A Modified Proximal Gradient Method for a Class of Sparse Optimization Problems

被引：0
|
作者：
Li, Yingyi [1 ,2 ]
Zhang, Haibin [1 ]
Zhang, Rong [3 ]
机构：
[1] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
[2] Hebei Finance Univ, Dept Basic Courses, Baoding 071051, Peoples R China
[3] Case Western Reserve Univ, Weatherhead Sch Management, Cleveland, OH 44106 USA
来源：
INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING | 2018年 / 25卷 / 06期
关键词：
Nonsmooth convex optimization; signal processing; modified proximal gradient method; Q-linear convergence;
D O I：
10.1142/S0218539318500274
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this paper, we propose a modified proximal gradient method for a class of sparse optimization problems. which arise in many contemporary statistical and signal processing applications. The new method uses 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. Numerical experiments have been conducted to evaluate the proposed method.
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页数：13
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