A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization

Author:

Hamed Eman T.1,Ahmed Huda I.1ORCID,Al-Bayati Abbas Y.2

Affiliation:

1. Department of Operation Research and Intelligent Techniques, College of Computer Sciences and Mathematics, University of Mosul, Iraq

2. University of Telafer, Iraq

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.

Funder

University of Mosul

Publisher

Hindawi Limited

Subject

Applied Mathematics

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A new conjugacy coefficient of nonlinear conjugate gradient method for minimization;AIP Conference Proceedings;2023

2. The New Spectral Conjugate Gradient Method for Minimization;2022 International Conference on Data Science and Intelligent Computing (ICDSIC);2022-11-01

3. A Method of Two New Augmented Lagrange Multiplier Versions for Solving Constrained Problems;International Journal of Mathematics and Mathematical Sciences;2022-04-23

4. A modified conjugate gradient parameter via hybridization approach for solving large-scale systems of nonlinear equations;SeMA Journal;2022-04-12

5. A Numerical Study for Solving Unconstrained Optimization Problems Using Method of Three-Term Conjugate Gradient;2022 International Conference on Computer Science and Software Engineering (CSASE);2022-03-15

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