On an iterative algorithm of order 1.839\(\dots\) for solving the nonlinear least squares problems

DOI10.1016/j.amc.2003.12.025zbMath1063.65048OpenAlexW2011721625MaRDI QIDQ1763238

S. M. Shakhno, O. P. Gnatyshyn

Publication date: 22 February 2005

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2003.12.025

zbMATH Keywords

convergencenumerical examplesnonlinear least squares problemderivative-free iteration method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Derivative-free methods and methods using generalized derivatives (90C56) Quadratic programming (90C20)

Related Items (10)

Convergence analysis of the Gauss-Newton-Potra method for nonlinear least squares problems ⋮ Enhancing the practicality of Newton-Cotes iterative method ⋮ Local convergence of a secant type method for solving least squares problems ⋮ Expanding the applicability of four iterative methods for solving least squares problems ⋮ Gauss-Newton-Kurchatov method for the solution of non-linear least-square problems using \(\omega\)-condition ⋮ On a unified convergence analysis for Newton-type methods solving generalized equations with the Aubin property ⋮ A derivative free iterative method for solving least squares problems ⋮ On an iterative algorithm of order 1.839\(\dots\) for solving the nonlinear least squares problems ⋮ A brief survey of methods for solving nonlinear least-squares problems ⋮ Weaker convergence criteria for Traub's method

Uses Software

• minpack

Cites Work

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• Modifications of the interval-Newton-method with improved asymptotic efficiency
• On an iterative algorithm of order 1.839\(\dots\) for solving the nonlinear least squares problems
• Derivative free analogues of the Levenberg-Marquardt and Gauss algorithms for nonlinear least squares approximation
• On an iterative algorithm of order 1.839… for solving nonlinear operator equations^∗)
• Testing Unconstrained Optimization Software
• Short Lecture Session

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