A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913823751929856 |
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| author | Işik, İrfan Karahan, Ibrahim Erkaymaz, Okan |
| author_facet | Işik, İrfan Karahan, Ibrahim Erkaymaz, Okan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03794 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems Işik, İrfan Karahan, Ibrahim Erkaymaz, Okan Machine Learning Optimization and Control 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. |
| title | A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2505.03794 |