Separable least squares, variable projection, and the Gauss-Newton algorithm
A regression problem is separable if the model can be represented as a linear combination of functions which have a nonlinear parametric dependence. The Gauss-Newton algorithm is a method for minimizing the residual sum of squares in such problems. It is known to be effective both when residuals are...
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| Published in | Electronic transactions on numerical analysis Vol. 28; p. 1 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Institute of Computational Mathematics
01.08.2007
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| Online Access | Get full text |
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