Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence

Abstract

We propose the specular gradient method for one-dimensional convex optimization. Assuming that the minimum is attained and a suitable initial distance bound holds, we establish R-linear convergence using normalized steps of geometrically decreasing length. Neither strong convexity nor differentiability is required.