An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery
Joint sparse recovery (JSR) in compressed sensing simultaneously recovers sparse signals with a common sparsity structure from their multiple measurement vectors obtained through a common sensing matrix. In this paper, we present an Armijo-type hard thresholding (AHT) algorithm for joint sparse reco...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9483898/ |
| _version_ | 1798003395867443200 |
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| author | Lili Pan Xunzhi Zhu |
| author_facet | Lili Pan Xunzhi Zhu |
| author_sort | Lili Pan |
| collection | DOAJ |
| description | Joint sparse recovery (JSR) in compressed sensing simultaneously recovers sparse signals with a common sparsity structure from their multiple measurement vectors obtained through a common sensing matrix. In this paper, we present an Armijo-type hard thresholding (AHT) algorithm for joint sparse recovery. Under the restricted isometry property (RIP), we show that the AHT can converge to a local minimizer of the optimization problem for JSR. Furthermore, we compute the AHT convergence rate with the above conditions. Numerical experiments show the good performance of the new algorithm for JSR. |
| first_indexed | 2024-04-11T12:06:54Z |
| format | Article |
| id | doaj.art-017776d68277488ab98f85f584801a20 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T12:06:54Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-017776d68277488ab98f85f584801a202022-12-22T04:24:41ZengIEEEIEEE Access2169-35362021-01-01910176510177210.1109/ACCESS.2021.30972169483898An Armijo-Type Hard Thresholding Algorithm for Joint Sparse RecoveryLili Pan0https://orcid.org/0000-0001-7061-8197Xunzhi Zhu1School of Mathematics and Statistics, Shandong University of Technology, Zibo, ChinaSchool of Mathematics and Statistics, Shandong University of Technology, Zibo, ChinaJoint sparse recovery (JSR) in compressed sensing simultaneously recovers sparse signals with a common sparsity structure from their multiple measurement vectors obtained through a common sensing matrix. In this paper, we present an Armijo-type hard thresholding (AHT) algorithm for joint sparse recovery. Under the restricted isometry property (RIP), we show that the AHT can converge to a local minimizer of the optimization problem for JSR. Furthermore, we compute the AHT convergence rate with the above conditions. Numerical experiments show the good performance of the new algorithm for JSR.https://ieeexplore.ieee.org/document/9483898/Joint sparse recoveryArmijo-type hard thresholdingconvergencenumerical experiment |
| spellingShingle | Lili Pan Xunzhi Zhu An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery IEEE Access Joint sparse recovery Armijo-type hard thresholding convergence numerical experiment |
| title | An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery |
| title_full | An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery |
| title_fullStr | An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery |
| title_full_unstemmed | An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery |
| title_short | An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery |
| title_sort | armijo type hard thresholding algorithm for joint sparse recovery |
| topic | Joint sparse recovery Armijo-type hard thresholding convergence numerical experiment |
| url | https://ieeexplore.ieee.org/document/9483898/ |
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