New hyrid conjugate gradient method as a convex combination of HZ and CD methods

被引：0
|
作者：
Hamdi, Amira [1 ]
Sellami, Badreddine [1 ]
Belloufi, Mohammed [1 ]
机构：
[1] Mohamed Cherif Messaadia Univ, Lab Informat & Math LiM, Souk Ahras, Algeria
来源：
ASIAN-EUROPEAN JOURNAL OF MATHEMATICS | 2021年 / 14卷 / 10期
关键词：
Unconstrained optimization; hybrid conjugate gradient method; global convergence; numerical results; UNCONSTRAINED OPTIMIZATION; ALGORITHM;
D O I：
10.1142/S1793557121501874
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, a new hybrid conjugate gradient algorithm is proposed for solving unconstrained optimization problems, the conjugate gradient parameter beta(k) is computed as a convex combination of beta(HZ)(k) and beta(CD)(k). Under the wolfe line search, we prove the sufficient descent and the global convergence. Numerical results are reported to show the effectiveness of our procedure.
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页数：10
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