Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence
Optimization and Control
2026-05-25 v2 Numerical Analysis
Numerical Analysis
Abstract
In this paper, we find the special case of the subgradient method minimizing a one-dimensional real-valued function, which we term the specular gradient method, that converges root-linearly without any additional assumptions except the convexity. Furthermore, we suggest a way to implement the specular gradient method without explicitly calculating specular derivatives.
Cite
@article{arxiv.2412.20747,
title = {Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence},
author = {Kiyuob Jung and Jehan Oh},
journal= {arXiv preprint arXiv:2412.20747},
year = {2026}
}
Comments
21 pages, 5 figures, 4 tables