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math.OC2026
On The Linear Convergence of Bregman Proximal Gradient Methods with Applications to Kullback--Leibler regression
Jonathan Chirinos-Rodríguez, Christian Daniele, Cédric Févotte +1
Bregman Proximal Gradient methods (BPGM) exploit the underlying geometry of the objective function through a carefully chosen mirror map. In this work, we introduce a novel notion…
math.OC2025
Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descent
Christian Daniele, Silvia Villa, Samuel Vaiter +1
Deep Equilibrium Models (DEQs) are implicit neural networks with fixed points, which have recently gained attention for learning image regularization functionals, particularly in s…