TY - JOUR
T1 - An efficient hybrid conjugate gradient method for unconstrained optimization
AU - Ibrahim, Abdulkarim Hassan
AU - Kumam, Poom
AU - Kamandi, Ahmad
AU - Abubakar, Auwal Bala
N1 - Funding Information:
The first author was supported by the ‘Petchra Pra Jom Klao PhD Research Scholarship from King Mongkut's University of Technology Thonburi’ [grant number 16/2561]. The authors also acknowledge the financial support provided by the Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT. Moreover, this project is funded by National Council of Thailand (NRCT) under Research Grants for Talented Mid-Career Researchers (Contract no. N41A640089). We are grateful to the anonymous referees and associate editor for their useful comments, which have made the paper clearer and more comprehensive than the earlier version. The last author acknowledges with thanks, the Department of Mathematics and Applied Mathematics at the Sefako Makgatho Health Sciences University.
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - CUTEst
KW - Dai–Liao conjugacy
KW - Quasi-Newton direction
KW - Unconstrained optimization
KW - conjugate gradient method
KW - hybrid conjugate gradient method
UR - http://www.scopus.com/inward/record.url?scp=85125333218&partnerID=8YFLogxK
U2 - 10.1080/10556788.2021.1998490
DO - 10.1080/10556788.2021.1998490
M3 - Article
AN - SCOPUS:85125333218
SN - 1055-6788
VL - 37
SP - 1370
EP - 1383
JO - Optimization Methods and Software
JF - Optimization Methods and Software
IS - 4
ER -