New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning

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Bibliographic Details
Main Authors:	Yao, Yonghong, Jolaoso, Lateef O., Shehu, Yekini, Yao, Jen-Chih
Format:	Preprint
Published:	2024
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
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author	Yao, Yonghong Jolaoso, Lateef O. Shehu, Yekini Yao, Jen-Chih
author_facet	Yao, Yonghong Jolaoso, Lateef O. Shehu, Yekini Yao, Jen-Chih
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_14800
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning Yao, Yonghong Jolaoso, Lateef O. Shehu, Yekini Yao, Jen-Chih Optimization and Control 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.
title	New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning
topic	Optimization and Control
url	https://arxiv.org/abs/2404.14800

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