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In this paper, we deal with nonlinear ill-posed problems involving monotone operators and consider Lavrentiev's regularization method. This approach, in contrast to Tikhonov's regularization method, does not make use of the adjoint of the…

Numerical Analysis · Mathematics 2016-04-20 Bernd Hofmann , Barbara Kaltenbacher , Elena Resmerita

We consider perturbed nonlinear ill-posed equations in Hilbert spaces, with operators that are monotone on a given closed convex subset. A simple stable approach is Lavrentiev regularization, but existence of solutions of the regularized…

Numerical Analysis · Mathematics 2018-06-05 Robert Plato , Bernd Hofmann

In the literature on singular perturbation (Lavrentiev regularization) for the stable approximate solution of operator equations with monotone operators in the Hilbert space the phenomena of conditional stability and local well-posedness…

Numerical Analysis · Mathematics 2016-11-23 Radu Ioan Bot , Bernd Hofmann

This paper provides a new regularization method which is particularly suitable for linear exponentially ill-posed problems. Under logarithmic source conditions (which have a natural interpretation in terms of Sobolev spaces in the…

Numerical Analysis · Mathematics 2020-07-08 Walter Cedric Simo Tao Lee

Convergence rates results for Tikhonov regularization of nonlinear ill-posed operator equations in abstract function spaces require the handling of both smoothness conditions imposed on the solution and structural conditions expressing the…

Numerical Analysis · Mathematics 2009-09-29 Radu Ioan Bot , Bernd Hofmann

In this work we consider, in a Banach space framework, the regularization of linear ill-posed problems. Our focus is on the recovery of solutions that have a logarithmic source representation. Such cases typically occur in exponentially…

Numerical Analysis · Mathematics 2025-09-09 Robert Plato

This paper deals with Lavrentiev regularization for solving linear ill-posed problems, mostly with respect to accretive operators on Hilbert spaces. We present converse and saturation results which are an important part in regularization…

Numerical Analysis · Mathematics 2016-07-19 Robert Plato

Based on the joint bidiagonalization process of a large matrix pair $\{A,L\}$, we propose and develop an iterative regularization algorithm for the large scale linear discrete ill-posed problems in general-form regularization: $\min\|Lx\| \…

Numerical Analysis · Mathematics 2020-07-21 Zhongxiao Jia , Yanfei Yang

We study Tikhonov regularization for solving ill--posed operator equations where the solutions are functions defined on surfaces. One contribution of this paper is an error analysis of Tikhonov regularization which takes into account…

Numerical Analysis · Mathematics 2016-12-15 Guozhi Dong , Bert Juettler , Otmar Scherzer , Thomas Takacs

A general method for solving nonlinear ill-posed problems is developed. The method consists of solving a Cauchy problem with a regularized operator and proving that the solution of this problem tends, as time grows, to a solution of the…

Mathematical Physics · Physics 2007-05-23 R. Airapetyan , A. G. Ramm , A. Smirnova

We study the application of the Augmented Lagrangian Method to the solution of linear ill-posed problems. Previously, linear convergence rates with respect to the Bregman distance have been derived under the classical assumption of a…

Numerical Analysis · Mathematics 2015-06-04 Klaus Frick , Markus Grasmair

In this paper, we consider Nesterov's Accelerated Gradient method for solving Nonlinear Inverse and Ill-Posed Problems. Known to be a fast gradient-based iterative method for solving well-posed convex optimization problems, this method also…

Numerical Analysis · Mathematics 2020-01-13 Simon Hubmer , Ronny Ramlau

In this paper, we address the problem of approximating solutions of ill-posed problems using mollification. We quickly review existing mollification regularization methods and provide two new approximate solutions to a general ill-posed…

Numerical Analysis · Mathematics 2020-05-05 Walter Cedric Simo Tao Lee

These lecture notes for a graduate class present the regularization theory for linear and nonlinear ill-posed operator equations in Hilbert spaces. Covered are the general framework of regularization methods and their analysis via spectral…

Functional Analysis · Mathematics 2021-02-09 Christian Clason

We consider determining $\R$-minimizing solutions of linear ill-posed problems $A x = y$, where $A: {\mathscr X} \to {\mathscr Y}$ is a bounded linear operator from a Banach space ${\mathscr X}$ to a Hilbert space ${\mathscr Y}$ and…

Numerical Analysis · Mathematics 2023-07-05 Qinian Jin , Wei Wang

This paper investigates using the conjugate gradient iterative solver for ill-posed problems. We show that preconditioner and Tikhonov-regularization work in conjunction. In particular when they employ the same symmetric positive…

Numerical Analysis · Mathematics 2025-12-12 Ahmed Chabib , Jean-Francois Witz , Vincent Magnier , Pierre Gosselet

We consider the method of quasi-solutions (also referred to as Ivanov regularization) for the regularization of linear ill-posed problems in non-reflexive Banach spaces. Using the equivalence to a metric projection onto the image of the…

Optimization and Control · Mathematics 2018-10-09 Christian Clason , Andrej Klassen

Tikhonov regularization is one of the most commonly used methods of regularization of ill-posed problems. In the setting of finite element solutions of elliptic partial differential control problems, Tikhonov regularization amounts to…

Numerical Analysis · Mathematics 2016-09-19 Erik Burman , Peter Hansbo , Mats Larson

We investigate a level-set type method for solving ill-posed problems, with the assumption that the solutions are piecewise, but not necessarily constant functions with unknown level sets and unknown level values. In order to get stable…

Numerical Analysis · Mathematics 2012-10-30 Adriano De Cezaro

This paper is concerned with exponentially ill-posed operator equations with additive impulsive noise on the right hand side, i.e. the noise is large on a small part of the domain and small or zero outside. It is well known that Tikhonov…

Numerical Analysis · Mathematics 2016-03-18 Claudia König , Frank Werner , Thorsten Hohage
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