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 218 articles )

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  1. Cai, Yun: Weighted (l_p- l_1) minimization methods for block sparse recovery and rank minimization (2021)
  2. Choi, Bosu; Iwen, Mark A.; Krahmer, Felix: Sparse harmonic transforms: a new class of sublinear-time algorithms for learning functions of many variables (2021)
  3. Choi, Bosu; Iwen, Mark; Volkmer, Toni: Sparse harmonic transforms. II: Best (s)-term approximation guarantees for bounded orthonormal product bases in sublinear-time (2021)
  4. De Loera, Jesús A.; Haddock, Jamie; Ma, Anna; Needell, Deanna: Data-driven algorithm selection and tuning in optimization and signal processing (2021)
  5. Huang, Jian; Jiao, Yuling; Jin, Bangti; Liu, Jin; Lu, Xiliang; Yang, Can: A unified primal dual active set algorithm for nonconvex sparse recovery (2021)
  6. Lüthen, Nora; Marelli, Stefano; Sudret, Bruno: Sparse polynomial chaos expansions: literature survey and benchmark (2021)
  7. Qin, Jing; Li, Shuang; Needell, Deanna; Ma, Anna; Grotheer, Rachel; Huang, Chenxi; Durgin, Natalie: Stochastic greedy algorithms for multiple measurement vectors (2021)
  8. Wang, Rui; Xiu, Naihua; Zhou, Shenglong: An extended Newton-type algorithm for (\ell_2)-regularized sparse logistic regression and its efficiency for classifying large-scale datasets (2021)
  9. Wen, Jinming; Li, Haifeng: Binary sparse signal recovery with binary matching pursuit (2021)
  10. 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)
  11. Zhu, Wenxing; Huang, Zilin; Chen, Jianli; Peng, Zheng: Iteratively weighted thresholding homotopy method for the sparse solution of underdetermined linear equations (2021)
  12. Blanchard, Jeffrey D.; Leedy, Caleb; Wu, Yimin: On rank awareness, thresholding, and MUSIC for joint sparse recovery (2020)
  13. Casazza, Peter G.; Chen, Xuemei; Lynch, Richard G.: Preserving injectivity under subgaussian mappings and its application to compressed sensing (2020)
  14. Cheng, Wanyou; Chen, Zixin; Hu, Qingjie: An active set Barzilar-Borwein algorithm for (l_0) regularized optimization (2020)
  15. Cui, Kaiyan; Song, Zhanjie; Han, Ningning: Fast thresholding algorithms with feedbacks and partially known support for compressed sensing (2020)
  16. Dupé, François-Xavier; Anthoine, Sandrine: Generalized greedy alternatives (2020)
  17. Haddou, M.; Migot, T.: A smoothing method for sparse optimization over convex sets (2020)
  18. Li, Song; Lin, Junhong; Liu, Dekai; Sun, Wenchang: Iterative hard thresholding for compressed data separation (2020)
  19. Mousavi, Ahmad; Rezaee, Mehdi; Ayanzadeh, Ramin: A survey on compressive sensing: classical results and recent advancements (2020)
  20. Rebollo-Neira, Laura; Rozložník, Miroslav; Sasmal, Pradip: Analysis of the self projected matching pursuit algorithm (2020)

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