Summary
This paper studies the algebraic properties and perturbation theory of the generalized total least squares problem (GTLS)AX≈B in whichA =(A 1,A 2),A 1 is free of error, and the error contained in (A 2,B) is of the formEC withC a given nonsingular matrix. The problem was proposed by Van Huffel and Vandewalle in [15]. The solvability conditions, formulas for the GTLS solutions, their residuals, and the minimum norm correction matrices are obtained, and a perturbation theory for the GTLS problem is given.
Similar content being viewed by others
References
Demmel, J.W. (1987): The smallest perturbation of a submatrix which lowers the rank and a constrained total least squares problem. SIAM J. Numer. Anal.24, 199–206
De Moor, B., Golub, G.H. (1991): The restricted singular value decomposition: properties and applications. SIAM J. Matrix Anal. Appl.12, 401–425
Gallo, P.P. (1982): Consistency of regression estimates when some variables are subject to error. Commun. Stat. Theory Methods11, 973–983
Golub, G.H., Van Loan, C.F. (1980): An analysis of the total least squares problem. SIAM J. Numer. Anal.17, 883–893
Golub, G.H., Van Loan, C.F. (1989): Matrix computations 2nd Edn., The Johns Hopkins University Press, Baltimore
Golub, G.H., Hoffman, A., Stewart, G.W. (1987): A generalization of the Eckart-Young-Mirsky matrix approximation theory. Linear Algebra Appl.88–89, 317–327
Horn, R.A., Johnson, C.A. (1985): Matrix analysis. Cambridge University Press, Cambridge
Lawson, C.L., Hanson, R.J. (1974): Sovling least squares problems. Prentice-Hall, Englewood, Cliffs, NJ
Paige, C.C. (1986): Computing the generalized singular value decomposition. SIAM J. Sci. Stat. Comput.7, 1126–1146
Paige, C.C., Saunders, M.A. (1981): Towards a generalized singular value decomposition. SIAM J. Numer. Anal.18, 398–405
Paige, C.C., Wei, M.: Ranks of submatrices of a matrix and its inverse (submitted for publication)
Pearson, K. (1901): On lines and planes of closest fit to points in space. Philos. Mag.2, 559–572
Sprent, P. (1969): Models in regression and related topics. Methuen, London
Van Huffel, S. (1987): Analysis of the total least squares problem and its use in parameter estimation. Ph.D. Thesis, Department Elect. Eng., Katholieke University Leuven, Belgium
Van Huffel, S., Vandewalle, J. (1989): Analysis and properties of the generalized total least squares problemAX≈B when some or all columns inA are subject to error. SIAM J. Matrix Anal. Appl.10, 294–315
Van Huffel, S., Vandewalle, J. (1991): The total least squares problem: computational aspects and analysis. SIAM, Philadelphia
Van Huffel, S., Zha, H. (1991): Restricted total least squares problem: formulation, algorithm, and properties. SIAM J. Matrix Anal. Appl.12, 292–309
Van Loan, C.F. (1976): Generalizing the singular value decomposition. SIAM J. Numer. Anal.13, 76–83
Watson, G.A. (1988): The smallest perturbation of a submatrix which lowers the rank of the matrix. IMA J. Numer. Anal.8, 295–303
Wei, M. (1992): The analysis for the total least squares problem, with more than one solution. SIAM J. Matrix. Anal. Appl.13, 746–763
Wei, M. (1992): Algebraic relations between the total least squares and least squares problems with more than one solution. Numer. Math.62, 123–148
Wei, M., Majda, G.: On the accuracy of the least squares and the total least squares methods. Math. Comput. (to appear)
Zha, H. (1991): Restricted singular value decomposition of matrix triples. SIAM J. Matrix Anal. Appl.12, 172–194
Author information
Authors and Affiliations
Additional information
This author was supported by NSERC of Canada Grant No. A9236
This author was supported by the National Natural Sciences Foundation, P.R. China. This work was carried out when this author was visiting the School of Computer Science. McGill University, Montreal, Quebec, Canada
Rights and permissions
About this article
Cite this article
Paige, C.C., Wei, M. Analysis of the generalized total least squares problemAX≈B when some columns ofA are free of error. Numer. Math. 65, 177–202 (1993). https://doi.org/10.1007/BF01385747
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF01385747