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).
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References
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.
H. Bungartz and M. Griebel. Sparse grids. Acta Numerica, 13:1–123, 2004.
D. Dũng. B-spline quasi-interpolant representations and sampling recovery of functions with mixed smoothness. Journal of Complexity, 27(6):541–567, 2011.
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.
D. Dũng, V. Temlyakov, and T. Ullrich. Hyperbolic Cross Approximation. Advanced Courses in Mathematics - CRM Barcelona. Birkhäuser/Springer, 2018.
D. Dũng and T. Ullrich. N-widths and ε-dimensions for high-dimensional approximations. Foundations of Computational Mathematics, 13:965–1003, 2013.
T. Gerstner and M. Griebel. Dimension-adaptive tensor-product quadrature. Computing, 71(1):65–87, 2003.
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.
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.
M. Griebel and M. Holtz. Dimension-wise integration of high-dimensional functions with applications to finance. Journal of Complexity, 26:455–489, 2010.
M. Griebel and S. Knapek. Optimized tensor-product approximation spaces. Constructive Approximation, 16(4):525–540, 2000.
M. Griebel and S. Knapek. Optimized general sparse grid approximation spaces for operator equations. Mathematics of Computation, 78:2223–2257, 2009.
K. Hallatschek. Fourier-transform on sparse grids with hierarchical bases. Numerische Mathematik, 63(1):83–97, 1992.
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.
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.
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.
L. Kämmerer, T. Ullrich, and T. Volkmer. Worst case recovery guarantees for least squares approximation using random samples. arXiv preprint arXiv:1911.10111, 2019.
S. Knapek. Approximation und Kompression mit Tensorprodukt-Multiskalenräumen. Dissertation, University of Bonn, 2000.
S. Knapek. Hyperbolic cross approximation of integral operators with smooth kernel. 2000. Technical Report 665, SFB 256, University of Bonn.
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.
D. Krieg and M. Ullrich. Function values are enough for L 2-approximation. arXiv preprint arXiv:1905.02516, 2019.
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.
F. Kupka. Sparse Grid Spectral Methods for the Numerical Solution of Partial Differential Equations with Periodic Boundary Conditions. PhD thesis, University of Wien, 1997.
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.
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.
W. Sickel and T. Ullrich. Spline interpolation on sparse grids. Applicable Analysis, 90(3–4):337–383, 2011.
V. Temlyakov. Approximation of Periodic Functions. Nova Science, New York, 1993.
H. Triebel. Bases in Function Spaces, Sampling, Discrepancy, Numerical Integration, volume 11 of EMS Tracts in Mathematics. European Mathematical Society, 2010.
V. Velikov. Fast Sparse Pseudo-spectral Methods for High-dimensional Problems. Master thesis, Institute for Numerical Simulation, Universität Bonn, 2016.
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This work was supported in part by the Collaborative Research Centre 1060 of the Deutsche Forschungsgemeinschaft.
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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
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