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Autori: Emil Catinas
Editorial: Journal of Optimization Theory and Applications, 108, no. 3, p.543-570, 2001.
Inexact Newton methods are variant of the Newton method in which each step satisfies only approximately the linear system (Ref. 1). The local convergence theory given by the authors of Ref. 1 and most of the results based on it consider the error terms as being provided only by the fact that the linear systems are not solved exactly. The few existing results for the general case (when some perturbed linear systems are considered, which in turn are not solved exactly) do not offer explicit formulas in terms of the perturbations and residuals. We extend this local convergence theory to the general case, characterizing the rate of convergence in terms of the perturbations and residuals.
The Newton iterations are then analyzed when, at each step, an approximate solution of the linear system is determined by the following Krylov solvers based on backward error minimization properties: GMRES, GMBACK, MINPERT. We obtain results concerning the following topics: monotone properties of the errors in these Newton–Krylov iterates when the initial guess is taken 0 in the Krylov algorithms; control of the convergence orders of the Newton–Krylov iterations by the magnitude of the backward errors of the approximate steps; similarities of the asymptotical behavior of GMRES and MINPERT when used in a converging Newton method. At the end of the paper, the theoretical results are verified on some numerical examples.
Cuvinte cheie: sisteme neliniare, metode Newton (inexacte), metode Newton perturbate (rezolvate inexact), ordine de convergenta, sisteme liniare, erori retrograde, metode Krylov (GMRES, GMBACK, MINPERT), metode Newton–Krylov // Nonlinear systems, (inexact) Newton methods, (inexact) perturbed Newton methods, convergence orders, linear systems, backward errors, Krylov methods (GMRES, GMBACK, MINPERT), Newton–Krylov methods