Conjugate Gradient Method: A Developed Version to Resolve Unconstrained Optimization Problems
- 1 Ton Duc Thang University, Vietnam
- 2 Universiti Malaysia Terengganu, Malaysia
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.
DOI: https://doi.org/10.3844/jcssp.2020.1220.1228
Copyright: © 2020 Ahmad Alhawarat, Nguyen-Thoi Trung and Zabidin Salleh. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Unconstrained Optimization
- Conjugate Gradient
- Line Search
- Convergence Analysis