A Reduced Half Thresholding Algorithm-Reference-Cited by-同舟云学术

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A Reduced Half Thresholding Algorithm

Published:2023-07-06 Issue:2 Volume:96 Page:
ISSN:0885-7474
Container-title:Journal of Scientific Computing
language:en
Short-container-title:J Sci Comput

Author:

Liang Xiubo,Wang Guoqiang,Yu Bo^ORCID

Funder

National Natural Science Foundation of China

Postdoctoral Research Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,General Engineering,Theoretical Computer Science,Software,Applied Mathematics,Computational Mathematics,Numerical Analysis

Link

https://link.springer.com/content/pdf/10.1007/s10915-023-02250-1.pdf

Reference45 articles.

1. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2, 183–202 (2009)

2. Large-scale regression with non-convex loss and penalty: Buccini, A., De la Cruz Cabrera, O., Donatelli et al., M. Appl. Numer. Math. 157, 590–601 (2020)

3. Candes, E.J., Randall, P.A.: Highly robust error correction byconvex programming. IEEE Trans. Inf. Theory 54, 2829–2840 (2008)

4. Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9, 717–772 (2009)

5. Candès, E.J., Romberg, J.: Quantitative robust uncertainty principles and optimally sparse decompositions. Found. Comput. Math. 6, 227–254 (2006)

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