Work

Inertial parameterized Douglas-Rachford for generalized DC programming problems

Year: 2026

Type: preprint

Abstract: We propose two new Douglas-Rachford type algorithms for solving generalized DC programming problems. More precisely, we deal with the minimization of the sum of two possibly nonsmooth convex functions from which a Fréchet differentiable convex function with a Lipschitz continuous gradient is subtracted, in a Hilbertian framework. We introduce an inertial Douglas-Rachford DC algorithm and a parametrized inertial Douglas-Rachford DC algorithm for solving optimization problems of this type, and study their convergence behavior. Numerical experiments illustrate the behavior of the proposed algorithms when solving sparse recovery and robust classification problems, highlighting their superior performance in comparison with their state-of-the-art counterparts.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.11440

Authors: Dixit, Avinash, Gautam, Pankaj, Grad, Sorin-Mihai

Institutions: University of Delhi, Indian Institute of Technology Roorkee, Babeș-Bolyai University, University of Duisburg-Essen

Language: English

Topic: Optimization and Variational Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7221114035

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