An Extension of Polak-Ribière-Polyak Method using Exact Line Search
Mahmoud Dawahdeh1, Mustafa Mamat2, Mohd Rivaie3, Mohamad Afendee Mohamed4, Puspa Liza Ghazali5

1Mahmoud Dawahdeh, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
2Mustafa Mamat, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
3Mohd Rivaie, Department of Computer Science and Mathematics, Universiti Technologi MARA (UiTM) Terengganu, Kuala Terengganu Campus, Malaysia.
4Mohamad Afendee Mohamed, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
5Puspa Liza Ghazali, Faculty of Economics and Management Sciences, Universiti Sultan Zainal Abidin, Terengganu, Malaysia.
Manuscript received on 18 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 368-373 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B10630782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1063.0782S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Lately, many large-scale unconstrained optimization problems rely upon nonlinear conjugate gradient (CG) methods. Many areas such as engineering, and computer science have benefited because of its simplicity, fast and low memory requirements. Many modified coefficients have appeared recently, all of which to improve these methods. This paper considers an extension conjugate gradient method of PolakRibière-Polyak using exact line search to show that it holds for some properties such as sufficient descent and global convergence. A set of 113 test problems is used to evaluate the performance of the proposed method and get compared to other existing methods using the same line search.
Keywords: Conjugate Gradient (CG) Method, Exact Line Search, Global Convergence, Sufficient Descent Property.
Scope of the Article: Smart Learning Methods and Environments