The State-of-the-Art of Preconditioners for Sparse Linear Least-Squares Problems
Nicholas I. M. Gould, J. A. Scott · ACM Transactions on Mathematical Software · 2017
In recent years, a variety of preconditioners have been proposed for use in solving large sparse linear least-squares problems. These include simple diagonal preconditioning, preconditioners based on incomplete factorizations, and stationary inner iterations used with Krylov subspace methods. In this study, we briefly review preconditioners for which software has been made available, then present a numerical evaluation of them using performance profiles and a large set of problems arising from practical applications. Comparisons are made with state-of-the-art sparse direct methods.