New hybrid conjugate gradient method as a convex combination of PRP and RMIL+ methods

被引：0
|
作者：
Hadji, Ghania [1 ,2 ]
Laskri, Yamina [3 ]
Bechouat, Tahar [2 ]
Benzine, Rachid [4 ]
机构：
[1] Badji Mokhtar Univ, Dept Math, Fac Sci, BP 12, Annaba 23000, Algeria
[2] Mohamed Cherif Messaadia Univ, Fac Sci & Technol, Dept Math & Informat, POB 1553, Souk Ahras 41000, Algeria
[3] Badji Mokhtar Univ, Fac Sci, Dept Math, ESTI, BP 12, Annaba 23000, Algeria
[4] Badji Mokhtar Univ, Fac Sci, Dept Math, Lab LANOS, BP 12, Annaba 23000, Algeria
来源：
STUDIA UNIVERSITATIS BABES-BOLYAI MATHEMATICA | 2024年 / 69卷 / 02期
关键词：
Unconstrained optimization; hybrid conjugate gradient method; line search; descent property; global convergence; GLOBAL CONVERGENCE; DESCENT; COEFFICIENTS; ALGORITHM;
D O I：
10.24193/subbmath.2024.2.14
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The Conjugate Gradient (CG) method is a powerful iterative approach for solving large-scale minimization problems, characterized by its simplicity, low computation cost and good convergence. In this paper, a new hybrid conjugate gradient HLB method (HLB: Hadji-Laskri-Bechouat) is proposed and analysed for unconstrained optimization. We compute the parameter beta HLB k as a convex combination of the Polak-Ribiere-Polyak ( beta(P RP )(k))and the Mohd Rivaie-Mustafa Mamat and Abdelrhaman Abashar (beta(k) (RMIL +) ) i.e. beta (HLB)(k ) = (1 - theta (k) ) beta(P RP)(k) + theta (k) beta(k) (RMIL +) . By comparing numerically CGHLB with PRP and RMIL+ and by using the Dolan and More CPU performance, we deduce that CGHLB is more efficient.
引用
收藏
页码：457 / 468
页数：12
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