Conjugate gradient and steepest descent approach on quasi-Newton search direction
| Format: | Restricted Document |
|---|
| _version_ | 1860799605536456704 |
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| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 |
| date | 2015-10-26 10:59:25 |
| eventvenue | Penang, Malaysia |
| format | Restricted Document |
| id | 6657 |
| institution | UniSZA |
| originalfilename | 0199-01-FH03-FIK-15-03958.jpg |
| person | UniSZA Unisza unisza |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6657 |
| spelling | 6657 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6657 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper UniSZA Unisza unisza image/jpeg inches 96 96 2015-10-26 10:59:25 787 1403x787 1403 35 35 0199-01-FH03-FIK-15-03958.jpg UniSZA Private Access Conjugate gradient and steepest descent approach on quasi-Newton search direction An approach of using conjugate gradient and classic steepest descent search direction onto quasi-Newton search direction had been proposed in this paper and we called it as 'scaled CGSD-QN' search direction. A new coefficient formula had been successfully constructed for being used in the 'scaled CGSD-QN' search direction and proven here that the coefficient formula is globally converge to the minimizer. The Hessian update formula that has been used in the quasi-Newton algorithm is DFP update formula. This new search direction approach was testes with some some standard unconstrained optimization test problems and proven that this new search direction approach had positively affect quasi-Newton method by using DFP update formula. 21st National Symposium on Mathematical Sciences: Germination of Mathematical Sciences Education and Research Towards Global Sustainability, SKSM 21 Penang, Malaysia |
| spellingShingle | Conjugate gradient and steepest descent approach on quasi-Newton search direction |
| summary | An approach of using conjugate gradient and classic steepest descent search direction onto quasi-Newton search direction had been proposed in this paper and we called it as 'scaled CGSD-QN' search direction. A new coefficient formula had been successfully constructed for being used in the 'scaled CGSD-QN' search direction and proven here that the coefficient formula is globally converge to the minimizer. The Hessian update formula that has been used in the quasi-Newton algorithm is DFP update formula. This new search direction approach was testes with some some standard unconstrained optimization test problems and proven that this new search direction approach had positively affect quasi-Newton method by using DFP update formula. |
| title | Conjugate gradient and steepest descent approach on quasi-Newton search direction |
| title_full | Conjugate gradient and steepest descent approach on quasi-Newton search direction |
| title_fullStr | Conjugate gradient and steepest descent approach on quasi-Newton search direction |
| title_full_unstemmed | Conjugate gradient and steepest descent approach on quasi-Newton search direction |
| title_short | Conjugate gradient and steepest descent approach on quasi-Newton search direction |
| title_sort | conjugate gradient and steepest descent approach on quasi-newton search direction |