New Conjugate Gradient Method Addressing Large Scale Unconstrained Optimization Problem
Ummie Khalthum Mohd Yusof1, Mohd Asrul Hery Ibrahim2, Mohd Rivaie3, Mustafa Mamat4, Mohamad Afendee Mohamed5, Puspa Liza Ghazali6
1Ummie Khalthum Mohd Yusof, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
2Mohd Asrul Hery Ibrahim, Faculty of Entrepneurship and Business, Universiti Malaysia Kelantan, Kelantan, Malaysia.
3Mohd Rivaei, Faculty of Computer Sciences and Mathematics, Universiti Teknologi Mara (UiTM) Kuala Terengganu, Malaysia.
4Mustafa Mamat, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
5Mohamad Afendee Mohamed, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
6Puspa Liza Ghazali, Faculty of Economics and Management Sciences, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
Manuscript received on 11 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 774-777 | Volume-8 Issue-12S2 October 2019 | Retrieval Number: L113310812S219/2019©BEIESP | DOI: 10.35940/ijitee.L1133.10812S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An iterative conjugate gradient (CG) method is prominently known for dealing with unconstrained optimization problem. A new CG method which is modified by Wei Yao Liu (WYL) method is tested by standard test functions. Moreover, the step size is calculated using exact line search. Theoretical proofs on convergence analysis are shown. As a result, this new CG is comparable to the other methods in finding the optimal points by measuring the total iterations required as well as the computing time. Numerical results showed the execution between three CG methods in details.
Keywords: Conjugate Gradient (CG) Method, Global Convergence, Sufficient Descent Condition, Unconstrained Optimization.
Scope of the Article: Cross-Layer Optimization