Fast Flexible LSQR with a Hybrid Variant for Efficient Large-Scale Regularization
Abstract. A wide range of applications necessitates solving large-scale ill-posed problems contaminated by noise, which are commonly formulated as linear approximation problems (possibly overdetermined or underdetermined). Krylov subspace regularization methods are particularly advantageous in this context, as they re...