Abstract
We recently introduced a sparse stretching strategy for handling dense rows that can arise in large-scale linear least-squares problems and make such problems challenging to solve. Sparse stretching is designed to limit the amount of fill within the stretched normal matrix and hence within the subsequent Cholesky factorization.
While preliminary results demonstrated that sparse stretching performs significantly better than standard stretching, it has a number of limitations. In this article, we discuss and illustrate these limitations and propose new strategies that are designed to overcome them.
Numerical experiments on problems arising from practical applications are used to demonstrate the effectiveness of these new ideas. We consider both direct and preconditioned iterative solvers.