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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
Optimization landscape of -Bregman relaxations
Jonathan Chirinos-Rodríguez, Cédric Févotte, Emmanuel Soubies
In this paper, we study (noisy) linear systems, and their -regularized optimization problems, coupled with general data fidelity terms. Recent approaches for solving this c…