A Hybrid Forward-Backward Algorithm and Its Optimization Application

被引：1
|
作者：
Liu, Liya [1 ]
Qin, Xiaolong [2 ]
Yao, Jen-Chih [3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China
[2] Hangzhou Normal Univ, Dept Math, Hangzhou 31121, Peoples R China
[3] China Med Univ Hosp, Res Ctr Interneural Comp, Taichung 40447, Taiwan
来源：
MATHEMATICS | 2020年 / 8卷 / 03期
关键词：
forward-backward method; hybrid steepest decent method; inertial extrapolation; maximally monotone; strong convergence; VARIATIONAL-INEQUALITIES; MONOTONE-OPERATORS; COMMON SOLUTIONS; DESCENT METHOD; FIXED-POINTS; CONVERGENCE; APPROXIMATION; SYSTEMS;
D O I：
10.3390/math8030447
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, we study a hybrid forward-backward algorithm for sparse reconstruction. Our algorithm involves descent, splitting and inertial ideas. Under suitable conditions on the algorithm parameters, we establish a strong convergence solution theorem in the framework of Hilbert spaces. Numerical experiments are also provided to illustrate the application in the field of signal processing.
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页数：16
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