Skip to main content

Generalized Sparse Grid Interpolation Based on the Fast Discrete Fourier Transform

  • Conference paper
  • First Online:
Book cover Sparse Grids and Applications - Munich 2018

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 144))

Abstract

In (Griebel and Hamaekers, Fast discrete Fourier transform on generalized sparse grids, Sparse grids and Applications, volume 97 of Lecture Notes in Computational Science and Engineering, pages 75–108, Springer, 2014), an algorithm for trigonometric interpolation involving only so-called standard information of multivariate functions on generalized sparse grids has been suggested and a study on its application for the interpolation of functions in periodic Sobolev spaces of dominating mixed smoothness has been presented. In this complementary paper, we now give a slight modification of the proofs, which yields an extension from the pairing \((\mathcal {H}^{s},\mathcal {H}_{{mix}}^t)\) to the more general pairing \((\mathcal {H}^{s},\mathcal {H}_{{mix}}^{t,r})\) and which in addition results in an improved estimate for the interpolation error. The improved (constructive) upper bound is in particular consistent with the lower bound for sampling on regular sparse grids with r = 0 and s = 0 given in (Dũng, Acta Mathematica Vietnamica, 43(1):83–110, 2018; Dũng et al., Hyperbolic Cross Approximation, Advanced Courses in Mathematics - CRM Barcelona, Birkhäuser/Springer, 2018).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. B. Bohn and M. Griebel. An adaptive sparse grid approach for time series predictions. In J. Garcke and M. Griebel, editors, Sparse grids and applications, volume 88 of Lecture Notes in Computational Science and Engineering, pages 1–30. Springer, 2012.

    Google Scholar 

  2. H. Bungartz and M. Griebel. Sparse grids. Acta Numerica, 13:1–123, 2004.

    Article  MathSciNet  Google Scholar 

  3. D. Dũng. B-spline quasi-interpolant representations and sampling recovery of functions with mixed smoothness. Journal of Complexity, 27(6):541–567, 2011.

    Article  MathSciNet  Google Scholar 

  4. D. Dũng. B-spline quasi-interpolation sampling representation and sampling recovery in Sobolev spaces of mixed smoothness. Acta Mathematica Vietnamica, 43(1):83–110, 2018.

    Article  MathSciNet  Google Scholar 

  5. D. Dũng, V. Temlyakov, and T. Ullrich. Hyperbolic Cross Approximation. Advanced Courses in Mathematics - CRM Barcelona. Birkhäuser/Springer, 2018.

    Google Scholar 

  6. D. Dũng and T. Ullrich. N-widths and ε-dimensions for high-dimensional approximations. Foundations of Computational Mathematics, 13:965–1003, 2013.

    Article  MathSciNet  Google Scholar 

  7. T. Gerstner and M. Griebel. Dimension-adaptive tensor-product quadrature. Computing, 71(1):65–87, 2003.

    Article  MathSciNet  Google Scholar 

  8. M. Griebel and J. Hamaekers. Tensor product multiscale many-particle spaces with finite-order weights for the electronic Schrödinger equation. Zeitschrift für Physikalische Chemie, 224:527–543, 2010.

    Article  Google Scholar 

  9. M. Griebel and J. Hamaekers. Fast discrete Fourier transform on generalized sparse grids. In Sparse grids and Applications, volume 97 of Lecture Notes in Computational Science and Engineering, pages 75–108. Springer, 2014.

    Google Scholar 

  10. M. Griebel and M. Holtz. Dimension-wise integration of high-dimensional functions with applications to finance. Journal of Complexity, 26:455–489, 2010.

    Article  MathSciNet  Google Scholar 

  11. M. Griebel and S. Knapek. Optimized tensor-product approximation spaces. Constructive Approximation, 16(4):525–540, 2000.

    Article  MathSciNet  Google Scholar 

  12. M. Griebel and S. Knapek. Optimized general sparse grid approximation spaces for operator equations. Mathematics of Computation, 78:2223–2257, 2009.

    Article  MathSciNet  Google Scholar 

  13. K. Hallatschek. Fourier-transform on sparse grids with hierarchical bases. Numerische Mathematik, 63(1):83–97, 1992.

    Article  MathSciNet  Google Scholar 

  14. J. Hamaekers. Sparse Grids for the Electronic Schrödinger Equation: Construction and Application of Sparse Tensor Product Multiscale Many-Particle Spaces with Finite-Order Weights for Schrödinger’s Equation. Südwestdeutscher Verlag für Hochschulschriften, Saarbrücken, 2010.

    Google Scholar 

  15. A. Hinrichs, E. Novak, and J. Vybíral. Linear information versus function evaluations for L 2-approximation. Journal of Approximation Theory, 153(1):97–107, 2008.

    Article  MathSciNet  Google Scholar 

  16. J. Jakeman and S. Roberts. Local and dimension adaptive stochastic collocation for uncertainty quantification. In J. Garcke and M. Griebel, editors, Sparse Grids and Applications, pages 181–203. Springer, 2013.

    Google Scholar 

  17. L. Kämmerer, T. Ullrich, and T. Volkmer. Worst case recovery guarantees for least squares approximation using random samples. arXiv preprint arXiv:1911.10111, 2019.

    Google Scholar 

  18. S. Knapek. Approximation und Kompression mit Tensorprodukt-Multiskalenräumen. Dissertation, University of Bonn, 2000.

    Google Scholar 

  19. S. Knapek. Hyperbolic cross approximation of integral operators with smooth kernel. 2000. Technical Report 665, SFB 256, University of Bonn.

    Google Scholar 

  20. H. Kreusler and H. Yserentant. The mixed regularity of electronic wave functions in fractional order and weighted Sobolev spaces. Numerische Mathematik, 121(4):781–802, 2012.

    Article  MathSciNet  Google Scholar 

  21. D. Krieg and M. Ullrich. Function values are enough for L 2-approximation. arXiv preprint arXiv:1905.02516, 2019.

    Google Scholar 

  22. T. Kühn, W. Sickel, and T. Ullrich. Approximation of mixed order Sobolev functions on the d-torus: asymptotics, preasymptotics, and d-dependence. Constructive Approximation, 42(3):353–398, 2015.

    Article  MathSciNet  Google Scholar 

  23. F. Kupka. Sparse Grid Spectral Methods for the Numerical Solution of Partial Differential Equations with Periodic Boundary Conditions. PhD thesis, University of Wien, 1997.

    Google Scholar 

  24. F. Kupka. Sparse grid spectral methods and some results from approximation theory. In C. Lai, P. Bjørstad, M. Cross, and O. Widlund, editors, Proceedings of the 11th International Conference on Domain Decomposition Methods in Greenwich, pages 57–64, England, 1999.

    Google Scholar 

  25. E. Novak and H. Wozniakowski. On the power of function values for the approximation problem in various settings. Surveys in Approximation Theory, 6:1–23, 2011.

    MathSciNet  MATH  Google Scholar 

  26. W. Sickel and T. Ullrich. Spline interpolation on sparse grids. Applicable Analysis, 90(3–4):337–383, 2011.

    Article  MathSciNet  Google Scholar 

  27. V. Temlyakov. Approximation of Periodic Functions. Nova Science, New York, 1993.

    MATH  Google Scholar 

  28. H. Triebel. Bases in Function Spaces, Sampling, Discrepancy, Numerical Integration, volume 11 of EMS Tracts in Mathematics. European Mathematical Society, 2010.

    Google Scholar 

  29. V. Velikov. Fast Sparse Pseudo-spectral Methods for High-dimensional Problems. Master thesis, Institute for Numerical Simulation, Universität Bonn, 2016.

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Collaborative Research Centre 1060 of the Deutsche Forschungsgemeinschaft.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Hamaekers .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Griebel, M., Hamaekers, J. (2021). Generalized Sparse Grid Interpolation Based on the Fast Discrete Fourier Transform. In: Bungartz, HJ., Garcke, J., Pflüger, D. (eds) Sparse Grids and Applications - Munich 2018. Lecture Notes in Computational Science and Engineering, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-030-81362-8_3

Download citation

Publish with us

Policies and ethics