A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems. (28th November 2013)

• Record Type: Journal Article
• Authors: Yao, Shengwei; Lu, Xiwen; Wei, Zengxin
• Other Names: Soares Delfim Academic Editor.
• Is Part Of: Journal of applied mathematics. Volume 2013(2013)
• Publication Date: 2013-11-28
• Subjects: Mathematics -- Periodicals; 519.05
• DOI: 10.1155/2013/730454
• Languages: English
• ISSNs: 1110-757X

Abstract

Abstract : The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. This paper proposes a conjugate gradient method which is similar to Dai-Liao conjugate gradient method (Dai and Liao, 2001) but has stronger convergence properties. The given method possesses the sufficient descent condition, and is globally convergent under strong Wolfe-Powell (SWP) line search for general function. Our numerical results show that the proposed method is very efficient for the test problems.