2 papers
math.OC2025
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.ST2024
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…