Regularization methods aimed at finding stable approximate solutions are a necessary tool
to tackle inverse and ill-posed problems. Inverse problems arise in a large variety of …

Inverse problems arise in practical applications whenever one needs to deduce unknowns
from observables. This monograph is a valuable contribution to the highly topical field of …

The regularization properties of the total generalized variation (TGV) functional for the
solution of linear inverse problems by means of Tikhonov regularization are studied …

Abstract We consider a Levenberg–Marquardt method for solving nonlinear inverse
problems in Hilbert spaces. The proposed method uses general convex penalty terms to …

The authors study parameter choice strategies for the Tikhonov regularization of nonlinear ill-
posed problems in Banach spaces. The effectiveness of any parameter choice for obtaining …

The electrical capacitance tomography (ECT) technology is a promising tomography
technology that can image the distribution information of permittivity in a measurement …

In the past few years, convergence rates results for Tikhonov regularization of nonlinear ill-
posed problems in Banach spaces have been published, where the classical concept of …

This work presents a thorough theoretical and numerical analysis of the elasticity imaging
inverse problem of tumor identification in the soft tissue of the human body. Beyond the …

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

We describe and analyze a general framework for solving ill-posed operator equations by
minimizing Tikhonov-like functionals. The fitting functional may be non-metric and the …