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Appears in collection : Imaging and machine learning

In this talk I wlll consider iterative regularization methods for solving linear inverse problems. An advantage of iterative regularization strategies with respect to Tikhonov regularization is that they are developed in conjunction with an optimization algorithm, adapted to the structure of the problem at hand, and the number of iterations plays the role of a regularization parameter. I will show that dual proximal gradient algorithms can be used as iterative regularization procedures, both in the standard and accelerated version. Theoretical findings are complemented with numerical experiments showing state- of-the-art performances.

Information about the video

  • Date of recording 4/1/19
  • Date of publication 5/7/19
  • Institution IHP
  • Language English
  • Format MP4
  • Venue IHP

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