Conjugate Gradient ‎Method: A Developed Version to Resolve Unconstrained Optimization Problems

Journal of computer sciences

article

Ahmad Alhawarat¹  Nguyen-Thoi Trung¹  Zabidin Salleh² 

[1] Ton Duc Thang University;Universiti Malaysia Terengganu

关键词: Unconstrained Optimization;    Conjugate Gradient;    Line Search;    Convergence Analysis;   

DOI  :  10.3844/jcssp.2020.1220.1228

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

One of the important methods that are widely utilized to resolve ‎unconstrained ‎optimization problems is the Conjugate Gradient (CG) method. This paper aims to propose a new version of the CG method on the basis of Weak Wolfe-Powell (WWP) line search. The assumption is bounded below optimization problems with the Lipschitz continuous gradient. The new parameter obtains global convergence properties when the WWP line search is used. The descent condition is established without using any line search. The performance of the proposed CG method is tested by obtaining some unconstrained optimization problems from the CUTEst library. Testing results show that the proposed version of the CG method outperforms CG-DESCENT version 5.3 in terms of CPU time, function evaluations, gradient evaluations and number of iterations.

【 授权许可】

CC BY