Abstract
In this paper, we propose a modified proximal gradient method for solving a class of nonsmooth convex optimization problems, which arise in many contemporary statistical and signal processing applications. The proposed method adopts a new scheme to construct the descent direction based on the proximal gradient method. It is proven that the modified proximal gradient method is Q-linearly convergent without the assumption of the strong convexity of the objective function. Some numerical experiments have been conducted to evaluate the proposed method eventually.
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This work is supported by the National Natural Science Foundation of China (No. 61179033).
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Li, YY., Zhang, HB. & Li, F. A Modified Proximal Gradient Method for a Family of Nonsmooth Convex Optimization Problems. J. Oper. Res. Soc. China 5, 391–403 (2017). https://doi.org/10.1007/s40305-017-0155-5
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DOI: https://doi.org/10.1007/s40305-017-0155-5