# 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 Award 28 Apr 2021 English University of Bath Silvia Gazzola (Supervisor), Melina Freitag (Supervisor) & Manuchehr Soleimani (Supervisor)

### Keywords

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

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