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An efficient hybrid conjugate gradient method for unconstrained optimization.

Authors :
Ibrahim, Abdulkarim Hassan
Kumam, Poom
Kamandi, Ahmad
Abubakar, Auwal Bala
Source :
Optimization Methods & Software; Aug2022, Vol. 37 Issue 4, p1370-1383, 14p
Publication Year :
2022

Abstract

In this paper, we propose a hybrid conjugate gradient method for unconstrained optimization, obtained by a convex combination of the LS and KMD conjugate gradient parameters. A favourite property of the proposed method is that the search direction satisfies the Dai–Liao conjugacy condition and the quasi-Newton direction. In addition, this property does not depend on the line search. Under a modified strong Wolfe line search, we establish the global convergence of the method. Numerical comparison using a set of 109 unconstrained optimization test problems from the CUTEst library show that the proposed method outperforms the Liu–Storey and Hager–Zhang conjugate gradient methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10556788
Volume :
37
Issue :
4
Database :
Complementary Index
Journal :
Optimization Methods & Software
Publication Type :
Academic Journal
Accession number :
160849212
Full Text :
https://doi.org/10.1080/10556788.2021.1998490