Some improved Dai-Yuan conjugate gradient methods for large-scale unconstrained optimization problems

被引：1
|
作者：
Bojari, S. [1 ]
Eslahchi, M. R. [2 ]
机构：
[1] Hamedan Univ Technol, Dept Basic Sci, Hamadan 65155579, Hamadan, Iran
[2] Tarbiat Modares Univ, Fac Math Sci, Dept Appl Math, Tehran 14115134, Iran
来源：
JOURNAL OF APPLIED MATHEMATICS AND COMPUTING | 2023年 / 69卷 / 06期
关键词：
Optimization; Large-scale problems; Conjugate gradient method; Weak-Wolfe-Powell line search technique; Dai-Yuan method; QUASI-NEWTON METHODS; GLOBAL CONVERGENCE; ALGORITHM; PERFORMANCE; DESCENT; FAMILY;
D O I：
10.1007/s12190-023-01918-z
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we introduce some modifications of the classic conjugate gradient method Dai-Yuan, to solve large-scale unconstrained optimization problems. Our modifications are based on four improved secant conditions. We indicate that the presented methods inherit the appropriate global convergence property of the Dai-Yuan method. Furthermore, we illustrate the amazing numerical behavior of these modifications in two sets of numerical experiments.
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页码：4213 / 4228
页数：16
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