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New Investigation for the Liu-Story Scaled Conjugate Gradient Method for Nonlinear Optimization
Joint Authors
Al-Kawaz, Rana Z.
Al-Bayati, Abbas Y.
Hamed, Eman T.
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
This article considers modified formulas for the standard conjugate gradient (CG) technique that is planned by Li and Fukushima.
A new scalar parameter θkNew for this CG technique of unconstrained optimization is planned.
The descent condition and global convergent property are established below using strong Wolfe conditions.
Our numerical experiments show that the new proposed algorithms are more stable and economic as compared to some well-known standard CG methods.
American Psychological Association (APA)
Hamed, Eman T.& Al-Kawaz, Rana Z.& Al-Bayati, Abbas Y.. 2020. New Investigation for the Liu-Story Scaled Conjugate Gradient Method for Nonlinear Optimization. Journal of Mathematics،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1188030
Modern Language Association (MLA)
Hamed, Eman T.…[et al.]. New Investigation for the Liu-Story Scaled Conjugate Gradient Method for Nonlinear Optimization. Journal of Mathematics No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1188030
American Medical Association (AMA)
Hamed, Eman T.& Al-Kawaz, Rana Z.& Al-Bayati, Abbas Y.. New Investigation for the Liu-Story Scaled Conjugate Gradient Method for Nonlinear Optimization. Journal of Mathematics. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1188030
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1188030