Krylov Methods in Imaging: Inverse Problems, Data Assimilation, and Uncertainty QuantificationMS8

Krylov methods have played and continue to play a critical role in the development of iterative techniques for solving inverse problems that arise in many important imaging applications such as image deblurring and tomographic reconstruction. This minisymposium will highlight recent developments on Krylov methods for large-scale inverse problems, data assimilation and uncertainty quantification.

Analysis of bidiagonalization-based regularization methods for inverse problems with general noise setting
Iveta Hnetynkova (Charles University, Faculty of Mathematics and Physics)
Flexible Krylov methods for l_p-regularization
Julianne Chung (Virginia Tech)
Incorporating Known Information into a Krylov Subspace Iteration
Kirk Soodhalter (Trinity College Dublin)
Krylov, Bayes and L2 magic.
Daniela Calvetti (Case Western Reserve University)
Adaptative preconditioning for TV regularization
Malena Sabate Landman (University of Bath)
Regularization parameter convergence for hybrid RSVD methods
Rosemary Renaut (Arizona State University)
Truncation and Recycling Methods for Lanczos Bidiagonalization and Hybrid Regularization
Eric de Sturler (Virginia Tech)
Julianne Chung (Virginia Tech)
Eric de Sturler (Virginia Tech)
Arvind Saibaba (North Carolina State University)
bayesian methods, computed tomography, image deblurring, image reconstruction, inverse problems, numerical linear algebra, partial differential equation models