27th Conference of the International Linear Algebra Society (ILAS 2026)

Mixed-to-Low Precision Iterative Methods for Linear Inverse Problems

May 20, 2026, 11:25 AM

25m

Torgersen Hall 1030

Minisymposium Talk Inverse Problems and Uncertainty Quantification through the Lens of Numerical Linear Algebra Inverse Problems and Uncertainty Quantification through the Lens of Numerical Linear Algebra

Speaker

Lucas Onisk (Emory University)

Description

Many problems in science and engineering give rise to linear systems of equations that are commonly referred to as large-scale linear discrete ill-posed problems. The matrices that define these problems are typically severely ill-conditioned and may be rank deficient. Because of this, regularization is often needed to stem the effect of perturbations caused by error in the available data. In this talk we consider the solution of the regularized least-squares problem using both mixed-to-low precision and Krylov subspace projection techniques. We utilize a filter factor analysis to investigate the regularizing behavior of the proposed iterative methods as well as numerical experiments to verify their efficacy.