
  20. Adamo, Alessandro; Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi: Sparse decomposition by iterating Lipschitzian-type mappings (2017)

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