k-Sparse Vector Recovery via $$\ell _1-\alpha \ell _2$$ Local Minimization-Reference-Cited by-同舟云学术

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k-Sparse Vector Recovery via $$\ell _1-\alpha \ell _2$$ Local Minimization

Published:2024-02-15 Issue:1 Volume:201 Page:75-102
ISSN:0022-3239
Container-title:Journal of Optimization Theory and Applications
language:en
Short-container-title:J Optim Theory Appl

Author:

Xie Shaohua,Li Jia^ORCID,Liang Kaihao

Funder

National Key R &D Program of China

Guangdong-Hong Kong-Macau Applied Math Center grant

National Natural Science Foundation of China

NSF of Guangdong under grant

the Science and Technology Planning Project of Guangdong under grant

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10957-024-02380-y.pdf

Reference33 articles.

1. Bi, N., Tan, J., Tang, W.: A new sufficient condition for sparse vector recovery via $${\ell }_1 - {\ell }_2$$ local minimization. Anal. Appl. 19, 1019–1031 (2021)

2. Bi, N., Tang, W.: A necessary and sufficient condition for sparse vector recovery via $${\ell }_2$$ minimization. Appl. Comput. Harmonic Anal. 56, 337–350 (2022)

3. Cai, T., Wang, L., Xu, G.: New bounds for restricted isometry constants. IEEE Press 56, 4388–4394 (2010)

4. Cai, T., Zhang, A.: Sparse representation of a polytope and recovery of sparse signals and low-rank matrices. IEEE Trans. Inf. Theory 60, 122–132 (2014)

5. Candes, E.: The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematiques 346, 589–592 (2008)

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