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.

Tue 05 June at 13:30 in Room E (Palazzina A - Building A floor 2)
Analysis of bidiagonalization-based regularization methods for inverse problems with general noise setting
Iveta Hnetynkova ( Charles University, Faculty of Mathematics and Physics )
Efficient generalized Golub-Kahan based methods for dynamic inverse problems
Julianne Chung ( Virginia Tech )
Incorporating Known Information into a Krylov Subspace Iteration
Kirk Soodhalter ( Trinity College Dublin )
Krylov Recycling for Sequences of Shifted Systems Arising in Image Restoration
Misha Kilmer ( Tufts University )
Tue 05 June at 16:00 in Room E (Palazzina A - Building A floor 2)
Krylov, Bayes and L2 magic.
Daniela Calvetti ( Case Western University )
Preconditioned Krylov subspace methods for sampling in Bayesian inverse problems
Arvind Saibaba ( North Carolina State University )
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