Iterative regularization for constrained minimization formulations of nonlinear inverse problems

01/14/2021
by   Barbara Kaltenbacher, et al.
0

In this paper we the formulation of inverse problems as constrained minimization problems and their iterative solution by gradient or Newton type. We carry out a convergence analysis in the sense of regularization methods and discuss applicability to the problem of identifying the spatially varying diffusivity in an elliptic PDE from different sets of observations. Among these is a novel hybrid imaging techology known as impedance acoustic tomography, for which we provide numerical experiments.

READ FULL TEXT

page 28

page 29

page 30

page 32

research
12/21/2020

Sparsity regularization for inverse problems with nullspaces

We study a weighted ℓ^1-regularization technique for solving inverse pro...
research
09/04/2021

On the inverse problem of vibro-acoustography

The aim of this paper is to put the problem of vibroacoustic imaging int...
research
07/27/2022

On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data

For numerous parameter and state estimation problems, assimilating new d...
research
02/19/2020

Relaxed Gauss-Newton methods with applications to electrical impedance tomography

As second-order methods, Gauss–Newton-type methods can be more effective...
research
03/30/2022

Efficient Computation of Extended Surface Sources

Source extension is a reformulation of inverse problems in wave propagat...
research
10/07/2019

All-at-once versus reduced iterative methods for time dependent inverse problems

In this paper we investigate all-at-once versus reduced regularization o...
research
04/27/2020

Some application examples of minimization based formulations of inverse problems and their regularization

In this paper we extend a recent idea of formulating and regularizing in...

Please sign up or login with your details

Forgot password? Click here to reset