9 August 2019
Iterative Regularization Methods for Inverse Problems
This course deals with iterative methods for nonlinear ill-posed problems. After an introduction to linear regularization theory and a short excursion to Tikhonov regularization for nonlinear problems, we present gradient and Newton type methods as well as nonstandard iterative algorithms such as Kaczmarz, Halley, expectation maximization, and Bregman iterations. Our emphasis here is on convergence results in the sense of regularization where we intend to also sketch some of the proofs and show numerical results in order to provide insight on the regularizing mechanisms; if time permits, we will also give an outlook to all-at-once formulations and adaptive discretization.
Lecturer: Barbara Kaltenbacher
Coordinator: Monika Wolfmayr
Jyväskylä Summer School offers courses to advanced Master's students, graduate students, and post-docs from the field of Mathematics and Science and Information Technology.
Prerequisites: Course is aimed at PhD students and postdocs. Master students with good knowledge in functional analysis and PDEs could take part as well.
Learning outcomes: After successful completion of this course, students will know methods and corresponding convergence results on modern regularization methods for inverse problems, in particular iterative reconstruction methods. They will understand these convergence results as well as their proofs and will be able to apply these methods.
EUR 0: Participation in the Summer School courses is free of charge, but students are responsible for covering their own meals, accommodation and travel costs as well as possible visa costs.
Jyväskylä Summer School is not able to grant Summer School students financial support.