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Recently, a new distance has been introduced for the graphs of two point-to-set operators, one of which is maximally monotone. When both operators are the subdifferential of a proper lower semicontinuous convex function, this distance…

Functional Analysis · Mathematics 2020-09-29 Regina S. Burachik , Minh N. Dao , Scott B. Lindstrom

Moreau's seminal paper, introducing what is now called the Moreau envelope and the proximity operator (also known as the proximal mapping), appeared in 1965. The Moreau envelope of a given convex function provides a regularized version…

Functional Analysis · Mathematics 2020-04-14 Heinz H. Bauschke , Minh N. Dao , Scott B. Lindstrom

We systematically study the local single-valuedness of the Bregman proximal mapping and local smoothness of the Bregman--Moreau envelope of a nonconvex function under relative prox-regularity - an extension of prox-regularity - which was…

Optimization and Control · Mathematics 2020-02-03 Emanuel Laude , Peter Ochs , Daniel Cremers

We provide a proximal average with repect to a $1$-coercive Legendre function. In the sense of Bregman distance, the Bregman envelope of the proximal average is a convex combination of Bregman envelopes of individual functions. The Bregman…

Optimization and Control · Mathematics 2022-02-25 Xianfu Wang , Heinz H. Bauschke

We study the relations between some geometric properties of maximal monotone operators and generic geometric and analytical properties of the functions on the associate Fitzpatrick family of convex representations. We also investigate under…

Functional Analysis · Mathematics 2012-07-13 B. F. Svaiter

Classic subdifferentials in variational analysis may fail to fully represent the Bregman proximal operator in the absence of convexity. In this paper, we fill this gap by introducing the left and right \emph{Bregman level proximal…

Optimization and Control · Mathematics 2026-04-14 Ziyuan Wang , Andreas Themelis

We use the relative modular operator to define a generalized relative entropy for any convex operator function g on the positive real line satisfying g(1) = 0. We show that these convex operator functions can be partitioned into convex…

Mathematical Physics · Physics 2015-06-26 Andrew Lesniewski , Mary Beth Ruskai

We provide quantitative and abstract strong convergence results for sequences from a compact metric space satisfying a certain form of \emph{generalized Fej\'er monotonicity} where (1) the metric can be replaced by a much more general type…

Functional Analysis · Mathematics 2025-07-15 Nicholas Pischke

The paper introduces scaled Bregman distances of probability distributions which admit non-uniform contributions of observed events. They are introduced in a general form covering not only the distances of discrete and continuous stochastic…

Information Theory · Computer Science 2021-05-12 Wolfgang Stummer , Igor Vajda

The Bregman divergence (Bregman distance, Bregman measure of distance) is a certain useful substitute for a distance, obtained from a well-chosen function (the "Bregman function"). Bregman functions and divergences have been extensively…

Optimization and Control · Mathematics 2019-04-10 Daniel Reem , Simeon Reich , Alvaro De Pierro

We propose a novel stochastic distributed method for both monotone and strongly monotone variational inequalities with Lipschitz operator and proper convex regularizers arising in various applications from game theory to adversarial…

Optimization and Control · Mathematics 2024-10-07 Aleksandr Beznosikov , Darina Dvinskikh , Dmitry Bylinkin , Andrei Semenov , Alexander Gasnikov

Recently, we systematically studied the basic theory of Bregman circumcenters in another paper. In this work, we aim to apply Bregman circumcenters to optimization algorithms. Here, we propose the forward Bregman monotonicity which is a…

Optimization and Control · Mathematics 2022-03-29 Hui Ouyang

We analyze and characterize maximal monotonicity of linear relations (set-valued operators with linear graphs). An important tool in our study are Fitzpatrick functions. The results obtained partially extend work on linear and at most…

Functional Analysis · Mathematics 2008-05-29 Heinz H. Bauschke , Xianfu Wang , Liangjin Yao

Minimization of suitable statistical distances~(between the data and model densities) has proved to be a very useful technique in the field of robust inference. Apart from the class of $\phi$-divergences of \cite{a} and \cite{b}, the…

Statistics Theory · Mathematics 2021-01-25 Sancharee Basak , Ayanendranath Basu

We establish the convergence of the forward-backward splitting algorithm based on Bregman distances for the sum of two monotone operators in reflexive Banach spaces. Even in Euclidean spaces, the convergence of this algorithm has so far…

Optimization and Control · Mathematics 2020-09-29 Minh N. Bùi , Patrick L. Combettes

We characterize proximity operators, that is to say functions that map a vector to a solution of a penalized least squares optimization problem. Proximity operators of convex penalties have been widely studied and fully characterized by…

Classical Analysis and ODEs · Mathematics 2020-02-24 Rémi Gribonval , Mila Nikolova

The Bregman proximal mapping and Bregman-Moreau envelope are traditionally studied for functions defined on the entire space $\mathbb{R}^n$, even though these constructions depend only on the values of the function within (the interior of)…

Optimization and Control · Mathematics 2025-10-14 Andreas Themelis , Ziyuan Wang

We introduce a notion of variable quasi-Bregman monotone sequence which unifies the notion of variable metric quasi-Fej\'er monotone sequences and that of Bregman monotone sequences. The results are applied to analyze the asymptotic…

Optimization and Control · Mathematics 2015-05-19 Quang Van Nguyen

The Bregman distance $B_{\xi_x}(y,x)$, $\xi_x \in \partial J(y),$ associated to a convex sub-differentiable functional $J$ is known to be in general non-symmetric in its arguments $x$, $y$. In this note we address the question when Bregman…

Optimization and Control · Mathematics 2021-04-14 Stefan Kindermann

In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We present a detailed study of Bregman Distance minimization, a family of generalized entropy measures associated with convex…

Machine Learning · Computer Science 2010-04-23 Mithun Das Gupta , Thomas S. Huang
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