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

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.

• Publication year

  2024

• Venue

  Journal of Scientific Computing

• Publication date

  2024-04-23

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/s10915-025-02900-6arXiv 2404.14800
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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