Forward-Backward Splitting with Bregman Distances
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Abstract (arXiv)
We propose a forward-backward splitting algorithm based on Bregman distances for composite minimization problems in general reflexive Banach spaces. The convergence is established using the notion of variable quasi-Bregman monotone sequences. Various examples are discussed, including some in Euclidean spaces, where new algorithms are obtained.
Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 May 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
7 papers.
- 2022Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopescited by 7
- 2021First-Order Methods for Convex Optimizationcited by 4
- 2021A Stochastic Bregman Primal-Dual Splitting Algorithm for Composite Optimizationcited by 4
- 2023Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithmscited by 4
- 2023Bregman Graph Neural Networkcited by 4
- 2024Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculumcited by 3
- 2026Adaptive Bregman Proximal Stochastic Gradient with a Stabilized Barzilai--Borwein Step Sizecited by 0
- 2026Adaptive Bregman Proximal Stochastic Gradient with a Stabilized Barzilai--Borwein Step Size12 Aug 2026
- 2024Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum26 Feb 2024
- 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
- 2021A Stochastic Bregman Primal-Dual Splitting Algorithm for Composite Optimization22 Dec 2021
- 2021First-Order Methods for Convex Optimization4 Jan 2021