A Modified Self-Adaptive Conjugate Gradient Method for Solving Convex Constrained Monotone Nonlinear Equations for Signal Recovery Problems

被引：21
|
作者：
Abubakar, Auwal Bala [1 ,2 ]
Kumam, Poom [1 ,3 ,4 ]
Awwal, Aliyu Muhammed [1 ,5 ]
Thounthong, Phatiphat [6 ]
机构：
[1] KMUTT, Fac Sci, Dept Math, KMUTTFixed Point Res Lab, 126 Pracha Uthit Rd, Bangkok 10140, Thailand
[2] Bayero Univ, Fac Phys Sci, Dept Math Sci, Kano 700241, Nigeria
[3] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung 40402, Taiwan
[4] KMUTT, Ctr Excellence Theoret & Computat Sci TaCS CoE, Sci Lab Bldg,126 Pracha Uthit Rd, Bangkok 10140, Thailand
[5] Gombe State Univ, Fac Sci, Dept Math, Gombe 760214, Nigeria
[6] King Mongkuts Univ Technol North Bangkok, Fac Tech Educ, Dept Teacher Training Elect Engn, Renewable Energy Res Ctr, 1518 Pracharat 1 Rd, Bangkok 10800, Thailand
来源：
MATHEMATICS | 2019年 / 7卷 / 08期
关键词：
non-linear equations; conjugate gradient method; projection method; convex constraints; signal reconstruction problem; SUFFICIENT DESCENT PROPERTY; PROJECTION METHOD; SYSTEMS; ALGORITHM; SPARSE;
D O I：
10.3390/math7080693
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this article, we propose a modified self-adaptive conjugate gradient algorithm for handling nonlinear monotone equations with the constraints being convex. Under some nice conditions, the global convergence of the method was established. Numerical examples reported show that the method is promising and efficient for solving monotone nonlinear equations. In addition, we applied the proposed algorithm to solve sparse signal reconstruction problems.
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收藏
页数：24
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