5 August 2015 Fast resolving of the nonconvex optimization with gradient projection
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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.
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Fangfang Shen, Fangfang Shen, Guangming Shi, Guangming Shi, Guanghui Zhao, Guanghui Zhao, Yi Niu, 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); doi: 10.1117/12.2193290; https://doi.org/10.1117/12.2193290

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