Computational Issues for a New Class of Preconditioners

In this paper we consider solving a sequence of weighted linear least squares problems where the only changes from one problem to the next are the weights and the right hand side (or data). We alternate between iterative and direct methods to solve the normal equations for the least squares problems. The direct method is the Cholesky factorization. For the iterative method we discuss a class of preconditioners based on a low rank correction of a Cholesky factorization of a weighted normal equation coefficient matrix. Different ways to compute the preconditioner are given. Further, a sparse algorithm for modifying the Cholesky factors by a low rank matrix is derived.