| _version_ |
1860796874020093952
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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 |
2021-06-15 04:26:16
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| eventvenue |
Virtual
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| format |
Restricted Document
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| id |
10510
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| institution |
UniSZA
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| originalfilename |
4539-01-FH03-FIK-21-53285.pdf
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| person |
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/91.0.4472.77 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=10510
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| spelling |
10510 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10510 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 4 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/91.0.4472.77 Safari/537.36 2021-06-15 04:26:16 4539-01-FH03-FIK-21-53285.pdf UniSZA Private Access New modification ofthe hestenes-stiefel with strong wolfe line search The method ofthe nonlinear conjugate gradient is widely used in solving large-scale unconstrained optimization since been proven in solving optimization problems without using large memory storage. In this paper, we proposed a new modification of the Hestenes-Stiefel conjugate gradient parameter that fulfils the condition of sufficient descent using a strong Wolfe-Powell line search. Besides, the conjugate gradient method with the proposed conjugate gradient also guarantees low computation of iteration and CPU time by comparing with other classical conjugate gradient parameters. Numerical results have shown that the conjugate gradient method with the proposed conjugate gradient parameter performed better than the conjugate gradient method with other classical conjugate gradient parameters. SCIEMATHIC 2020 Virtual
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| spellingShingle |
New modification ofthe hestenes-stiefel with strong wolfe line search
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| summary |
The method ofthe nonlinear conjugate gradient is widely used in solving large-scale unconstrained optimization since been proven in solving optimization problems without using large memory storage. In this paper, we proposed a new modification of the Hestenes-Stiefel conjugate gradient parameter that fulfils the condition of sufficient descent using a strong Wolfe-Powell line search. Besides, the conjugate gradient method with the proposed conjugate gradient also guarantees low computation of iteration and CPU time by comparing with other classical conjugate gradient parameters. Numerical results have shown that the conjugate gradient method with the proposed conjugate gradient parameter performed better than the conjugate gradient method with other classical conjugate gradient parameters.
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| title |
New modification ofthe hestenes-stiefel with strong wolfe line search
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| title_full |
New modification ofthe hestenes-stiefel with strong wolfe line search
|
| title_fullStr |
New modification ofthe hestenes-stiefel with strong wolfe line search
|
| title_full_unstemmed |
New modification ofthe hestenes-stiefel with strong wolfe line search
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| title_short |
New modification ofthe hestenes-stiefel with strong wolfe line search
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| title_sort |
new modification ofthe hestenes-stiefel with strong wolfe line search
|