Academic Journal
New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning: New Douglas-Rashford splitting algorithms for generalized DC programming with applications in machine learning
| Title: | New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning: New Douglas-Rashford splitting algorithms for generalized DC programming with applications in machine learning |
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| Authors: | Yonghong Yao, Lateef O. Jolaoso, Yekini Shehu, Jen-Chih Yao |
| Source: | Journal of Scientific Computing. 103 |
| Publication Status: | Preprint |
| Publisher Information: | Springer Science and Business Media LLC, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | machine learning, Numerical mathematical programming methods, DC programming, Nonlinear programming, Optimization and Control (math.OC), nonconvex optimization, Learning and adaptive systems in artificial intelligence, FOS: Mathematics, Nonconvex programming, global optimization, Douglas-Rachford splitting algorithm, Mathematics - Optimization and Control |
| Description: | In this work, we propose some new Douglas-Rashford splitting algorithms for solving a class of generalized DC (difference of convex functions) in real Hilbert spaces. The proposed methods leverage the proximal properties of the nonsmooth component and a fasten control parameter which improves the convergence rate of the algorithms. We prove the convergence of these methods to the critical points of nonconvex optimization under reasonable conditions. We evaluate the performance and effectiveness of our methods through experimentation with three practical examples in machine learning. Our findings demonstrated that our methods offer efficiency in problem-solving and outperform state-of-the-art techniques like the DCA (DC Algorithm) and ADMM. |
| Document Type: | Article |
| File Description: | application/xml |
| Language: | English |
| ISSN: | 1573-7691 0885-7474 |
| DOI: | 10.1007/s10915-025-02900-6 |
| DOI: | 10.48550/arxiv.2404.14800 |
| Access URL: | http://arxiv.org/abs/2404.14800 |
| Rights: | Springer Nature TDM CC BY |
| Accession Number: | edsair.doi.dedup.....1ad6a271c23c8b830f3e54ca845fdd9d |
| Database: | OpenAIRE |
| ISSN: | 15737691 08857474 |
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| DOI: | 10.1007/s10915-025-02900-6 |