Novel algorithms based on forward-backward splitting technique: effective methods for regression and classification
| dc.contributor.author | Atalan, Yunus | |
| dc.contributor.author | Hacıoğlu, Emirhan | |
| dc.contributor.author | Ertürk, Müzeyyen | |
| dc.contributor.author | Gürsoy, Faik | |
| dc.contributor.author | Milovanovi?, Gradimir V. | |
| dc.date.accessioned | 2024-08-26T11:06:07Z | |
| dc.date.available | 2024-08-26T11:06:07Z | |
| dc.date.issued | 2024 | |
| dc.department | Sabire Yazıcı Fen Edebiyat Fakültesi | |
| dc.description.abstract | In this paper, we introduce two novel forward-backward splitting algorithms (FBSAs) for nonsmooth convex minimization. We provide a thorough convergence analysis, emphasizing the new algorithms and contrasting them with existing ones. Our findings are validated through a numerical example. The practical utility of these algorithms in real-world applications, including machine learning for tasks such as classification, regression, and image deblurring reveal that these algorithms consistently approach optimal solutions with fewer iterations, highlighting their efficiency in real-world scenarios. | |
| dc.identifier.doi | 10.1007/s10898-024-01425-w | |
| dc.identifier.issn | 0925-5001 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https:/dx.doi.org/10.1007/s10898-024-01425-w | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/12379 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Journal of Global Optimization | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Iterative Algorithm | |
| dc.subject | Nonexpansive Mappings | |
| dc.subject | Relaxed (?, ?)-cocoercive Mappings | |
| dc.subject | Variational Inequalities | |
| dc.title | Novel algorithms based on forward-backward splitting technique: effective methods for regression and classification | |
| dc.type | Article |
