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About the Balancing Principle for Choice of the Regularization Parameter (Q3405615)

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Language	Label	Description	Also known as
English	About the Balancing Principle for Choice of the Regularization Parameter	scientific article

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scholarly article

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About the Balancing Principle for Choice of the Regularization Parameter (English)

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Numerical Functional Analysis and Optimization

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publication date

10 February 2010

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zbMATH Keywords

balancing principle

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ill-posed problem

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parameter choice

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regularization

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describes a project that uses

Regularization tools

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1080/01630560903393139

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A Lepskij-type stopping rule for regularized Newton methods

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Iteratively Regularized Gauss–Newton Method for Nonlinear Inverse Problems with Random Noise

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Local solutions to inverse problems in geodesy. The impact of the noise covariance structure upon the accuracy of estimation

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Optimal regularization for ill-posed problems in metric spaces

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Regularization without preliminary knowledge of smoothness and error behaviour

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The balancing principle for the regularization of elliptic Cauchy problems

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A Carleman estimate and the balancing principle in the quasi-reversibility method for solving the Cauchy problem for the Laplace equation

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Adaptive kernel methods using the balancing principle

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An a posteriori parameter choice for ordinary and iterated Tikhonov regularization of ill-posed problems leading to optimal convergence rates

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Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems

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Use of extrapolation in regularization methods

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On the balancing principle for some problems of numerical analysis

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Numerical differentiation from a viewpoint of regularization theory

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Sparse recovery by the standard Tikhonov method

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An analysis of Tikhonov regularization for nonlinear ill-posed problems under a general smoothness assumption

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The Lepskii principle revisited

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Discretization strategy for linear ill-posed problems in variable Hilbert scales

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Geometry of linear ill-posed problems in variable Hilbert scales

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Regularization of some linear ill-posed problems with discretized random noisy data

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The discretized discrepancy principle under general source conditions

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Interpolation in variable Hilbert scales with application to inverse problems

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On the Adaptive Selection of the Parameter in Regularization of Ill-Posed Problems

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ON NUMERICAL REALIZATION OF QUASIOPTIMAL PARAMETER CHOICES IN (ITERATED) TIKHONOV AND LAVRENTIEV REGULARIZATION

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On the quasioptimal regularization parameter choices for solving ill-posed problems

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The use of monotonicity for choosing the regularization parameter in ill-posed problems

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Identifiers

zbMATH Open document ID

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10.1080/01630560903393139

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:3405615

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