The revisited total least squares problems with linear equality constraint$1

摘要

The total least squares problem with linear equality constraint is proved to be approximated by an unconstrained total least squares problem with a large weight on the constraint. A criterion for choosing the weighting factor is given, and a QR-based inverse (QR-INV) iteration method is presented. Numerical results show that the QR-INV method is more efficient than the standard QR-SVD procedure and Schaffrin's inverse iteration method, especially for large and sparse matrices.