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 132 articles )

Showing results 1 to 20 of 132.
Sorted by year (citations)

1 2 3 ... 5 6 7 next

  1. Brugiapaglia, Simone; Nobile, Fabio; Micheletti, Stefano; Perotto, Simona: A theoretical study of compressed solving for advection-diffusion-reaction problems (2018)
  2. Lai, Chun-Kit; Li, Shidong; Mondo, Daniel: Spark-level sparsity and the $\ell_1$ tail minimization (2018)
  3. Mathelin, Lionel; Kasper, Kévin; Abou-Kandil, Hisham: Observable dictionary learning for high-dimensional statistical inference (2018)
  4. Saab, Rayan; Wang, Rongrong; Yılmaz, Özgür: Quantization of compressive samples with stable and robust recovery (2018)
  5. Adamo, Alessandro; Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi: Sparse decomposition by iterating Lipschitzian-type mappings (2017)
  6. Andersen, Michael Riis; Vehtari, Aki; Winther, Ole; Hansen, Lars Kai: Bayesian inference for spatio-temporal spike-and-slab priors (2017)
  7. Bouwmans, Thierry; Sobral, Andrews; Javed, Sajid; Jung, Soon Ki; Zahzah, El-Hadi: Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset (2017)
  8. Eghbali, Reza; Fazel, Maryam: Decomposable norm minimization with proximal-gradient homotopy algorithm (2017)
  9. Fu, Yusheng; Liu, Shanshan; Ren, Chunhui: Adaptive step-size matching pursuit algorithm for practical sparse reconstruction (2017)
  10. Haviv, Ishay; Regev, Oded: The restricted isometry property of subsampled Fourier matrices (2017)
  11. Hu, Jun; Zhang, Shudao: Global sensitivity analysis based on high-dimensional sparse surrogate construction (2017)
  12. Iwen, Mark; Viswanathan, Aditya; Wang, Yang: Robust sparse phase retrieval made easy (2017)
  13. Jie, Yingmo; Guo, Cheng; Fu, Zhangjie: Newly deterministic construction of compressed sensing matrices via singular linear spaces over finite fields (2017)
  14. Krol, Jakub; Wynn, Andrew: Dynamic reconstruction and data reconstruction for subsampled or irregularly sampled data (2017)
  15. Needell, Deanna; Saab, Rayan; Woolf, Tina: Weighted $\ell_1$-minimization for sparse recovery under arbitrary prior information (2017)
  16. Rauhut, Holger; Schwab, Christoph: Compressive sensing Petrov-Galerkin approximation of high-dimensional parametric operator equations (2017)
  17. Satpathi, Siddhartha; Chakraborty, Mrityunjoy: On the number of iterations for convergence of CoSaMP and subspace pursuit algorithms (2017)
  18. Stanković, Ljubiša; Daković, Miloš; Vujović, Stefan: Reconstruction of sparse signals in impulsive disturbance environments (2017)
  19. Wang, Dan; Zhang, Zhuhong: Generalized sparse recovery model and its neural dynamical optimization method for compressed sensing (2017)
  20. Wang, Yang; Xiang, Xiuqiao; Zhou, Shunping; Luo, Zhongwen; Fang, Fang: Compressed sensing based on trust region method (2017)

1 2 3 ... 5 6 7 next