4 citations · 13 across the 15 of their papers we have counts for
4 papers · 1 filter
Deep Neural Network Structures Solving Variational Inequalities
Patrick L. Combettes, Jean-Christophe Pesquet
Motivated by structures that appear in deep neural networks, we investigate nonlinear composite models alternating proximity and affine operators defined on different spaces. We fi…
Perspective Maximum Likelihood-Type Estimation via Proximal Decomposition
Patrick L. Combettes, Christian L. Müller
We introduce an optimization model for maximum likelihood-type estimation (M-estimation) that generalizes a large class of existing statistical models, including Huber's concomitan…
Proximal Activation of Smooth Functions in Splitting Algorithms for Convex Image Recovery
Patrick L. Combettes, Lilian E. Glaudin
Structured convex optimization problems typically involve a mix of smooth and nonsmooth functions. The common practice is to activate the smooth functions via their gradient and th…
Monotone Operator Theory in Convex Optimization
Patrick L. Combettes
Several aspects of the interplay between monotone operator theory and convex optimization are presented. The crucial role played by monotone operators in the analysis and the numer…