Regularizing with Bregman-Moreau envelopes
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Abstract (arXiv)
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 which has additional desirable properties such as differentiability and full domain. Fifty years ago, Attouch proposed using the Moreau envelope for regularization. Since then, this branch of convex analysis has developed in many fruitful directions. In 1967, Bregman introduced what is nowadays known as the Bregman distance as a measure of discrepancy between two points generalizing the square of the Euclidean distance. Proximity operators based on the Bregman distance have become a topic of significant research as they are useful in the algorithmic solution of optimization problems. More recently, in 2012, Kan and Song studied regularization aspects of the left Bregman-Moreau envelope even for nonconvex functions. In this paper, we complement previous works by analyzing the left and right Bregman-Moreau envelopes and by providing additional asymptotic results. Several examples are provided.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 3; 2025: 3; 2026: 1.
12 papers.
- 2021Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensionscited by 52
- 2025Proper scoring rules for estimation and forecast evaluationcited by 18
- 2022Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopescited by 7
- 2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layerscited by 6
- 2023Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithmscited by 4
- 2023Bregman Graph Neural Networkcited by 4
- 2024Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithmscited by 2
- 2023Compressive Recovery of Sparse Precision Matricescited by 1
- 2024A Bregman firmly nonexpansive proximal operator for baryconvex optimizationcited by 0
- 2025Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problemscited by 0
- 2025On the Tension Between Optimality and Adversarial Robustness in Policy Optimizationcited by 0
- 2026Entropy-Smooth Convex Optimization Cannot Be Acceleratedcited by 0
- 2026Entropy-Smooth Convex Optimization Cannot Be Accelerated29 Jul 2026
- 2025On the Tension Between Optimality and Adversarial Robustness in Policy Optimization1 Dec 2025
- 2025Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problems15 Nov 2025
- 2025Proper scoring rules for estimation and forecast evaluation2 Apr 2025
- 2024A Bregman firmly nonexpansive proximal operator for baryconvex optimization1 Nov 2024
- 2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layers8 Jul 2024
- 2024Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithms11 Apr 2024
- 2023Compressive Recovery of Sparse Precision Matrices8 Nov 2023
- 2023Bregman Graph Neural Network12 Sep 2023
- 2023Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms25 May 2023
- 2022Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes10 Jul 2022
- 2021Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions7 Jun 2021