A HS-PRP-Type Hybrid Conjugate Gradient Method with Sufficient Descent Property. (22nd October 2021)

• Record Type: Journal Article
• Authors: Wu, Xiaodi; Zhu, Yihan; Yin, Jianghua
• Other Names: Khalil Ahmed Mostafa Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2021(2021)
• Publication Date: 2021-10-22
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2021/2087438
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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.