Novel algorithms based on forward-backward splitting technique: effective methods for regression and classification
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Dosyalar
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Iterative Algorithm, Nonexpansive Mappings, Relaxed (?, ?)-cocoercive Mappings, Variational Inequalities
Kaynak
Journal of Global Optimization
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
