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
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