Global Convergence Analysis of a New Nonlinear Conjugate Gradient Coefficient with Strong Wolfe Line Search

被引:0
作者
Abdelrahman, Awad [1 ]
Mamat, Mustafa [2 ]
Rivaie, Mohd [3 ]
Omer, Osman [1 ]
机构
[1] UMT, Dept Math, Fac Sci & Technol, Kuala Terengganu, Terengganu, Malaysia
[2] Univ Sultan Zainal Abidin, Fac Informat & Comp, Computat & Appl Math, Besut 22200, Malaysia
[3] Univ Teknol MARA Terenggan, Dept Comp Sci & Math, Dungun, Terenggan, Malaysia
来源
INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2014 (ICOMEIA 2014) | 2015年 / 1660卷
关键词
Unconstrained optimization; conjugate gradient method; sufficient descent property; global convergence;
D O I
10.1063/1.4915647
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Nonlinear conjugate gradient (CG) methods are the most important method for solving large-scale unconstrained optimization problems. Many studies and modifications have been conducted recently to improve this method. In this paper, a new class of conjugate gradient coefficients (beta(k)) with a new parameter m = parallel to g(k)parallel to/parallel to g(k-1)parallel to that possess global convergence properties is presented. The global convergence and sufficient decent property result is established using inexact line searches to determine the (alpha(k) > 0) is a step size of CG methods. Numerical result shows that the new formula is superior and more efficient when compared to other CG coefficients.
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页数:7
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