ON ROW RELAXATION METHODS FOR LARGE CONSTRAINED LEAST-SQUARES PROBLEMS

This paper addresses the question of how to construct a row relaxation method for solving large unstructured linear least squares problems, with or without linear constraints. The proposed approach combines the Herman-Lent-Hurwitz scheme for solving regularized least squares problems with the Lent-Censor-Hildreth method for solving linear constraints. However, numerical experiments show that the Herman-Lent-Hurwitz scheme has difficulty reaching a least squares solution. This difficulty is resolved by applying the Riley-Golub iterative improvement process.