International Society of Science and Applied Technologies |
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A Modified Proximal Gradient Method for a Class of Sparse Optimization Problems | ||||
Author | Yingyi Li
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Co-Author(s) | Haibin Zhang
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Abstract | In this paper, we propose a modified proximal gradient method for solving a class of sparse 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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Keywords | Nonsmooth convex optimization, Modified proximal gradient method, Q-linear convergence | |||
Article #: 23-311 |
August 3-5, 2017 - Chicago, Illinois, U.S.A. |