Displaying similar documents to “On the convergence rate of regularization methods for ill-posed extremal problems”

Local convergence of a multi-step high order method with divided differences under hypotheses on the first derivative

Ioannis K. Argyros, Santhosh George (2017)

Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica

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This paper is devoted to the study of a multi-step method with divided differences for solving nonlinear equations in Banach spaces. In earlier studies, hypotheses on the Fréchet derivative up to the sixth order of the operator under consideration is used to prove the convergence of the method. That restricts the applicability of the method. In this paper we extended the applicability of the sixth-order multi-step method by using only hypotheses on the first derivative of the operator...

Local convergence comparison between two novel sixth order methods for solving equations

Santhosh George, Ioannis K. Argyros (2019)

Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica

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The aim of this article is to provide the local convergence analysis of two novel competing sixth convergence order methods for solving equations involving Banach space valued operators. Earlier studies have used hypotheses reaching up to the sixth derivative but only the first derivative appears in these methods. These hypotheses limit the applicability of the methods. That is why we are motivated to present convergence analysis based only on the first derivative. Numerical examples...

Epigraphical analysis

H. Attouch, R. J.-B. Wets (1989)

Annales de l'I.H.P. Analyse non linéaire

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A note on the convergence rate in regularized stochastic programming

Evgueni I. Gordienko, Yury Gryazin (2021)

Kybernetika

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We deal with a stochastic programming problem that can be inconsistent. To overcome the inconsistency we apply Tikhonov's regularization technique, and, using recent results on the convergence rate of empirical measures in Wasserstein metric, we treat the following two related problems: 1. A choice of regularization parameters that guarantees the convergence of the minimization procedure. 2. Estimation of the rate of convergence in probability. Considering both light and heavy tail distributions...