By Ake Bjõrck
The tactic of least squares was once chanced on by means of Gauss in 1795. It has considering that turn into the imperative device for decreasing the effect of mistakes whilst becoming types to given observations. this day, purposes of least squares come up in numerous clinical parts, akin to statistics, geodetics, sign processing, and keep watch over.
In the final twenty years there was a good bring up within the capability for computerized info shooting and computing. Least squares difficulties of huge dimension are actually generally solved. large growth has been made in numerical tools for least squares difficulties, specifically for generalized and changed least squares difficulties and direct and iterative tools for sparse difficulties. previously there has now not been a monograph that covers the whole spectrum of correct difficulties and strategies in least squares.
This quantity offers an in-depth therapy of subject matters equivalent to tools for sparse least squares difficulties, iterative equipment, transformed least squares, weighted difficulties, and limited and regularized difficulties. The greater than 800 references supply a accomplished survey of the on hand literature at the topic.
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Additional info for Numerical Methods for Least Squares Problems
Derivatives of orthogonal projectors and pseudoinverses were first considered by Golub and Pereyra [378, 1973]Stewart [731, 1977] gives asymptotic forms and derivatives for orthogonal projectors, pseudoinverses, and least squares solutions. 5. Componentwise perturbation analysis. There are several drawbacks with a normwise perturbation analysis. As already mentioned, it can give huge overestimates when the corresponding problem is badly scaled. Using norms we ignore how the perturbation is distributed among the elements of the matrix and vector.
If only part of the Penrose conditions hold, the corresponding matrix X is called a generalized inverse. Such inverses have been extensively analyzed; see Nashed [596, 1976]. The pseudoinverse can be shown to have the following properties. 12. 8. A, AH. At, and At A all have rank equal to trace (At A). Proof. 23). See also Penrose [655, i955]The pseudoinverse does not share some other properties of the ordinary inverse. 2. 2. 3 and relates to the least squares solution in the case of full column rank.
16) Proof. The result is established in almost the same way as for the corresponding eigenvalue theorem, the Courant-Fischer theorem; see Wilkinson [836, 1965, pp. 99-101]. The minmax characterization of the singular values may be used to establish results on the sensitivity of the singular values of A to perturbations. 7. 18) Proof. See Stewart [729, 1973, pp. 321-322]. 18) is known as the Wielandt-Hoffman theorem for singular values. The theorem shows the important fact that the singular values of a matrix A are well-conditioned with respect to perturbations of A.