Least-squares refinement with subsidiary conditions

Abstract

A method of least-squares refinement is described in which the subsidiary conditions are treated like observational equations. The advantages of the method are its generality, its adaptability to machine computing, and the possibility of relaxing the subsidiary conditions to any desired degree by appropriate changes in the weighting. In suitable cases the method extends the range for which least-squares refinements converge to the correct solution.