A new scaled steepest descent method for unconstrained optimization with global convergence properties
The steepest descent method is the simplest gradient method for solving unconstrained optimization problems. In this study, a new scaled search direction of steepest descent method is proposed. The proposed method is motivated by Andrei's approach of scaled conjugate gradient method. The numeri...
| 出版年: | Journal of Engineering and Applied Sciences |
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| 第一著者: | |
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Medwell Journals
2018
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| オンライン･アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052916904&doi=10.3923%2fjeasci.2018.5442.5445&partnerID=40&md5=822e79dfab1cbbc4ca1e6247aba3db69 |
| 要約: | The steepest descent method is the simplest gradient method for solving unconstrained optimization problems. In this study, a new scaled search direction of steepest descent method is proposed. The proposed method is motivated by Andrei's approach of scaled conjugate gradient method. The numerical results show that the proposed method outperforms than the other classical steepest descent method. © Medwell Journals, 2018. |
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| ISSN: | 1816949X |
| DOI: | 10.3923/jeasci.2018.5442.5445 |