Speaker
Description
This talk presents a new family of algorithms for large-scale linear inverse problems built on flexible and inexact variants of the Golub–Kahan factorization. The proposed approach constructs regularized solutions through a sequence of projected (re)weighted least-squares problems, where the projection spaces are adaptively generated and endowed with iteration-dependent preconditioning and controlled inexactness. This framework enables a unified and flexible treatment of challenging problem settings, including general data fidelity models such as those involving p-norms. Numerical experiments in imaging applications, such as deblurring and computed tomography, highlight the effectiveness and competitiveness of the proposed methods with respect other popular methods.