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1860800045059670016
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2020-09-02 12:43:57
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| eventvenue |
Michigan, USA
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Restricted Document
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| id |
8425
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UniSZA
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1825-01-FH03-FIK-20-42147.pdf
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helpdeskadm
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=8425
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| spelling |
8425 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=8425 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 10 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in helpdeskadm 2020-09-02 12:43:57 1825-01-FH03-FIK-20-42147.pdf 41 UniSZA Private Access Solving unconstrained minimization problems with a new hybrid conjugate gradient method Conjugate gradient (CG) method is an efficient method for solving unconstrained, large-scale optimization problems. Hybridization is one of the common approaches in the modification of the CG method. This paper presents a new hybrid CG and compares its efficiency with the classical CG method, which are Hestenes-Stiefel (HS), Nurul HajarMustafa-Rivaie (NHMR), Fletcher-Reeves (FR) and Wei-Yao-Liu (WYL) methods. The proposed a new hybrid CG is evaluated as a convex combination of HS and NHMR method. Their performance is analyzed under the exact line search. The new method satisfies the sufficient descent condition and supports global convergence. The results show that the new hybrid CG has the best efficiency among the classical CG of HS, NHMR, FR, and WYL in terms of the number of iterations (NOI) and the central processing unit (CPU) per time. 5th North American International Conference on Industrial Engineering and Operations Management Michigan, USA
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| spellingShingle |
Solving unconstrained minimization problems with a new hybrid conjugate gradient method
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| subject |
41
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| summary |
Conjugate gradient (CG) method is an efficient method for solving unconstrained, large-scale optimization problems. Hybridization is one of the common approaches in the modification of the CG method. This paper presents a new hybrid CG and compares its efficiency with the classical CG method, which are Hestenes-Stiefel (HS), Nurul HajarMustafa-Rivaie (NHMR), Fletcher-Reeves (FR) and Wei-Yao-Liu (WYL) methods. The proposed a new hybrid CG is evaluated as a convex combination of HS and NHMR method. Their performance is analyzed under the exact line search. The new method satisfies the sufficient descent condition and supports global convergence. The results show that the new hybrid CG has the best efficiency among the classical CG of HS, NHMR, FR, and WYL in terms of the number of iterations (NOI) and the central processing unit (CPU) per time.
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| title |
Solving unconstrained minimization problems with a new hybrid conjugate gradient method
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| title_full |
Solving unconstrained minimization problems with a new hybrid conjugate gradient method
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| title_fullStr |
Solving unconstrained minimization problems with a new hybrid conjugate gradient method
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| title_full_unstemmed |
Solving unconstrained minimization problems with a new hybrid conjugate gradient method
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| title_short |
Solving unconstrained minimization problems with a new hybrid conjugate gradient method
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| title_sort |
solving unconstrained minimization problems with a new hybrid conjugate gradient method
|