For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the choice of the regularizationparameters when performing multi-parameter Tikhonov regularization through an iterative...
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For the solution of linear ill-posed problems, in this paper we introduce a simple algorithm for the choice of the regularizationparameters when performing multi-parameter Tikhonov regularization through an iterative scheme. More specifically, the new technique is based on the use of the Arnoldi-Tikhonov method and the discrepancy principle. Numerical experiments arising from the discretization of integral equations are presented.
For solving linear ill-posed problems, regularization methods are required when the right-hand side and/or the operator are corrupted by some noise. In the present paper, regularized solutions are constructed using re...
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For solving linear ill-posed problems, regularization methods are required when the right-hand side and/or the operator are corrupted by some noise. In the present paper, regularized solutions are constructed using regularized total least squares (RTLS) and dual regularized total least squares (DRTLS). We discuss computational aspects and provide order optimal error bounds that characterize the accuracy of the regularized solutions. The results extend earlier results where the operator is exactly given. We also present some numerical experiments, which shed light on the relationship between RTLS, DRTLS, and the standard Tikhonov regularization.
In this paper, we present an inverse problem of identifying the reaction coefficient for time fractional diffusion equations in two dimensional spaces by using boundary Neumann data. It is proved that the forward oper...
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In this paper, we present an inverse problem of identifying the reaction coefficient for time fractional diffusion equations in two dimensional spaces by using boundary Neumann data. It is proved that the forward operator is continuous with respect to the unknown parameter. Because the inverse problem is often ill-posed, regularization strategies are imposed on the least fit-to-data functional to overcome the stability issue. There may exist various kinds of functions to reconstruct. It is crucial to choose a suitable regularization method. We present a multi-parameter regularization L-2 + BV method for the inverse problem. This can extend the applicability for reconstructing the unknown functions. Rigorous analysis is carried out for the inverse problem. In particular, we analyze the existence and stability of regularized variational problem and the convergence. To reduce the dimension in the inversion for numerical simulation, the unknown coefficient is represented by a suitable set of basis functions based on a priori information. A few numerical examples are presented for the inverse problem in time fractional diffusion equations to confirm the theoretic analysis and the efficacy of the different regularization methods. (C) 2018 Elsevier B.V. All rights reserved.
We consider a class of regularization methods for inverse problems where a coupled regularization is employed for the simultaneous reconstruction of data from multiple sources. Applications for such a setting can be f...
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We consider a class of regularization methods for inverse problems where a coupled regularization is employed for the simultaneous reconstruction of data from multiple sources. Applications for such a setting can be found in multi-spectral or multi-modality inverse problems, but also in inverse problems with dynamic data. We consider this setting in a rather general framework and derive stability and convergence results, including convergence rates. In particular, we show how parameter choice strategies adapted to the interplay of different data channels allow to improve upon convergence rates that would be obtained by treating all channels equally. Motivated by concrete applications, our results are obtained under rather general assumptions that allow to include the Kullback-Leibler divergence as data discrepancy term. To simplify their application to concrete settings, we further elaborate several practically relevant special cases in detail. To complement the analytical results, we also provide an algorithmic framework and source code that allows to solve a class of jointly regularized inverse problems with any number of data discrepancies. As concrete applications, we show numerical results for multi-contrast MR and joint MR-PET reconstruction.
Motivated by applications in hyperspectral imaging, we investigate methods for approximating a high-dimensional non-negative matrix Y by a product of two lower-dimensional, non-negative matrices K and X. This so-calle...
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Motivated by applications in hyperspectral imaging, we investigate methods for approximating a high-dimensional non-negative matrix Y by a product of two lower-dimensional, non-negative matrices K and X. This so-called non-negative matrix factorization is based on defining suitable Tikhonov functionals, which combine a discrepancy measure for Y approximate to KX with penalty terms for enforcing additional properties of K and X. The minimization is based on alternating minimization with respect to K and X, where in each iteration step one replaces the original Tikhonov functional by a locally defined surrogate functional. The choice of surrogate functionals is crucial: It should allow a comparatively simple minimization and simultaneously its first-order optimality condition should lead to multiplicative update rules, which automatically preserve non-negativity of the iterates. We review the most standard construction principles for surrogate functionals for Frobenius-norm and Kullback-Leibler discrepancy measures. We extend the known surrogate constructions by a general framework, which allows to add a large variety of penalty terms. The paper finishes by deriving the corresponding alternating minimization schemes explicitly and by applying these methods to MALDI imaging data.
Joint inversion of multiple observation models has important applications in many disciplines including geoscience, image processing and computational biology. One of the methodologies for joint inversion of ill-posed...
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Joint inversion of multiple observation models has important applications in many disciplines including geoscience, image processing and computational biology. One of the methodologies for joint inversion of ill-posed observation equations naturally leads to multi-parameter regularization, which has been intensively studied over the last several years. However, problems such as the choice of multiple regularizationparameters remain unsolved. In the present study, we discuss a rather general approach to the regularization of multiple observation models, based on the idea of the linear aggregation of approximations corresponding to different values of the regularizationparameters. We show how the well-known linear functional strategy can be used for such an aggregation and prove that the error of a constructive aggregator differs from the ideal error value by a quantity of an order higher than the best guaranteed accuracy from the most trustable observation model. The theoretical analysis is illustrated by numerical experiments with simulated data.
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