Fast resolving of the nonconvex optimization with gradient projection

Fangfang Shen; Guangming Shi; Guanghui Zhao; Yi Niu

doi:10.1117/12.2193290

5 August 2015 Fast resolving of the nonconvex optimization with gradient projection

Fangfang Shen, Guangming Shi, Guanghui Zhao, Yi Niu

Proceedings Volume 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 962218 (2015) https://doi.org/10.1117/12.2193290
Event: International Conference on Optical Instruments and Technology 2015, 2015, Beijing, China

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.

Citation Download Citation

Fangfang Shen, Guangming Shi, Guanghui Zhao, and Yi Niu "Fast resolving of the nonconvex optimization with gradient projection", Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 962218 (5 August 2015); https://doi.org/10.1117/12.2193290

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