Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence
arXiv:2412.20747
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.
21 pages, 5 figures, 4 tables