Conjugate Gradient Method: A Developed Version to Resolve Unconstrained Optimization Problems
Ahmad Alhawarat1, Nguyen-Thoi Trung1 & Zabidin Salleh2 Show affiliations
- Ton Duc Thang University, Vietnam
- Universiti Malaysia Terengganu, Malaysia
Journal of Computer Science, Volume 16, Issue 9, pp. 1220–1228 · doi.org/10.3844/jcssp.2020.1220.1228
Abstract
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.
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Alhawarat, A., Trung, N. & Salleh, Z. (2020). Conjugate Gradient Method: A Developed Version to Resolve Unconstrained Optimization Problems. Journal of Computer Science, 16(9), 1220–1228. https://doi.org/10.3844/jcssp.2020.1220.1228
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