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Proceedings Paper

Fast resolving of the nonconvex optimization with gradient projection
Author(s): Fangfang Shen; Guangming Shi; Guanghui Zhao; Yi Niu
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Paper Abstract

In this paper, we propose a novel algorithm for processing the non-convex l0≤p≤1 semi-norm minimization model under the gradient descent framework. Since the proposed algorithm only involves some matrix-vector products, it is easy to implement fast implicit operation and make it possible to take use of the advantage of l0≤p≤1 semi-norm based model practically in large-scale applications which is a hard task for common procedure for l0≤p≤1 semi-norm optimization such as FOCUSS. The simulation of image compression and reconstruction shows the super performance of the proposed algorithm.

Paper Details

Date Published: 5 August 2015
PDF: 4 pages
Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 962218 (5 August 2015); doi: 10.1117/12.2193290
Show Author Affiliations
Fangfang Shen, Xidian Univ. (China)
Guangming Shi, Xidian Univ. (China)
Guanghui Zhao, Xidian Univ. (China)
Yi Niu, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 9622:
2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Guangming Shi; Xuelong Li; Bormin Huang, Editor(s)

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