A New Hybrid Algorithm for Convex Nonlinear Unconstrained Optimization

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ID: 10707
2019
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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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Authors Hamed, Eman T.;Ahmed, Huda I.;Al-Bayati, Abbas Y.;Hamed, Eman T.;Ahmed, Huda I.;Al-Bayati, Abbas Y.;
Journal journal of applied mathematics
Year 2019
DOI
10.1155/2019/8728196
URL
https://www.hindawi.com/journals/jam/2019/8728196/abs/
Keywords Keywords not found

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