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 208 articles )

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  1. Cai, Yun: Weighted (l_p- l_1) minimization methods for block sparse recovery and rank minimization (2021)
  2. Xiao-Qing, Wang; Hao, Zhang; Yu-Jie, Sun; Xing-Yuan, Wang: A plaintext-related image encryption algorithm based on compressive sensing and a novel hyperchaotic system (2021)
  3. Zhu, Wenxing; Huang, Zilin; Chen, Jianli; Peng, Zheng: Iteratively weighted thresholding homotopy method for the sparse solution of underdetermined linear equations (2021)
  4. Blanchard, Jeffrey D.; Leedy, Caleb; Wu, Yimin: On rank awareness, thresholding, and MUSIC for joint sparse recovery (2020)
  5. Casazza, Peter G.; Chen, Xuemei; Lynch, Richard G.: Preserving injectivity under subgaussian mappings and its application to compressed sensing (2020)
  6. Cheng, Wanyou; Chen, Zixin; Hu, Qingjie: An active set Barzilar-Borwein algorithm for (l_0) regularized optimization (2020)
  7. Cui, Kaiyan; Song, Zhanjie; Han, Ningning: Fast thresholding algorithms with feedbacks and partially known support for compressed sensing (2020)
  8. Dupé, François-Xavier; Anthoine, Sandrine: Generalized greedy alternatives (2020)
  9. Haddou, M.; Migot, T.: A smoothing method for sparse optimization over convex sets (2020)
  10. Li, Song; Lin, Junhong; Liu, Dekai; Sun, Wenchang: Iterative hard thresholding for compressed data separation (2020)
  11. Mousavi, Ahmad; Rezaee, Mehdi; Ayanzadeh, Ramin: A survey on compressive sensing: classical results and recent advancements (2020)
  12. Rebollo-Neira, Laura; Rozložník, Miroslav; Sasmal, Pradip: Analysis of the self projected matching pursuit algorithm (2020)
  13. Soubies, Emmanuel; Blanc-Féraud, Laure; Aubert, Gilles: New insights on the optimality conditions of the (\ell_2-\ell_0) minimization problem (2020)
  14. Tirer, Tom; Giryes, Raja: Generalizing CoSaMP to signals from a union of low dimensional linear subspaces (2020)
  15. Tong, Fenghua; Li, Lixiang; Peng, Haipeng; Yang, Yixian: An effective algorithm for the spark of sparse binary measurement matrices (2020)
  16. Wang, Gang; Niu, Min-Yao; Fu, Fang-Wei: Deterministic construction of compressed sensing matrices from constant dimension codes (2020)
  17. Wang, Jinming; Xu, Zhenyu; Wang, Zhangquan; Xu, Sen; Jiang, Jun: Rapid compressed sensing reconstruction: a semi-tensor product approach (2020)
  18. Wang, Jun; Wang, Xing Tao: Sparse signal reconstruction via the approximations of (\ell_0) quasinorm (2020)
  19. Zhao, Yun-Bin: Optimal (k)-thresholding algorithms for sparse optimization problems (2020)
  20. Arridge, Simon; Maass, Peter; Öktem, Ozan; Schönlieb, Carola-Bibiane: Solving inverse problems using data-driven models (2019)

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