A novel two-point gradient method for Regularization of inverse problems in Banach spaces

04/29/2020
by   Gaurav Mittal, et al.
0

In this paper, we introduce a novel two-point gradient method for solving the ill-posed problems in Banach spaces and study its convergence analysis. The method is based on the well known iteratively regularized Landweber iteration method together with an extrapolation strategy. The general formulation of iteratively regularized Landweber iteration method in Banach spaces excludes the use of certain functions such as total variation like penalty functionals, L^1 functions etc. The novel scheme presented in this paper allows to use such non-smooth penalty terms that can be helpful in practical applications involving the reconstruction of several important features of solutions such as piecewise constancy and sparsity. We carefully discuss the choices for important parameters, such as combination parameters and step sizes involved in the design of the method. Additionally, we discuss an example to validate our assumptions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/07/2021

On the asymptotical regularization with convex constraints for inverse problems

In this paper, we consider the asymptotical regularization with convex c...
research
02/01/2020

Tikhonov regularization with oversmoothing penalty for nonlinear statistical inverse problems

In this paper, we consider the nonlinear ill-posed inverse problem with ...
research
02/14/2023

Sparse Bayesian Inference with Regularized Gaussian Distributions

Regularization is a common tool in variational inverse problems to impos...
research
12/22/2015

FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging

The total variation (TV) penalty, as many other analysis-sparsity proble...
research
02/27/2019

Bouligand-Levenberg-Marquardt iteration for a non-smooth ill-posed inverse problem

In this paper, we consider a modified Levenberg--Marquardt method for so...
research
03/02/2019

A unifying representer theorem for inverse problems and machine learning

The standard approach for dealing with the ill-posedness of the training...
research
05/19/2020

Inverse problems with second-order Total Generalized Variation constraints

Total Generalized Variation (TGV) has recently been introduced as penalt...

Please sign up or login with your details

Forgot password? Click here to reset