| _version_ |
1860796915245907968
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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 |
2021-03-14 03:24:02
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| eventvenue |
Nigeria, Virtual
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| format |
Restricted Document
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| id |
10669
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| institution |
UniSZA
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| originalfilename |
4741-01-FH03-FIK-21-51447.pdf
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| person |
Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/88.0.4324.190 Safari/537.36
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10669
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| spelling |
10669 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10669 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 9 1.6 Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/88.0.4324.190 Safari/537.36 Skia/PDF m88 2021-03-14 03:24:02 4741-01-FH03-FIK-21-51447.pdf UniSZA Private Access The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models The hybrid conjugate gradient (CG) algorithms are among the efficient modifications of the conjugate gradient methods. Some interesting features of the hybrid modifications include inherenting the nice convergence properties and efficient numerical performance of the existing CG methods. In this paper, we proposed a new hybrid CG algorithm that inherits the features of the Rivaie et al. (RMIL∗) and Dai (RMIL+) conjugate gradient methods. The proposed algorithm generates a descent direction under the strong Wolfe line search conditions. Preliminary results on some benchmark problems reveal that the proposed method efficient and promising. 1st International Conference on Recent Trends in Applied Research Nigeria, Virtual
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| spellingShingle |
The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
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| summary |
The hybrid conjugate gradient (CG) algorithms are among the efficient modifications of the conjugate gradient methods. Some interesting features of the hybrid modifications include inherenting the nice convergence properties and efficient numerical performance of the existing CG methods. In this paper, we proposed a new hybrid CG algorithm that inherits the features of the Rivaie et al. (RMIL∗) and Dai (RMIL+) conjugate gradient methods. The proposed algorithm generates a descent direction under the strong Wolfe line search conditions. Preliminary results on some benchmark problems reveal that the proposed method efficient and promising.
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| title |
The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
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| title_full |
The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
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| title_fullStr |
The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
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| title_full_unstemmed |
The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
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| title_short |
The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
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| title_sort |
convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
|