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First-Order Primal–Dual Methods for Nonsmooth Non-convex Optimization

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

Author

Listed:
  • Tuomo Valkonen

    (Escuela Politécnica Nacional, Center for Mathematical Modeling
    University of Helsinki, Department of Mathematics and Statistics)

Abstract

We provide an overview of primal–dual algorithms for nonsmooth and non-convex-concave saddle-point problems. This flows around a new analysis of such methods, using Bregman divergences to formulate simplified conditions for convergence.

Suggested Citation

  • Tuomo Valkonen, 2023. "First-Order Primal–Dual Methods for Nonsmooth Non-convex Optimization," 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 18, pages 707-748, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_93
    DOI: 10.1007/978-3-030-98661-2_93
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