125 citations · 684 across the 34 of their papers we have counts for
5 papers · 1 filter
Proximal Gradient Algorithms under Local Lipschitz Gradient Continuity: A Convergence and Robustness Analysis of PANOC
Alberto De Marchi, Andreas Themelis
Composite optimization offers a powerful modeling tool for a variety of applications and is often numerically solved by means of proximal gradient methods. In this paper, we consid…
Dualities for non-Euclidean smoothness and strong convexity under the light of generalized conjugacy
Emanuel Laude, Andreas Themelis, Panagiotis Patrinos
Relative smoothness and strong convexity have recently gained considerable attention in optimization. These notions are generalizations of the classical Euclidean notions of smooth…
Neural Network Training as an Optimal Control Problem: An Augmented Lagrangian Approach
Brecht Evens, Puya Latafat, Andreas Themelis +2
Training of neural networks amounts to nonconvex optimization problems that are typically solved by using backpropagation and (variants of) stochastic gradient descent. In this wor…
Lasry-Lions Envelopes and Nonconvex Optimization: A Homotopy Approach
Miguel Simões, Andreas Themelis, Panagiotis Patrinos
In large-scale optimization, the presence of nonsmooth and nonconvex terms in a given problem typically makes it hard to solve. A popular approach to address nonsmooth terms in con…
Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity
Puya Latafat, Andreas Themelis, Masoud Ahookhosh +1
We introduce two algorithms for nonconvex regularized finite sum minimization, where typical Lipschitz differentiability assumptions are relaxed to the notion of relative smoothnes…