Nonlinear Conjugate Gradient Methods with Sufficient Descent Condition for Large-Scale Unconstrained Optimization. (1st July 2009)

• Record Type: Journal Article
• Authors: Zhang, Jianguo; Xiao, Yunhai; Wei, Zengxin
• Other Names: Júdice Joaquim J. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2009(2009)
• Publication Date: 2009-07-01
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2009/243290
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Two nonlinear conjugate gradient-type methods for solving unconstrained optimization problems are proposed. An attractive property of the methods, is that, without any line search, the generated directions always descend. Under some mild conditions, global convergence results for both methods are established. Preliminary numerical results show that these proposed methods are promising, and competitive with the well-known PRP method.