"This SIAM edition is an unabridged, revised republication of the work first published by Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1974"--Title page verso.
Includes bibliographical references (pages 312-326) and index.
1. Introduction -- 2. Analysis of the least squares problem -- 3. Orthogonal decomposition by certain elementary orthogonal transformations -- 4. Orthogonal decomposition by singular value decomposition -- 5. Perturbation theorems for singular values -- 6. Bounds for the condition number of a triangular matrix -- 7. The pseudoinverse -- 8. Perturbation bounds for the pseudoinverse -- 9. Perturbation bounds for the solution of problem LS -- 10. Numerical computations using elementary orthogonal transformations -- 11. Computing the solution for the overdetermined or exactly determined full rank problem -- 12. Computation of the covariance matrix of the solution parameters -- 13. Computing the solution for the underdetermined full rank problem -- 14. Computing the solution for problem LS with possibly deficient pseudorank -- 15. Analysis of computing errors for householder transformations -- 16. Analysis of computing errors for the problem LS -- 17. Analysis of computing errors for the problem LS using mixed precision arithmetic -- 18. Computation of the singular value decomposition and the solution of problem LS -- 19. Other methods for least squares problems -- 20. Linear least squares with linear equality constraints using a basis of the null space -- 21. Linear least squares with linear equality constraints by direct elimination -- 22. Linear least squares with linear equality constraints by weighting -- 23. Linear least squares with linear inequality constraints -- 24. Modifying a QR decomposition to add or remove column vectors -- 25. Practical analysis of least squares problems -- 26. Examples of some methods of analyzing a least squares problem -- 27. Modifying a QR decomposition to add or remove row vectors with application to sequential processing of problems having a large or banded coefficient matrix -- Appendixes: -- A. Basic linear algebra including projections -- B. Proof of global quadratic convergence of the QR algorithm -- C. Description and use of FORTRAN codes for solving problem LS -- D. Developments from 1974-1995.