Two sufficient descent spectral conjugate gradient algorithms for unconstrained optimization with application (Jun, 10.1007/s11081-024-09899-z, 2024)

被引：0

作者：

Ibrahim, Sulaiman Mohammed ^{[1
,2
]}

Salihu, Nasiru ^{[3
,4
]}

机构：

[1] Univ Utara Malaysia, Sch Quantitat Sci, Inst Strateg Ind Decis Modelling, Sintok 06010, Kedah, Malaysia

[2] Sohar Univ, Fac Educ & Arts, Sohar 311, Oman

[3] King Mongkuts Univ Technol Thonburi KMUTT, Fixed Point Res Lab, Fixed Point Theory & Applicat Res Grp,Fac Sci, Ctr Excellence Theoret & Computat Sci TaCS CoE, Bangkok 10140, Thailand

[4] Modibbo Adama Univ, Fac Sci, Dept Math, Yola 652105, Nigeria

来源：

OPTIMIZATION AND ENGINEERING | 2025年 / 26卷 / 01期

关键词：

Conjugate gradient; Convergence analysis; Image restoration; Large scale optimization; Spectral parameter;

D O I：

10.1007/s11081-024-09905-4

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This study introduces a new modification of the conjugate gradient (CG) method (IMRMIL). Additionally, two spectral CG algorithms (SCG1 and SCG2) are constructed for unconstrained optimization functions with practical applications. Unlike the modified search methods, the search directions in these algorithms satisfy the important descent property without imposing additional restrictions and are independent of the line search. The global convergence of the new algorithms is established under suitable Wolfe line search conditions by assuming that the gradient g(x) of a continuously differentiable function f is Lipschitz continuous. Numerical computations on both optimization functions and image restoration problems demonstrate the effectiveness of the proposed algorithms.

引用

页码：681 / 683

页数：3