Affiliation:
1. California Institute of Technology, Pasadena
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.
Publisher
Association for Computing Machinery (ACM)
Reference4 articles.
1. The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate
2. Derivative free analogues of the Levenberg-Marquardt and Gauss algorithms for nonlinear least squares approximation
3. Lawson C.L. and Hanson R.J. Solving Least Squares Problems Prentice-Hall Englewood Cliffs N.J. 1974 (to be published). Lawson C.L. and Hanson R.J. Solving Least Squares Problems Prentice-Hall Englewood Cliffs N.J. 1974 (to be published).
Cited by
30 articles.
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