A conjugate gradient algorithm and its applications in image restoration

被引：35
作者：
Cao, Junyue [1 ,2 ]
Wu, Jinzhao [3 ,4 ]
机构：
[1] Chinese Acad Sci, Chengdu Inst Comp Applicat, Chengdu, Peoples R China
[2] Univ Chinese Acad Sci, Beijing, Peoples R China
[3] Guangxi Univ Nationalities, Guangxi Key Lab Hybrid Computat & IC Design Anal, Nanning 530006, Peoples R China
[4] Guangxi Univ, Sch Comp & Elect Informat, Nanning 530004, Peoples R China
来源：
APPLIED NUMERICAL MATHEMATICS | 2020年 / 152卷 / 152期
基金：
中国国家自然科学基金;
关键词：
Conjugate gradient; Impulse noise; Global convergence; Inexact line search; Nonconvex functions; CONVERGENCE PROPERTIES; GLOBAL CONVERGENCE; DESCENT; MINIMIZATION;
D O I：
10.1016/j.apnum.2019.12.002
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, a nonlinear conjugate gradient algorithm is presented. This given algorithm possesses the following properties: (i) the sufficient descent property is satisfied; (ii) the trust region feature holds; (iii) the global convergence is proved for nonconvex functions; (iv) the applications for image restoration problem are done to show the performance of the proposed algorithm. Notice that the properties (i) and (ii) will be obtained without other special conditions. (C) 2019 IMACS. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：243 / 252
页数：10
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