Local convergence of the Gauss–Newton methods for constrained nonlinear equations

Abstract We derive, from unified principles, local convergence and rate-of-convergence results for the classical Gauss-Newton method in a variety of settings. These include overdetermined and underdetermined systems of equations, constrained and unconstrained, possibly with inexact solution of subproblems, as well as the projected variant in the constrained case. Moreover, by a counter-example we show that contrary to some results claimed in the literature, the projected Gauss-Newton method in general does not converge superlinearly under any reasonable assumptions. We then establish its linear rate of convergence.

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Publication Details

Journal
Computational Optimization and Applications
Published
2026-06-16
DOI
https://doi.org/10.1007/s10589-026-00801-4
Primary Topic
Iterative Methods for Nonlinear Equations
Type
article
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article

Local convergence of the Gauss–Newton methods for constrained nonlinear equations

A. F. Izmailov, M. V. Solodov
Computational Optimization and Applications
Iterative Methods for Nonlinear Equations
article

Local convergence of the Gauss–Newton methods for constrained nonlinear equations

A. F. Izmailov, M. V. Solodov
article en

Abstract

Abstract We derive, from unified principles, local convergence and rate-of-convergence results for the classical Gauss-Newton method in a variety of settings. These include overdetermined and underdetermined systems of equations, constrained and unconstrained, possibly with inexact solution of subproblems, as well as the projected variant in the constrained case. Moreover, by a counter-example we show that contrary to some results claimed in the literature, the projected Gauss-Newton method in general does not converge superlinearly under any reasonable assumptions. We then establish its linear rate of convergence.

Computational Optimization and Applications
Instituto Nacional de Matemática Pura e Aplicada (BR), Lomonosov Moscow State University (RU)
Reduced inequalities
Openalex Percentile: Top 5%
Iterative Methods for Nonlinear Equations
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