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On comparison of accuracy of approximate solutions of operator equations with noisy data

AIP Conference Proceedings, 2016, Vol.1738 (1)

2016

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Q-Curve and Area Rules for Choosing Heuristic Parameter in Tikhonov Regularization

Mathematics (Basel), 2020-07, Vol.8 (7), p.1166

2020

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Semi-heuristic parameter choice rules for Tikhonov regularisation with operator perturbations

Journal of inverse and ill-posed problems, 2019-02, Vol.27 (1), p.117-131

2019

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Heuristic Parameter Choice in Tikhonov Method from Minimizers of the Quasi-Optimality Function

New Trends in Parameter Identification for Mathematical Models, 2018, p.227-244

2018

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On the quasi-optimal rules for the choice of the regularization parameter in case of a noisy operator

Advances in computational mathematics, 2012-02, Vol.36 (2), p.221-233

2012

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On the Self-Regularization of Ill-Posed Problems by the Least Error Projection Method

Mathematical modelling and analysis, 2014-06, Vol.19 (3), p.299-308

2014

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On Self-regularization of Ill-Posed Problems in Banach Spaces by Projection Methods

New Trends in Parameter Identification for Mathematical Models, 2018, p.89-105

2018

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Analysis of Approximation by Linear Operators on Variable L ρ p ( · ) Spaces and Applications in Learning Theory

Abstract and applied analysis, 2014, Vol.2014 (2014), p.1-10

2014

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Extrapolation of Tikhonov and Lavrentiev regularization methods

Journal of physics. Conference series, 2008-11, Vol.135 (1), p.012048

2008

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Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem

Abstract and Applied Analysis, 2014, Vol.2014 (2014), p.928-936-1489

2014

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New rule for choice of the regularization parameter in (iterated) tikhonov method

Mathematical modelling and analysis, 2009-01, Vol.14 (2), p.187-198

2009

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On numerical realization of quasioptimal parameter choices in (iterated) Tikhonov and Lavrentiev regularization

Mathematical modelling and analysis, 2009-01, Vol.14 (1), p.99-108

2009

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Regularization by discretization in Banach spaces

Inverse problems, 2016-03, Vol.32 (3), p.35004-35031

2016

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Extrapolation of Tikhonov regularization method

Mathematical modelling and analysis, 2010-01, Vol.15 (1), p.55-68

2010

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A family of rules for parameter choice in Tikhonov regularization of ill-posed problems with inexact noise level

Journal of computational and applied mathematics, 2012-02, Vol.236 (8), p.2146-2157

2012

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On rules for stopping the conjugate gradient type methods in ill-posed problems

Mathematical modelling and analysis, 2007-01, Vol.12 (1), p.61-70

2007

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Erratum: Regularization by discretization in Banach spaces (2016 Inverse Problems 32 035004)

Inverse problems, 2016-06, Vol.32 (6), p.69501

2016

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Comparison of parameter choices in regularization algorithms in case of different information about noise level

Calcolo, 2011-03, Vol.48 (1), p.47-59

2011

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A family of rules for the choice of the regularization parameterin the Lavrentiev method in the case of rough estimate of the noise level ofthe data

Journal of inverse and ill-posed problems, 2012-12, Vol.20 (5-6), p.831-854

2012

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Ill-Posed and Non-Classical Problems of Mathematical Physics and Analysis: Proceedings of the International Conference, Samarkand, Uzbekistan

Reprint 2014 edition., 2014

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Monotonicity of error of regularized solution and its use for parameter choice

Inverse problems in science and engineering, 2014-01, Vol.22 (1), p.10-30

2014

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A family of rules for the choice of the regularization parameter in the Lavrentiev method in the case of rough estimate of the noise level of the data

Journal of inverse and ill-posed problems, 2012-12, Vol.20 (5-6), p.831-854

2012

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Monotonicity of error of regularized solution and its use for parameter choice

Applied mathematics in science and engineering, 2014-01, Vol.22 (1), p.10

2014

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Foreword

Numerical functional analysis and optimization, 2009-12, Vol.30 (9-10), p.895-895

2009

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Foreword

Numerical functional analysis and optimization, 2009-12, Vol.30 (9-10), p.895-895

2009

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On comparison of accuracy of approximate solutions of operator equations with noisy data

Q-Curve and Area Rules for Choosing Heuristic Parameter in Tikhonov Regularization

Semi-heuristic parameter choice rules for Tikhonov regularisation with operator perturbations

Heuristic Parameter Choice in Tikhonov Method from Minimizers of the Quasi-Optimality Function

On the quasi-optimal rules for the choice of the regularization parameter in case of a noisy operator

On the Self-Regularization of Ill-Posed Problems by the Least Error Projection Method

On Self-regularization of Ill-Posed Problems in Banach Spaces by Projection Methods

Analysis of Approximation by Linear Operators on Variable L ρ p ( · ) Spaces and Applications in Learning Theory

Extrapolation of Tikhonov and Lavrentiev regularization methods

Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem

New rule for choice of the regularization parameter in (iterated) tikhonov method

On numerical realization of quasioptimal parameter choices in (iterated) Tikhonov and Lavrentiev regularization

Regularization by discretization in Banach spaces

Extrapolation of Tikhonov regularization method

A family of rules for parameter choice in Tikhonov regularization of ill-posed problems with inexact noise level

On rules for stopping the conjugate gradient type methods in ill-posed problems

Erratum: Regularization by discretization in Banach spaces (2016 Inverse Problems 32 035004)

Comparison of parameter choices in regularization algorithms in case of different information about noise level

A family of rules for the choice of the regularization parameterin the Lavrentiev method in the case of rough estimate of the noise level ofthe data

Ill-Posed and Non-Classical Problems of Mathematical Physics and Analysis: Proceedings of the International Conference, Samarkand, Uzbekistan

Monotonicity of error of regularized solution and its use for parameter choice

A family of rules for the choice of the regularization parameter in the Lavrentiev method in the case of rough estimate of the noise level of the data

Monotonicity of error of regularized solution and its use for parameter choice

Foreword

Foreword

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