A HS-PRP-Type Hybrid Conjugate Gradient Method with Sufficient Descent Property

In this paper, based on the HS method and a modified version of the PRP method, a hybrid conjugate gradient (CG) method is proposed for solving large-scale unconstrained optimization problems. The CG parameter generated by the method is always nonnegative. Moreover, the search direction possesses th...

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Detalles Bibliográficos
Autores principales: Wu, Xiaodi, Zhu, Yihan, Yin, Jianghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8556087/
https://www.ncbi.nlm.nih.gov/pubmed/34721562
http://dx.doi.org/10.1155/2021/2087438
Descripción
Sumario:In this paper, based on the HS method and a modified version of the PRP method, a hybrid conjugate gradient (CG) method is proposed for solving large-scale unconstrained optimization problems. The CG parameter generated by the method is always nonnegative. Moreover, the search direction possesses the sufficient descent property independent of line search. Utilizing the standard Wolfe–Powell line search rule to yield the stepsize, the global convergence of the proposed method is shown under the common assumptions. Finally, numerical results show that the proposed method is promising compared with two existing methods.

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