A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization
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 rep...
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2019-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2019/8728196 |
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doaj-894c1fb694764cdebc01b4b6b93aba052020-11-24T20:56:23ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422019-01-01201910.1155/2019/87281968728196A New Hybrid Algorithm for Convex Nonlinear Unconstrained OptimizationEman T. Hamed0Huda I. Ahmed1Abbas Y. Al-Bayati2Department of Operation Research and Intelligent Techniques, College of Computer Sciences and Mathematics, University of Mosul, IraqDepartment of Operation Research and Intelligent Techniques, College of Computer Sciences and Mathematics, University of Mosul, IraqUniversity of Telafer, IraqIn 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.http://dx.doi.org/10.1155/2019/8728196 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Eman T. Hamed Huda I. Ahmed Abbas Y. Al-Bayati |
| spellingShingle |
Eman T. Hamed Huda I. Ahmed Abbas Y. Al-Bayati A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization Journal of Applied Mathematics |
| author_facet |
Eman T. Hamed Huda I. Ahmed Abbas Y. Al-Bayati |
| author_sort |
Eman T. Hamed |
| title |
A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization |
| title_short |
A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization |
| title_full |
A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization |
| title_fullStr |
A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization |
| title_full_unstemmed |
A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization |
| title_sort |
new hybrid algorithm for convex nonlinear unconstrained optimization |
| publisher |
Hindawi Limited |
| series |
Journal of Applied Mathematics |
| issn |
1110-757X 1687-0042 |
| publishDate |
2019-01-01 |
| description |
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. |
| url |
http://dx.doi.org/10.1155/2019/8728196 |
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