Speaker
Description
In recent years, mixed-precision and reduced-precision algorithms for solving large-scale linear systems have emerged as an effective approach for exploiting modern GPU architectures. While much of this work has focused on well-conditioned systems, comparatively little attention has been given to ill-posed inverse problems, where regularization is essential. In this talk we consider projected iterated Tikhonov regularization methods in reduced precision and show that these methods can produce reconstructions comparable to their high-precision counterparts. In addition, we discuss a secant-type update for automatic regularization parameter selection within the Golub–Kahan bidiagonalization framework, and demonstrate its effectiveness in the reduced-precision setting.