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Journal of Applied Mathematics
Volume 2013, Article ID 730454, 9 pages
Research Article

A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems

1School of Science, East China University of Science and Technology, Shanghai 200237, China
2School of Information and Statistics, Guangxi University of Finance and Economics, Nanning 530003, China
3College of Mathematics and Information Science, Guangxi University, Nanning 530004, China

Received 26 August 2013; Accepted 22 October 2013

Academic Editor: Delfim Soares Jr.

Copyright © 2013 Shengwei Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.