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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

Based on the variable Hilbert scale interpolation inequality bounds for the error of regularisation methods are derived under range inclusions. In this context, new formulae for the modulus of continuity of the inverse of bounded operators…

Numerical Analysis · Mathematics 2010-05-24 Markus Hegland , Bernd Hofmann

This paper addresses Tikhonov like regularization methods with convex penalty functionals for solving nonlinear ill-posed operator equations formulated in Banach or, more general, topological spaces. We present an approach for proving…

Numerical Analysis · Mathematics 2009-06-19 Jens Geissler , Bernd Hofmann

The article is devoted to the development of numerical methods for solving variational inequalities with relatively strongly monotone operators. We consider two classes of variational inequalities related to some analogs of the Lipschitz…

Optimization and Control · Mathematics 2022-05-25 F. S. Stonyakin , A. A. Titov , D. V. Makarenko , M. S. Alkousa

Dual gradient descent combined with early stopping represents an efficient alternative to the Tikhonov variational approach when the regularizer is strongly convex. However, for many relevant applications, it is crucial to deal with…

Optimization and Control · Mathematics 2023-05-12 Vassilis Apidopoulos , Cesare Molinari , Lorenzo Rosasco , Silvia Villa

We analyze matrix convex functions of a fixed order defined on a real interval by differential methods as opposed to the characterization in terms of divided differences given by Kraus. We obtain for each order conditions for matrix…

Operator Algebras · Mathematics 2007-05-23 Frank Hansen , Jun Tomiyama

This paper is concerned with a novel regularisation technique for solving linear ill-posed operator equations in Hilbert spaces from data that is corrupted by white noise. We combine convex penalty functionals with extreme-value statistics…

Statistics Theory · Mathematics 2012-04-03 Klaus Frick , Philipp Marnitz , Axel Munk

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

For a class of ordinary differential operators $P$ with polynomial coefficients, we give a necessary and sufficient condition for $P$ to be globally regular in $\R$, i.e. $u\in\cS^\prime(\R)$ and $Pu\in\cS(\R)$ imply $u\in \cS(\R)$ (this…

Classical Analysis and ODEs · Mathematics 2015-02-19 Fabio Nicola , Luigi Rodino

We prove regularity results for the unique minimizer of the total variation functional, currently used in image processing analysis since the work by L. Rudin, S. Osher and E. Fatemi. In particular we show that if the source term $f$ is…

Analysis of PDEs · Mathematics 2019-09-05 Alessio Porretta

We introduce and study a mathematical framework for a broad class of regularization functionals for ill-posed inverse problems: Regularization Graphs. Regularization graphs allow to construct functionals using as building blocks linear…

Optimization and Control · Mathematics 2022-09-28 Kristian Bredies , Marcello Carioni , Martin Holler

In the present paper the unconditional convergence and the invertibility of multipliers is investigated. Multipliers are operators created by (frame-like) analysis, multiplication by a fixed symbol, and resynthesis. Sufficient and/or…

Functional Analysis · Mathematics 2012-06-15 D. Stoeva , P. Balazs

We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error. Our approach…

Machine Learning · Statistics 2017-12-15 John Duchi , Hongseok Namkoong

According to a result due to B.T. Polyak, a mapping between Hilbert spaces, which is $C^{1,1}$ around a regular point, carries a ball centered at that point to a convex set, provided that the radius of the ball is small enough. The present…

Optimization and Control · Mathematics 2013-04-01 Amos Uderzo

In this note, we study the complex constant rank condition for differential operators and its implications for coercive differential inequalities. These are inequalities of the form \[ \Vert A u \Vert_{L^p} \leq \Vert \mathscr{A} u…

Analysis of PDEs · Mathematics 2023-02-16 Stefan Schiffer

We present a new approach to convergence rate results for variational regularization. Avoiding Bregman distances and using image space approximation rates as source conditions we prove a nearly minimax theorem showing that the modulus of…

Numerical Analysis · Mathematics 2021-07-07 Philip Miller

We prove weak duality between two recent convex relaxation methods for bounding the optimal value of a constrained variational problem in which the objective is an integral functional. The first approach, proposed by Valmorbida et al. (IEEE…

Optimization and Control · Mathematics 2019-07-01 Giovanni Fantuzzi

A sufficient condition for $\bar{\partial}$ to have closed range is given for pseudoconvex, possibly unbounded domains in $\mathbb{C}^n$.

Complex Variables · Mathematics 2015-02-05 A. -K. Herbig , J. D. McNeal

This paper establishes comprehensive stability results for quasi-variational inequalities (QVIs) under monotone perturbations of the governing operator. We prove strong convergence of both minimal and maximal solutions when sequences of…

Functional Analysis · Mathematics 2025-12-16 M. H. M. Rashid

This paper introduces a novel double regularization scheme for bilevel optimization problems whose lower-level problem is composite and convex, but not necessarily strongly convex, in the lower-level variable. The analysis focuses on the…

Optimization and Control · Mathematics 2026-02-06 Mattia Solla , Johannes O. Royset