Fast Iterative Regularisation Methods
: (Alternate Format Thesis)

Student thesis: Doctoral ThesisPhD

Abstract

This thesis assembles three published papers containing original research in the area of regularization techniques for large-scale linear discrete inverse problems. These include a new principled algorithmic framework for Krylov-Tikhonov methods that automatically sets the regularization parameter, and new algorithms for $\ell_1$-$\ell_p$ and total variation regularization. In order to present the natural framework of this thesis, a general introduction to large scale linear discrete inverse problems is given first, along with a brief description of the nature of these problems that motivates the need for regularization. 

Date of Award28 Apr 2021
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorSilvia Gazzola (Supervisor), Melina Freitag (Supervisor) & Manuchehr Soleimani (Supervisor)

Keywords

  • Krylov subspace methods
  • imaging problems
  • large-scale linear inverse problems

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