1. Neural Network for Sparse Reconstruction.
- Author
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Qingfa Li, Yaqiu Liu, and Liangkuan Zhu
- Subjects
- *
ARTIFICIAL neural networks , *PROBLEM solving , *MATHEMATICAL optimization , *SET-valued maps , *ALGORITHMS , *APPROXIMATION theory - Abstract
We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an importantmethod for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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