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Proximal Bundle Algorithms for Nonsmooth Convex Optimization via Fast Gradient Smooth Methods by Ouorou, Adam is a scholarly article available to read on EtoBox.

What is Proximal Bundle Algorithms for Nonsmooth Convex Optimization via Fast Gradient Smooth Methods about?

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.

Author

Ouorou, Adam

Published

2020

Language

EN