A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems

Name: A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems
Keywords: FOS: Computer and information sciences, Computer Science - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control, Machine Learning (cs.LG)

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2025Embargo end date: 01 Jan 2025Publisher:arXiv

Authors: Işik, İrfan; Karahan, Ibrahim; Erkaymaz, Okan;

doi: 10.48550/arxiv.2505.03794

arXiv: http://arxiv.org/abs/2505.03794

A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems

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Abstract

This paper presents an improved forward-backward splitting algorithm with two inertial parameters. It aims to find a point in the real Hilbert space at which the sum of a co-coercive operator and a maximal monotone operator vanishes. Under standard assumptions, our proposed algorithm demonstrates weak convergence. We present numerous experimental results to demonstrate the behavior of the developed algorithm by comparing it with existing algorithms in the literature for regression and data classification problems. Furthermore, these implementations suggest our proposed algorithm yields superior outcomes when benchmarked against other relevant algorithms in existing literature.

20 pages, 5 sections, 5 figures, 5 tables

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control, Machine Learning (cs.LG)

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