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2019 A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization
Eman T. Hamed, Huda I. Ahmed, Abbas Y. Al-Bayati
J. Appl. Math. 2019: 1-6 (2019). DOI: 10.1155/2019/8728196

Abstract

In this study, we tend to propose a replacement hybrid algorithmic rule which mixes the search directions like Steepest Descent (SD) and Quasi-Newton (QN). First, we tend to develop a replacement search direction for combined conjugate gradient (CG) and QN strategies. Second, we tend to depict a replacement positive CG methodology that possesses the adequate descent property with sturdy Wolfe line search. We tend to conjointly prove a replacement theorem to make sure global convergence property is underneath some given conditions. Our numerical results show that the new algorithmic rule is powerful as compared to different standard high scale CG strategies.

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Eman T. Hamed. Huda I. Ahmed. Abbas Y. Al-Bayati. "A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization." J. Appl. Math. 2019 1 - 6, 2019. https://doi.org/10.1155/2019/8728196

Information

Received: 24 December 2018; Revised: 16 February 2019; Accepted: 13 March 2019; Published: 2019
First available in Project Euclid: 16 May 2019

zbMATH: 07132129
MathSciNet: MR3939001
Digital Object Identifier: 10.1155/2019/8728196

Rights: Copyright © 2019 Hindawi

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