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In this paper we derive higher order convergence rates in terms of the Bregman distance for Tikhonov like convex regularisation for linear operator equations on Banach spaces. The approach is based on the idea of variational inequalities,…

Optimization and Control · Mathematics 2011-07-14 Markus Grasmair

This paper deals with Tikhonov regularization for linear and nonlinear ill-posed operator equations with wavelet Besov norm penalties. We focus on $B^0_{p,1}$ penalty terms which yield estimators that are sparse with respect to a wavelet…

Numerical Analysis · Mathematics 2019-09-04 Thorsten Hohage , Philip Miller

Tikhonov regularization with square-norm penalty for linear forward operators has been studied extensively in the literature. However, the results on convergence theory are based on technical proofs and difficult to interpret. It is also…

Numerical Analysis · Mathematics 2021-07-07 Daniel Gerth

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

In this paper we deal with linear inverse problems and convergence rates for Tikhonov regularization. We consider regularization in a scale of Banach spaces, namely the scale of Besov spaces. We show that regularization in Banach scales…

Functional Analysis · Mathematics 2009-11-13 Dirk Lorenz , Dennis Trede

This paper deals with Tikhonov regularization for linear and nonlinear ill-posed operator equations with wavelet Besov norm penalties. We show order optimal rates of convergence for finitely smoothing operators and for the backwards heat…

Numerical Analysis · Mathematics 2019-04-03 Frederic Weidling , Benjamin Sprung , Thorsten Hohage

Measuring the error by an l^1-norm, we analyze under sparsity assumptions an l^0-regularization approach, where the penalty in the Tikhonov functional is complemented by a general stabilizing convex functional. In this context, ill-posed…

Numerical Analysis · Mathematics 2018-10-23 Wei Wang , Shuai Lu , Bernd Hofmann , Jin Cheng

To solve convex optimization problems with a noisy gradient input, we analyze the global behavior of subgradient-like flows under stochastic errors. The objective function is composite, being equal to the sum of two convex functions, one…

Optimization and Control · Mathematics 2025-06-05 Rodrigo Maulen-Soto , Jalal Fadili , Hedy Attouch

We propose and analyze variational source conditions (VSC) for the Tikhonov regularization method with Lp-norm penalties for a general ill-posed operator equation in a Banach space. Our analysis is based on the use of the celebrated…

Functional Analysis · Mathematics 2021-02-22 De-Han Chen , Irwin Yousept

The Tikhonov regularization of linear ill-posed problems with an $\ell^1$ penalty is considered. We recall results for linear convergence rates and results on exact recovery of the support. Moreover, we derive conditions for exact support…

Functional Analysis · Mathematics 2015-05-18 Dirk A. Lorenz , Stefan Schiffler , Dennis Trede

Although the \emph{residual method}, or \emph{constrained regularization}, is frequently used in applications, a detailed study of its properties is still missing. This sharply contrasts the progress of the theory of Tikhonov…

Optimization and Control · Mathematics 2012-12-06 Markus Grasmair , Markus Haltmeier , Otmar Scherzer

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

Motivated by a seminal paper of professor M. Z. Nashed published in 1987 on classification of ill-posed linear operator equations and distinguishing two types of ill-posedness in Banach and Hilbert spaces, we present, illustrate and justify…

Functional Analysis · Mathematics 2025-11-11 Jens Flemming , Bernd Hofmann

The existence of a solution, convergence and stability of the penalty method for variational inequalities with nonsmooth unbounded uniformly and properly monotone operators in Banach spase $B$ are investigated. All the objects of the…

funct-an · Mathematics 2008-02-03 Ya. I. Alber

Previously unknown estimates of uniform continuity of projection operators in Banach space have been obtained. They can be used in the investigations of approximation methods, in particular, the method of quasisolutions, methods of…

funct-an · Mathematics 2008-02-03 Ya. I. Alber , A. I. Notik

Ensemble Kalman inversion is a parallelizable methodology for solving inverse or parameter estimation problems. Although it is based on ideas from Kalman filtering, it may be viewed as a derivative-free optimization method. In its most…

Numerical Analysis · Mathematics 2024-12-20 Neil K. Chada , Andrew M. Stuart , Xin T. Tong

We study maximal regularity in interpolation spaces for the sum of three closed linear operators on a Banach space, and we apply the abstract results to obtain Besov and H\"older maximal regularity for complete second order Cauchy problems…

Functional Analysis · Mathematics 2014-04-14 Charles J. K. Batty , Ralph Chill , Sachi Srivastava

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 a modified Tikhonov-type functional for the solution of ill-posed nonlinear inverse problems. Motivated by applications in the field of production engineering, we allow small deviations in the solution, which are modeled through…

Numerical Analysis · Mathematics 2020-07-14 Iwona Piotrowska-Kurczewski , Georgia Sfakianaki

Our focus is on the stable approximate solution of linear operator equations based on noisy data by using $\ell^1$-regularization as a sparsity-enforcing version of Tikhonov regularization. We summarize recent results on situations where…

Functional Analysis · Mathematics 2017-11-27 Daniel Gerth , Bernd Hofmann