On regularization methods based on dynamic programming techniques

DOI10.1080/00036810701354953zbMath1129.65040arXiv2101.09339OpenAlexW1989470471WikidataQ58140515 ScholiaQ58140515MaRDI QIDQ5297113

Stefan Kindermann, Antonio Leitão

Publication date: 18 July 2007

Published in: Applicable Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.09339

zbMATH Keywords

convergencenumerical exampledynamic programmingregularizationvariational methodsHilbert spaceserror estimationsintegral equation of the first kindminimum norm solutionill-posed linear operator equations

Mathematics Subject Classification ID

Numerical solutions to equations with linear operators (65J10) Numerical methods for ill-posed problems for integral equations (65R30) Fredholm integral equations (45B05) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Linear operators and ill-posed problems, regularization (47A52)

Related Items (1)

Weak-norm posterior contraction rate of the 4DVAR method for linear severely ill-posed problems

Cites Work

• A convergence analysis of the Landweber iteration for nonlinear ill-posed problems
• Convergence rates for Tikhonov regularisation of non-linear ill-posed problems
• On the asymptotical regularization of nonlinear ill-posed problems
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