An efficient modified HS conjugate gradient algorithm in machine learning
The Hestenes-Stiefe (HS) conjugate gradient method is very effective in resolving larger-scale sophisticated smoothing optimization tasks due to its low computational requirements and high computational efficiency. Additionally, the algorithm has been employed in practical applications to address im...
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AIMS Press
2024-11-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024287 |
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| author | Gonglin Yuan Minjie Huang |
| author_facet | Gonglin Yuan Minjie Huang |
| author_sort | Gonglin Yuan |
| collection | DOAJ |
| description | The Hestenes-Stiefe (HS) conjugate gradient method is very effective in resolving larger-scale sophisticated smoothing optimization tasks due to its low computational requirements and high computational efficiency. Additionally, the algorithm has been employed in practical applications to address image restoration and machine learning issues. In this paper, the authors proposed an improved Hestenes-Stiefe conjugate gradient algorithm having characteristics like: ⅰ) The algorithm depicts the decreasing features and trust region properties free of conditionalities. ⅱ) The algorithm satisfies global convergence. ⅲ) The algorithm can be applied to tackle the image restoration problem, monotone nonlinear equations, and machine learning problems. Numerical results revealed that the proffered technique is a competitive method. |
| format | Article |
| id | doaj-art-d4f89d59bdd1460284de604ea11a5920 |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-d4f89d59bdd1460284de604ea11a59202025-01-23T07:53:00ZengAIMS PressElectronic Research Archive2688-15942024-11-0132116175619910.3934/era.2024287An efficient modified HS conjugate gradient algorithm in machine learningGonglin Yuan0Minjie Huang1School of Mathematics and Information Science, Center for Applied Mathematics of Guangxi, Guangxi University, Nanning 530004, ChinaSchool of Mathematics and Information Science, Center for Applied Mathematics of Guangxi, Guangxi University, Nanning 530004, ChinaThe Hestenes-Stiefe (HS) conjugate gradient method is very effective in resolving larger-scale sophisticated smoothing optimization tasks due to its low computational requirements and high computational efficiency. Additionally, the algorithm has been employed in practical applications to address image restoration and machine learning issues. In this paper, the authors proposed an improved Hestenes-Stiefe conjugate gradient algorithm having characteristics like: ⅰ) The algorithm depicts the decreasing features and trust region properties free of conditionalities. ⅱ) The algorithm satisfies global convergence. ⅲ) The algorithm can be applied to tackle the image restoration problem, monotone nonlinear equations, and machine learning problems. Numerical results revealed that the proffered technique is a competitive method.https://www.aimspress.com/article/doi/10.3934/era.2024287conjugate gradientglobal convergenceimage restorationnonlinear equationsmachine learning |
| spellingShingle | Gonglin Yuan Minjie Huang An efficient modified HS conjugate gradient algorithm in machine learning Electronic Research Archive conjugate gradient global convergence image restoration nonlinear equations machine learning |
| title | An efficient modified HS conjugate gradient algorithm in machine learning |
| title_full | An efficient modified HS conjugate gradient algorithm in machine learning |
| title_fullStr | An efficient modified HS conjugate gradient algorithm in machine learning |
| title_full_unstemmed | An efficient modified HS conjugate gradient algorithm in machine learning |
| title_short | An efficient modified HS conjugate gradient algorithm in machine learning |
| title_sort | efficient modified hs conjugate gradient algorithm in machine learning |
| topic | conjugate gradient global convergence image restoration nonlinear equations machine learning |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024287 |
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