Nonmonotone Adaptive Barzilai-Borwein Gradient Algorithm for Compressed Sensing
We study a nonmonotone adaptive Barzilai-Borwein gradient algorithm for l1-norm minimization problems arising from compressed sensing. At each iteration, the generated search direction enjoys descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneous...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/410104 |
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| Summary: | We study a nonmonotone adaptive Barzilai-Borwein gradient algorithm for l1-norm minimization problems arising from compressed sensing. At each iteration, the generated search direction enjoys descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneously taking the favorable structure of the l1-norm. Under some suitable conditions, its global convergence result could be established. Numerical results illustrate that the proposed method is promising and competitive with the existing algorithms NBBL1 and TwIST. |
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| ISSN: | 1085-3375 1687-0409 |