RIS ID
121657
Abstract
Locating proximal points is a component of numerous minimization algorithms. This work focuses on developing a method to find the proximal point of a convex function at a point, given an inexact oracle. Our method assumes that exact function values are at hand, but exact subgradients are either not available or not useful. We use approximate subgradients to build a model of the objective function, and prove that the method converges to the true prox-point within acceptable tolerance. The subgradient g_k used at each step k is such that the distance from g_k to the true subdifferential of the objective function at the current iteration point is bounded by some fixed epsilon>0. The algorithm includes a novel tilt-correct step applied to the approximate subgradient.
Publication Details
Planiden, C. & Hare, W. (2018). Computing proximal points of convex functions with inexact subgradients. Set-Valued and Variational Analysis: an international journal devoted to the theory of multifunctions, 26 (3), 469-492.