Hybrid Quasi-Newton with New Conjugate Gradient using Exact Line Search
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 Rivaie, Department of Computer Sciences and Mathematics, Universiti Teknologi MARA, 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 16 February 2019 | Revised Manuscript received on 07 March 2019 | Manuscript Published on 08 June 2019 | PP: 651-655 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E11350275S419/19©BEIESP
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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: Until now, Quasi-newton (QN) method is the most well-known method for solving unconstrained optimization problem. This method consumes lesser time as compared to Newton method since it is unnecessary to compute Hessian matrices. For QN method, BFGS is the best solver in finding the optimum solutions. Therefore, a new hybrid coefficient which possesses the convergence analysis computed by exact line search is introduced. This new hybrid coefficient is numerically proven by producing the best outcomes with least iteration number and CPU time.
Keywords: Quasi-Newton Method, Sufficient Descent Condition and Global Convergence, Unconstrained Optimization.
Scope of the Article: Search-Based Software Engineering