Quantile-based random sparse Kaczmarz for corrupted and noisy linear systems
Author:
Funder
National Natural Science Foundation of China
Natural Science Fund of Hunan for Excellent Youth
Fund for NUDT Young Innovator Awards
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11075-024-01844-6.pdf
Reference40 articles.
1. Lorenz, D.A., Wenger, S., Schöpfer, F., Magnor, M.: A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 1347–1351 (2014). https://doi.org/10.1109/ICIP.2014.7025269. IEEE
2. Tan, Y.S., Vershynin, R.: Phase retrieval via randomized Kaczmarz: theoretical guarantees. Inf. Inference: J. IMA 8(1), 97–123 (2019). https://doi.org/10.1093/imaiai/iay005
3. Xian, Y., Liu, H.G., Tai, X.C., Wang, Y.: Randomized Kaczmarz Method for Single-Particle X-Ray Image Phase Retrieval[M]//Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision. Cham: Springer International Publishing, pp. 1–16 (2022). https://doi.org/10.1007/978-3-030-98661-2112
4. Römer, P., Filbir, F., Krahmer, F.: On the randomized Kaczmarz algorithm for phase retrieval. In: 2021 55th Asilomar Conference on Signals, Systems, and Computers, pp. 847–851 (2021). https://doi.org/10.1109/IEEECONF53345.2021.9723291. IEEE
5. Chen, X., Qin, J.: Regularized Kaczmarz algorithms for tensor recovery. SIAM J. Imaging Sci. 14(4), 1439–1471 (2021). https://doi.org/10.1137/21M1398562
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