Zlokazov, V. B. GFIT – Generalized quadratic approximation of functions under constraints. (English) Zbl 0798.65018 Comput. Phys. Commun. 54, No. 2-3, 371-379 (1989). Summary: A method and a program are described for the minimization of a function of the following type \(\Sigma_ x w(x)\{y(x) - f(x,p)\}^ 2\), where \(f(x,p)\) is a regression, nonlinear with respect to the parameter \(p\), and satisfying some additional condition, given by a constraint \(r(f(x,p)) = 0\). The problem is solved using a special recalculation of the weight function \(w(x)\) so that this constraint is taken into account. The method is the most appropriate and efficient for the solution of problems of decompositional filtering of data (function decomposition, robust regression analysis etc.). MSC: 65D10 Numerical smoothing, curve fitting 65K05 Numerical mathematical programming methods 65C99 Probabilistic methods, stochastic differential equations 90C20 Quadratic programming 62J02 General nonlinear regression Keywords:nonlinear regression; weighted fitting; least squares; function decomposition; program; filtering of data; robust regression Software:GFIT PDFBibTeX XMLCite \textit{V. B. Zlokazov}, Comput. Phys. Commun. 54, No. 2--3, 371--379 (1989; Zbl 0798.65018) Full Text: DOI References: [1] Zlokazov, V. B., Preprint of JINR, P10-87-894 (1986), Dubna [2] Zlokazov, V. B., Preprint of JINR, P10-86-502 (1986), Dubna This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.