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
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|Source:||Electronic Transactions on Numerical Analysis|
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