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An iteratively approximated gradient projection algorithm for sparse signal reconstruction
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DOI：10.1016/j.amc.2013.10.063.png)
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Abstract
En 中文
In this paper we developed an iteratively approximated gradient projection algorithm for l(1)-minimization problems arising from sparse signal reconstruction in compressive sensing. By introducing a relaxed variable, the noisy problem can be transformed into the problem with equality constraints. The nonsmooth l(1) term was tackled by variable-splitting techniques. Thus the problem was transformed into a quadratic programming problem. All linear variables in the objective function were imposed on l(2) regularization. Based on ideas of quasi-Lagrangian functions and partial duality, a reduced quadratic programming problem can be obtained iteratively. At each iteration, we applied gradient projection methods with approximated gradients to get the next iterates. The computational experiments show the proposed method is very effective. (c) 2013 Elsevier Inc. All rights reserved.
Keywords:
Sparse signal reconstruction
Quadratic programming
Gradient projection methods
Nonnegative constraints
Quasi-Lagrangian function
Partial duality
Journal
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3.4
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2.3W
Citations:
3.3W