Proximal bundle algorithms for nonsmooth convex optimization via fast gradient smooth methods

Autor:	Ouorou, Adam
Rok vydání:	2020
Předmět:	Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	We propose new proximal bundle algorithms for minimizing a nonsmooth convex function. These algorithms are derived from the application of Nesterov fast gradient methods for smooth convex minimization to the so-called Moreau-Yosida regularization $F_\mu$ of $f$ w.r.t. some $\mu>0$. Since the exact values and gradients of $F_\mu$ are difficult to evaluate, we use approximate proximal points thanks to a bundle strategy to get implementable algorithms. One of these algorithms appears as an implementable version of a special case of inertial proximal algorithm. We give their complexity estimates in terms of the original function values, and report some preliminary numerical results. Comment: 20 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.03437 Zobrazit plný text záznamu View this record from Arxiv