A Modified Proximal Gradient Method for a Class of Sparse Optimization Problems  
Author Yingyi Li
Co-Author(s) Haibin Zhang
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.
Keywords Nonsmooth convex optimization, Modified proximal gradient method, Q-linear convergence
    Article #:  23-311
Proceedings of the 23rd ISSAT International Conference on Reliability and Quality in Design
August 3-5, 2017 - Chicago, Illinois, U.S.A.