Abstract. Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems with error-contaminated data. A regularization operator and a suitable value of a regularization parameter have to be chosen. This paper describes an iterative method, based on Golub-Kahan bidiagonalization, for solving large-scale Tikhonov minimization problems with a linear regularizat...

The numerical solution of linear discrete ill-posed problems typically requires regularization. Two of the most popular regularization methods are due to Tikhonov and Lavrentiev. These methods require the choice of a regularization matrix. Common choices include the identity matrix and finite difference approximations of a derivative operator. It is the purpose of the present paper to explore t...

This review provides a comprehensive understanding of regularization theory from different perspectives, emphasizing smoothness and simplicity principles. Using the tools of operator theory and Fourier analysis, it is shown that the solution of the classical Tikhonov regularization problem can be derived from the regularized functional defined by a linear differential (integral) operator in the...

factor of 3.66 by GSENSE (a), JSENSE (b), l1 regularization of the coil sensitivity Fourier transform without (c) and with (e) l1 regularization of the image norm in a wavelet domain, and l1 regularization of the coil sensitivity polynomial transform without (d) and with (f) l1 regularization of the image norm in a wavelet domain. L1-norm regularization of coil sensitivities in non-linear paral...

Abstract. 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 differs from regularization in Hilbert scales in the sense that it is possible that stronger source conditions may lead to weaker convergence rates an...

In this paper we present an iterative algorithm for the solution of regularization problems arising in inverse image processing. The regularization function to be minimized is constituted by two terms, a data fit function and a regularization function, weighted by a regularization parameter. The proposed algorithm solves the minimization problem and estimates the regularization parameter by an ...

This work looks at fitting probabilistic graphical models to data when the structure is not known. The main tool to do this is `1-regularization and the more general group `1-regularization. We describe limited-memory quasi-Newton methods to solve optimization problems with these types of regularizers, and we examine learning directed acyclic graphical models with `1-regularization, learning un...

We discuss adaptive strategies for choosing regularization parameters in TikhonovPhillips regularization of discretized linear operator equations. Two rules turn out to be entirely based on the underlying regularization scheme. Among them only the discrepancy principle allows to search for the optimal regularization parameter from the easiest problem. This possible advantage cannot be used with...

We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed al...

We consider Tikhonov regularization of large linear discrete ill-posed problems with a regularization operator of general form and present an iterative scheme based on a generalized Krylov subspace method. This method simultaneously reduces both the matrix of the linear discrete ill-posed problem and the regularization operator. The reduced problem so obtained may be solved, e.g., with the aid ...