ZHOU Jun, HUANG Wei. Improved Conditions for Block-Sparse Signal Recovery via the Non-Convex Optimization Model[J]. Applied Mathematics and Mechanics, 2019, 40(2): 167-180. doi: 10.21656/1000-0887.390154
Citation: ZHOU Jun, HUANG Wei. Improved Conditions for Block-Sparse Signal Recovery via the Non-Convex Optimization Model[J]. Applied Mathematics and Mechanics, 2019, 40(2): 167-180. doi: 10.21656/1000-0887.390154

Improved Conditions for Block-Sparse Signal Recovery via the Non-Convex Optimization Model

doi: 10.21656/1000-0887.390154
Funds:  The Major Research Plan of the National Natural Science Foundation of China(91538112); The National Science Fund for Young Scholars of China(11201450)
  • Received Date: 2018-05-25
  • Rev Recd Date: 2018-12-04
  • Publish Date: 2019-02-01
  • Compressed sensing (CS) is a newly developed theoretical framework for information acquisition and processing, which shows that sparse signals can be recovered exactly from far less samples than those required by the classical ShannonNyquist theorem. The blocksparse signal recovery algorithm under the compressed sensing framework was mainly studied, and a class of improved exact recovery conditions based on the block restricted isometry property (RIP) were established in the noiseless cases via the mixed l2/lq(0
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