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Bregman Methods for Large-Scale Optimization with Applications in Imaging

In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Author

Listed:
  • Martin Benning

    (Queen Mary University of London, The School of Mathematical Sciences)

  • Erlend Skaldehaug Riis

    (The Department of Applied Mathematics and Theoretical Physics)

Abstract

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.

Suggested Citation

  • Martin Benning & Erlend Skaldehaug Riis, 2023. "Bregman Methods for Large-Scale Optimization with Applications in Imaging," Springer Books, in: Ke Chen & Carola-Bibiane Schönlieb & Xue-Cheng Tai & Laurent Younes (ed.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, chapter 3, pages 97-138, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_62
    DOI: 10.1007/978-3-030-98661-2_62
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