Foundations of Computational Mathematics

, Volume 18, Issue 2, pp 459–508 | Cite as

Regularized Kernel-Based Reconstruction in Generalized Besov Spaces

  • Michael Griebel
  • Christian Rieger
  • Barbara Zwicknagl


We present a theoretical framework for reproducing kernel-based reconstruction methods in certain generalized Besov spaces based on positive, essentially self-adjoint operators. An explicit representation of the reproducing kernel is given in terms of an infinite series. We provide stability estimates for the kernel, including inverse Bernstein-type estimates for kernel-based trial spaces, and we give condition estimates for the interpolation matrix. Then, a deterministic error analysis for regularized reconstruction schemes is presented by means of sampling inequalities. In particular, we provide error bounds for a regularized reconstruction scheme based on a numerically feasible approximation of the kernel. This allows us to derive explicit coupling relations between the series truncation, the regularization parameters and the data set.


Reproducing kernels A priori error analysis Generalized Besov spaces Feasible reconstruction schemes Spline smoothing 

Mathematics Subject Classification

41A17 41A25 41A58 42A82 62G08 



We are grateful for the comments and suggestions of the anonymous referees. The authors acknowledge support of the Deutsche Forschungsgemeinschaft (DFG) through the Sonderforschungsbereich 1060: The Mathematics of Emergent Effects.


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Copyright information

© SFoCM 2017

Authors and Affiliations

  • Michael Griebel
    • 1
    • 2
  • Christian Rieger
    • 2
  • Barbara Zwicknagl
    • 3
    • 4
  1. 1.Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAISchloss BirlinghovenSankt AugustinGermany
  2. 2.Institut für Numerische SimulationUniversität BonnBonnGermany
  3. 3.Institut für Angewandte MathematikUniversität BonnBonnGermany
  4. 4.Institut für Mathematik, Universität WürzburgWürzburgGermany

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