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

Author

Listed:
  • Yunus Atalan

    (Aksaray University)

  • Emirhan Hacıoğlu

    (Trakya University)

  • Müzeyyen Ertürk

    (Adiyaman University)

  • Faik Gürsoy

    (Adiyaman University)

  • Gradimir V. Milovanović

    (Serbian Academy of Sciences and Arts
    University of Niš)

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.

Suggested Citation

  • Yunus Atalan & Emirhan Hacıoğlu & Müzeyyen Ertürk & Faik Gürsoy & Gradimir V. Milovanović, 2024. "Novel algorithms based on forward-backward splitting technique: effective methods for regression and classification," Journal of Global Optimization, Springer, vol. 90(4), pages 869-890, December.
    • Handle: RePEc:spr:jglopt:v:90:y:2024:i:4:d:10.1007_s10898-024-01425-w
    DOI: 10.1007/s10898-024-01425-w
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    References listed on IDEAS

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    1. Kweku-Muata Osei-Bryson & Kendall Giles, 2006. "Splitting methods for decision tree induction: An exploration of the relative performance of two entropy-based families," Information Systems Frontiers, Springer, vol. 8(3), pages 195-209, July.
    2. Puya Latafat & Panagiotis Patrinos, 2017. "Asymmetric forward–backward–adjoint splitting for solving monotone inclusions involving three operators," Computational Optimization and Applications, Springer, vol. 68(1), pages 57-93, September.
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