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A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems
Joint Authors
Wei, Zengxin
Lu, Xiwen
Yao, Shengwei
Source
Journal of Applied Mathematics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yao, Shengwei& Lu, Xiwen& Wei, Zengxin. 2013. A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-494101
Modern Language Association (MLA)
Yao, Shengwei…[et al.]. A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems. Journal of Applied Mathematics No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-494101
American Medical Association (AMA)
Yao, Shengwei& Lu, Xiwen& Wei, Zengxin. A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-494101
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-494101