Mixed Precision Iterative Methods for Linear Inverse Problems

 

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 precision and Krylov subspace projection techniques. We utilize a filter factor analysis to investigate the regularizing behavior of the proposed iterative methods.