TY  - CHAP
A1  - Benning, Martin
A1  - Riis, Erlend Skaldehaug
UR  - https://doi.org/10.1007/978-3-030-03009-4_62-1
N1  - This version is the author-accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SP  - 1
T3  - Living reference work
KW  - Optimisatio
KW  -  Bregman proximal methods
KW  -  Bregman iterations
KW  -  Inverse problems
KW  -  Nesterov acceleration
KW  -   Mirror descent
KW  -  Kaczmarz method
KW  -  Coordinate descent
KW  -  Itoh-Abe method
KW  -  Alternating direction method of multipliers
KW  -  Primal-dual hybrid gradient
KW  -  Robust principal components analysis
KW  -  Deep learning
KW  -  Image denoising
CY  - Cham, Switzerland
PB  - Springer Cham
T2  - Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging
N2  - In this chapter we review recent developments in the research of Bregman methods, with particular focus on their potential use for large-scale applications. We give an overview on several families of Bregman algorithms and discuss modifications such as accelerated Bregman methods, incremental and stochastic variants, and coordinate descent-type methods. We conclude this chapter with numerical examples in image and video decomposition, image denoising, and dimensionality reduction with auto-encoders.
ID  - discovery10189902
ED  - Chen, Ke
ED  - Schönlieb, Carola-Bibiane
ED  - Tai, Xue-Cheng
ED  - Younce, Laurent
AV  - public
Y1  - 2021/05/27/
EP  - 42
TI  - Bregman Methods for Large-Scale Optimisation with Applications in Imaging
ER  -