Fred T. Krogh
Abstract
Nonlinear least squares problems frequently arise for which the variables to be solved for can be separated into a linear and a nonlinear part. A variable projection algorithm has been developed recently which is designed to take advantage of the structure of a problem whose variables separate in this way. This paper gives a slightly more efficient and slightly more general version of this algorithm than has appeared earlier.
- 1 Golub, G.H., and Pereyra, V. The differentiation of pseudoinverses and nonlinear least squares problems whose variables separate. Siam J. Numer. Analysis 10 (1973), 413-432.Google Scholar
Cross Ref
- 2 Brown, K.M., and Dennis, J.E. Jr. Derivative free analogues of the Levenberg-Marquardt and Gauss algorithms for nonlinear least squares approximation. Numer. Math. 18 (1972), 289-297.Google ScholarDigital Library
- 3 Lawson, C.L., and Hanson, R.J. Solving Least Squares Problems, Prentice-Hall, Englewood Cliffs, N.J. 1974 (to be published).Google Scholar
- 4 Golub, G.H., and Pereyra, V. The differentiation of pseudoinverses and nonlinear least squares problems whose variables separate. Rep. STAN-CS-72-261, Stanford U. Comput. Sci. Dep., Stanford, Calif., 1972. Google ScholarDigital Library
Index Terms
- Efficient implementation of a variable projection algorithm for nonlinear least squares problems
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