Novel algorithms based on forward-backward splitting technique: effective methods for regression and classification

Atalan, YunusHacıoğlu, EmirhanErtürk, MüzeyyenGürsoy, FaikMilovanovi?, Gradimir V.2024-08-262024-08-2620240925-5001https:/dx.doi.org/10.1007/s10898-024-01425-whttps://hdl.handle.net/20.500.12451/12379In 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.eninfo:eu-repo/semantics/openAccessIterative AlgorithmNonexpansive MappingsRelaxed (?, ?)-cocoercive MappingsVariational InequalitiesNovel algorithms based on forward-backward splitting technique: effective methods for regression and classificationArticle10.1007/s10898-024-01425-wQ2N/A