A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS
Conjugate Gradient (CG) methods are widely used for solving unconstrained optimization problems due to their efficiency and low memory requirements. However, standard CG methods may not always guarantee sufficient descent condition, which can impact their robustness and convergence behavior. Additio...
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| Format: | Article |
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Universitas Pattimura
2025-07-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17704 |
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| author | Umar A Omesa Muhammad Y. Waziri Issam A. R. Moghrabi Sulaiman M. Ibrahim Gudu E B Fakai S L Rabiu Bashir Yunus Elissa Nadia Madi |
| author_facet | Umar A Omesa Muhammad Y. Waziri Issam A. R. Moghrabi Sulaiman M. Ibrahim Gudu E B Fakai S L Rabiu Bashir Yunus Elissa Nadia Madi |
| author_sort | Umar A Omesa |
| collection | DOAJ |
| description | Conjugate Gradient (CG) methods are widely used for solving unconstrained optimization problems due to their efficiency and low memory requirements. However, standard CG methods may not always guarantee sufficient descent condition, which can impact their robustness and convergence behavior. Additionally, their effectiveness in training artificial neural networks (ANNs) remains an area of interest. In response, this paper presents a three-term conjugate gradient (CG) method for unconstrained optimization problems. The new parameter is formulated so that the search direction satisfies the sufficient descent condition. The global convergence result of the new algorithm is discussed under suitable assumptions. To evaluate the performance of the new method we considered some standard test problems for unconstrained optimization and applied the proposed method to train different ANNs on some benchmark data sets contained in the NN toolbox. The experimental results show that performance is encouraging for both unconstrained minimization test problems and in training neural networks. |
| format | Article |
| id | doaj-art-cf0eae78f5814e71a99014ef942b1cbc |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-cf0eae78f5814e71a99014ef942b1cbc2025-08-20T03:02:54ZengUniversitas PattimuraBarekeng1978-72272615-30172025-07-011931973198810.30598/barekengvol19iss3pp1973-198817704A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKSUmar A Omesa0Muhammad Y. Waziri1Issam A. R. Moghrabi2Sulaiman M. Ibrahim3Gudu E B4Fakai S L5Rabiu Bashir Yunus6Elissa Nadia Madi7Department of Mathematics, College of Science, Federal University of Agriculture, NigeriaDepartment of Mathematics, Faculty of Physical Sciences, Bayero University, NigeriaDepartment of Information Systems and Technology, Kuwait Technical College, KuwaitFaculty of Arts and Education, Sohar University, OmanDepartment of Mathematics, College of Science, Federal University of Agriculture, NigeriaDepartment of Mathematics, College of Science, Federal University of Agriculture, NigeriaDepartment of Mathematics, Aliko Dangote University of Science and Technology, NigeriaFaculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, MalaysiaConjugate Gradient (CG) methods are widely used for solving unconstrained optimization problems due to their efficiency and low memory requirements. However, standard CG methods may not always guarantee sufficient descent condition, which can impact their robustness and convergence behavior. Additionally, their effectiveness in training artificial neural networks (ANNs) remains an area of interest. In response, this paper presents a three-term conjugate gradient (CG) method for unconstrained optimization problems. The new parameter is formulated so that the search direction satisfies the sufficient descent condition. The global convergence result of the new algorithm is discussed under suitable assumptions. To evaluate the performance of the new method we considered some standard test problems for unconstrained optimization and applied the proposed method to train different ANNs on some benchmark data sets contained in the NN toolbox. The experimental results show that performance is encouraging for both unconstrained minimization test problems and in training neural networks.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17704conjugate gradientglobal convergenceneural networkssufficient descent conditionunconstrained optimization |
| spellingShingle | Umar A Omesa Muhammad Y. Waziri Issam A. R. Moghrabi Sulaiman M. Ibrahim Gudu E B Fakai S L Rabiu Bashir Yunus Elissa Nadia Madi A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS Barekeng conjugate gradient global convergence neural networks sufficient descent condition unconstrained optimization |
| title | A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS |
| title_full | A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS |
| title_fullStr | A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS |
| title_full_unstemmed | A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS |
| title_short | A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS |
| title_sort | three term conjugate gradient method for large scale minimization in artificial neural networks |
| topic | conjugate gradient global convergence neural networks sufficient descent condition unconstrained optimization |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17704 |
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