An Improved Tikhonov-Regularized Variable Projection Algorithm for Separable Nonlinear Least Squares
Axioms, 2021 In this work, we investigate the ill-conditioned problem of a separable, nonlinear least squares model by using the variable projection method. Based on the truncated singular value decomposition method and the Tikhonov regularization method, we propose ...
Hua Guo, Guolin Liu, Luyao Wang
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Mathematical and numerical modeling of inverse heat conduction problem [PDF]
INCAS Bulletin, 2014 The present paper refers to the assessment of three numerical methods for solving the inverse heat conduction problem: the Alifanov’s iterative regularization method, the Tikhonov local regularization method and the Tikhonov equation regularization ...
Sterian DANAILA, Alina-Ioana CHIRA
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A Combined Use of TSVD and Tikhonov Regularization for Mass Flux Solution in Tibetan Plateau
Remote Sensing, 2020 Limited by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) measurement principle and sensors, the spatial resolution of mass flux solutions is about 2–3° in mid-latitudes at monthly intervals.
Tianyi Chen +3 more
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Fractional Regularized Distorted Born Iterative Method for Permittivity Reconstruction [PDF]
Radioengineering, 2022 In this paper, we propose a fractional regularized distorted Born iterative method (DBIM) to solve non-linear ill-posed problems of microwave imaging.
A. D. Magdum +2 more
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DIAS: A Data-Informed Active Subspace Regularization Framework for Inverse Problems
Computation, 2022 This paper presents a regularization framework that aims to improve the fidelity of Tikhonov inverse solutions. At the heart of the framework is the data-informed regularization idea that only data-uninformed parameters need to be regularized, while the ...
Hai Nguyen +2 more
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A new interpretation of (Tikhonov) regularization [PDF]
Inverse Problems, 2021 Abstract Tikhonov regularization with square-norm penalty for linear forward operators has been studied extensively in the literature. However, the results on convergence theory are based on technical proofs and sometimes difficult to interpret.
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Multi-parameter Tikhonov regularization [PDF]
Methods and Applications of Analysis, 2011 We study multi-parameter Tikhonov regularization, i.e., with multiple penalties. Such models are useful when the sought-for solution exhibits several distinct features simultaneously. Two choice rules, i.e., discrepancy principle and balancing principle, are studied for choosing an appropriate (vector-valued) regularization parameter, and some ...
Kazufumi Ito +2 more
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On the convergence of algorithms with Tikhonov regularization terms [PDF]
Optimization Letters, 2020 We consider the strongly convergent modified versions of the Krasnosel'ski\uı-Mann, the forward-backward and the Douglas-Rachford algorithms with Tikhonov regularization terms, introduced by Radu Boţ, Ernö Csetnek and Dennis Meier. We obtain quantitative information for these modified iterations, namely rates of asymptotic regularity and metastability.
Bruno Dinis, Pedro Pinto 0003
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Intelligent Particle Swarm Optimization Method for Parameter Selecting in Regularization Method for Integral Equation [PDF]
BIO Web of ConferencesWe use the Tikhonov method as a regularization technique for solving the integral equation of the first kind with noisy and noise-free data. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Intelligent ...
Al-Mahdawi H.K. +5 more
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An Adaptive Image Denoising Model Based on Tikhonov and TV Regularizations
Advances in Multimedia, 2014 To avoid the staircase artifacts, an adaptive image denoising model is proposed by the weighted combination of Tikhonov regularization and total variation regularization.
Kui Liu, Jieqing Tan, Benyue Su
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