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

ZHOU Jun; HUANG Wei

doi:10.21656/1000-0887.390154

Volume 40 Issue 2

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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

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

doi: 10.21656/1000-0887.390154

ZHOU Jun,
HUANG Wei

School of Mathematics, Hefei University of Technology, Hefei 230009, P.R.China

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

Abstract

Abstract
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/l_q(0
- compressed sensing,
- block-RIP,
- block-sparse signal,
- mixed l₂/l_q norm minimization

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References

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