Fast Convergent Inertial Proximal Gradient Algorithms With Dry Friction for Nonconvex Optimization
Abstract
This paper investigates fast first-order methods for nonconvex optimization, inspired by a second-order dynamical system with vanishing damping and strongly convex dry friction. By discretizing this system, we propose a strongly convex dry-friction inertial proximal gradient algorithm, and establish its convergence and geometric convergence rate, under mild conditions. Moreover, an accelerated variant, which combines Nesterov’s accelerated gradient method with the strongly convex dry-friction inertial proximal gradient method, is introduced. Numerical experiments are reported to demonstrate the effectiveness of the proposed algorithms.
Fields of Study & Subjects
Optimization and Variational Analysis
Stochastic Gradient Optimization Techniques
Advanced Optimization Algorithms Research
Publication details
- Journal / source
- IEEE Control Systems Letters
- Volume
- 10
- Pages
- 85–90
- Publication date
- 1 January 2026
- Language
- EN