3 papers
math.OC2024
Learning Firmly Nonexpansive Operators
Kristian Bredies, Jonathan Chirinos-Rodriguez, Emanuele Naldi
This paper proposes a data-driven approach for constructing firmly nonexpansive operators. We demonstrate its applicability in Plug-and-Play (PnP) methods, where classical algorith…
math.ST2023
On Learning the Optimal Regularization Parameter in Inverse Problems
Jonathan Chirinos Rodriguez, Ernesto De Vito, Cesare Molinari +2
Selecting the best regularization parameter in inverse problems is a classical and yet challenging problem. Recently, data-driven approaches have become popular to tackle this chal…
math.FA2023
On extreme points and representer theorems for the Lipschitz unit ball on finite metric spaces
Kristian Bredies, Jonathan Chirinos Rodriguez, Emanuele Naldi
In this note, we provide a characterization for the set of extreme points of the Lipschitz unit ball in a specific vectorial setting. While the analysis of the case of real-valued…