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.


References in zbMATH (referenced in 197 articles )

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  1. Blanchard, Jeffrey D.; Leedy, Caleb; Wu, Yimin: On rank awareness, thresholding, and MUSIC for joint sparse recovery (2020)
  2. Casazza, Peter G.; Chen, Xuemei; Lynch, Richard G.: Preserving injectivity under subgaussian mappings and its application to compressed sensing (2020)
  3. Cheng, Wanyou; Chen, Zixin; Hu, Qingjie: An active set Barzilar-Borwein algorithm for (l_0) regularized optimization (2020)
  4. Dupé, François-Xavier; Anthoine, Sandrine: Generalized greedy alternatives (2020)
  5. Haddou, M.; Migot, T.: A smoothing method for sparse optimization over convex sets (2020)
  6. Li, Song; Lin, Junhong; Liu, Dekai; Sun, Wenchang: Iterative hard thresholding for compressed data separation (2020)
  7. Tirer, Tom; Giryes, Raja: Generalizing CoSaMP to signals from a union of low dimensional linear subspaces (2020)
  8. Tong, Fenghua; Li, Lixiang; Peng, Haipeng; Yang, Yixian: An effective algorithm for the spark of sparse binary measurement matrices (2020)
  9. Wang, Gang; Niu, Min-Yao; Fu, Fang-Wei: Deterministic construction of compressed sensing matrices from constant dimension codes (2020)
  10. Wang, Jun; Wang, Xing Tao: Sparse signal reconstruction via the approximations of (\ell_0) quasinorm (2020)
  11. Zhao, Yun-Bin: Optimal (k)-thresholding algorithms for sparse optimization problems (2020)
  12. Arridge, Simon; Maass, Peter; Öktem, Ozan; Schönlieb, Carola-Bibiane: Solving inverse problems using data-driven models (2019)
  13. Barbara, Abdessamad; Jourani, Abderrahim; Vaiter, Samuel: Maximal solutions of sparse analysis regularization (2019)
  14. Calderbank, Robert; Hansen, Anders; Roman, Bogdan; Thesing, Laura: On reconstructing functions from binary measurements (2019)
  15. Choe, Chol-Guk; Rim, Myong-Gil; Ryang, Ji-Song: Sparse recovery with general frame via general-dual-based analysis Dantzig selector (2019)
  16. Feng, Joe-Mei; Krahmer, Felix; Saab, Rayan: Quantized compressed sensing for random circulant matrices (2019)
  17. Fukshansky, Lenny; Needell, Deanna; Sudakov, Benny: An algebraic perspective on integer sparse recovery (2019)
  18. Geng, Pengbo; Chen, Wengu; Ge, Huanmin: Perturbation analysis of orthogonal least squares (2019)
  19. Geppert, Jakob; Krahmer, Felix; Stöger, Dominik: Sparse power factorization: balancing peakiness and sample complexity (2019)
  20. Kreuzer, Wolfgang: Using B-spline frames to represent solutions of acoustics scattering problems (2019)

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