A numerical method of fitting a multiparameter non-linear function to experimental data in the \(L_ 1\) norm. (English) Zbl 0654.65010

A numerical method is developed for fitting a multiparameter function, nonlinear in the parameters which are to be estimated, to experimental data in the \(L_ 1\) norm. This method starts with the least squares solution for the function and then minimizes the expression \(\sum_{i}(x\) \(2_ i+a\) \(2)^{1/2}\), where \(x_ i\) is the i-th experimental datum, starting with an a comparable with the root-mean- square error of the least squares solution. The solution for each fixed a is searched by using the Hessian matrix.
Reviewer: P.I.Ialamov


65D10 Numerical smoothing, curve fitting
Full Text: DOI EuDML


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