Linear least squares problems with additional constraints and an application to scattered data approximation

We construct generalized inverses to solve least squares problems with partially prescribed kernel and image spaces. To this end we parameterize a special subset of all (1,3)-generalized inverses, and analyze their properties. Furthermore, we discuss an application to scattered data approximation where certain (1,3)-generalized inverses are more adequate than the Moore–Penrose inverse.