Bregman distances play a key role in generalized versions of the proximal algorithm. This paper proposes a new characterization of Bregman distances in terms of their gradient and Hessian matrix. Thanks to this characterization, we obtain two results: all the so called self-proximal distances are Bregman, and all the induced proximal distances, under some regularity assumptions, are Bregman functions

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Felipe Alvarez

Centro de Modelamiento Matemático, Dep. de Ingeniería Matemática, Universidad de Chile, Beauchef 851, Santiago, Chile

Rafael Correa

Centro de Modelamiento Matemático, Dep. de Ingeniería Matemática, Universidad de Chile, Beauchef 851, Santiago, Chile

Matthieu Marechal

Instituto de Ciencias Basicas, Facultad de Ingeniería, Universidad Diego Portales, Ejército 441, Santiago, Chile

matthieu.marechal@udp.cl

F. Alvarez, R. Correa, M. Marechal. “Regular Self-Proximal Distances are Bregman.” Journal of Convex Analysis 24 (2017), No. 1, 135–148.