[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

Optimal Truncations for Multivariate Fourier and Chebyshev Series: Mysteries of the Hyperbolic Cross: Part I: Bivariate Case

  • Published:
Journal of Scientific Computing Aims and scope Submit manuscript

Abstract

The key to most successful applications of Chebyshev and Fourier spectral methods in high space dimension are a combination of a Smolyak sparse grid together with so-called “hyperbolic cross” truncation. It is easy to find counterexamples for which the hyperbolic cross truncation is far from optimal. An important question is: what characteristics of a function make it “crossy”, that is, suitable for the hyperbolic crosss truncation? We have not been able to find a complete answer to this question. However, by combining low-rank SVD approximation, Poisson summation and imbricate series, hyperbolic coordinates and numerical experimentation, we are, to borrow from Fermi, “confused at a higher level”. For rank-one (separable) functions, which are the product of two univaraiate functions, we show that the hyperbolic cross truncation is indeed the best if the functions have weak singularities on the domain or boundaries so that the spectral series has a finite order of power-law convergence. For functions smooth on the domain, and therefore blessed with exponentially convergent spectral series, we have failed to find any reasonable examples where the hyperbolic cross truncation is best.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. “Circular” is replaced by“Spherical” (in three dimensions) or “Hyperspherical” (in more than three dimensions).

References

  1. Baddour, N.: Two-dimensional Fourier transforms in polar coordinates. Adv. Imaging Electron Phys. 165, 1–45 (2011)

    Article  Google Scholar 

  2. Barthelmann, V., Novak, E., Ritter, K.: High dimensional polynomial interpolation on sparse grids. Adv. Comput. Math. 12, 273–288 (2000)

    Article  MathSciNet  Google Scholar 

  3. Bebendorf, M.: Adaptive cross approximation of multivariate functions. Construct. Approx. 34, 149–179 (2011)

    Article  MathSciNet  Google Scholar 

  4. Boyd, J.P.: The double cnoidal wave of the Korteweg–de Vries equation: an overview. J. Math. Phys. 25, 3390–3401 (1984)

    Article  MathSciNet  Google Scholar 

  5. Boyd, J.P.: New directions in solitons and nonlinear periodic waves: polycnoidal waves, imbricated solitons, weakly non-local solitary waves and numerical boundary value algorithms. In: Wu, T.-Y., Hutchinson, J.W. (eds.) Advances in Applied Mechanics No. 27, pp. 1–82. Academic Press, New York (1989)

    Google Scholar 

  6. Boyd, J.P.: Chebyshev and Fourier Spectral Methods, 2nd edn. Dover, Mineola, New York (2001). 665 pp

    MATH  Google Scholar 

  7. Boyd, J.P.: Large-degree asymptotics and exponential asymptotics for Fourier coefficients and transforms, Chebyshev and other spectral coefficients. J. Eng. Math. 63, 355–399 (2009)

    Article  Google Scholar 

  8. Boyd, J.P.: Imbricate-Fourier Series with Applications. SIAM, Philadelphia (2018)

    Google Scholar 

  9. Boyd, J.P., Flyer, N.: Compatibility conditions for time-dependent partial differential equations and the the rate of convergence of Chebyshev and Fourier spectral methods. Comput. Methods. Appl. Mech. Eng. 175, 281–309. Errata: in Eq.(22), the square root should be in front of the integral, not in the exponential (1999)

    Article  MathSciNet  Google Scholar 

  10. Boyd, J.P., Haupt, S.E.: Polycnoidal waves: spatially periodic generalizations of multiple solitary waves. In: Osborne, A.R. (ed.) Nonlinear Topics of Ocean Physics: Fermi Summer School, Course LIX, pp. 827–856. North-Holland, Amsterdam (1991)

    Google Scholar 

  11. Boyd, J.P., Yu, F.: Comparing six spectral methods for interpolation and the Poisson equation in a disk: Radial basis functions, Logan-Shepp ridge polynomials, Fourier-Bessel, Fourier-Chebyshev, Zernike polynomials, and double Chebyshev series. J. Comput. Phys. 230, 1408–1438 (2011)

    Article  MathSciNet  Google Scholar 

  12. Bryzgalov, A.A.: Integral relations for Bessel functions and analytical solutions for Fourier transform in elliptic coordinates. WSEAS Trans. Math. 17, 205–212 (2018)

    Google Scholar 

  13. Carvajal, O.A., Chapman, F.W., Geddes, K.O.: Hybrid symbolic-numeric integration in multiple dimensions via [tensor-product] series. In: Proceedings of the ISSAC 05, Philadelphia, ACM (2005)

  14. Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1, 211–218 (1936)

    Article  Google Scholar 

  15. Flyer, N.: Asymptotic upper bounds for the coefficients in the Chebyshev series expansion for a general order integral of a function. Math. Comput. 67, 1601–1616 (1998)

    Article  MathSciNet  Google Scholar 

  16. Frauenfelder, P., Schwab, C., Todor, R.: Finite elements for elliptic problems with stochastic coefficients. Comput. Methods Appl. Mech. Eng. 194, 205–228 (2005)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  18. Goreinov, S., Tyrtyshnikov, E.E., Zamarashkin, N.L.: A theory of pseudoskeleton approximations. Linear Algebra Appl. 261, 1–21 (1997)

    Article  MathSciNet  Google Scholar 

  19. Gottlieb, D., Orszag, S.A.: Numerical Analysis of Spectral Methods, p. 200. SIAM, Philadelphia, PA (1977)

    Book  Google Scholar 

  20. Higham, N.J.: Accuracy and Stability of Numerical Algorithms. SIAM, Philadelphia (1996)

    MATH  Google Scholar 

  21. Karniadakis, G.E., Sherwin, S.J.: Spectral/hp Element Methods for CFD, p. 448. Oxford University Press, Oxford (1999)

    MATH  Google Scholar 

  22. Oberhettinger, F.: Tables of Bessel Transforms, p. 290. Springer, Heidelberg (1973)

    Book  Google Scholar 

  23. Olver, F.W.J., Lozier, D.W., Boisvert, R.F., Clark, C.W. (eds.): NIST Handbook of Mathematical Functions. Cambridge University Press, New York (2010)

    MATH  Google Scholar 

  24. Orszag, S.A.: Transform method for calculation of vector coupled sums: application to the spectral form of the vorticity equation. J. Atmos. Sci. 27, 890–895 (1970)

    Article  Google Scholar 

  25. Orszag, S.A.: Fourier series on spheres. Mon. Weather Rev. 102, 56–75 (1974)

    Article  Google Scholar 

  26. Schwab, C., Radu-Alexandru, T.: Sparse finite elements for elliptic problems with stochastic loading. Numer. Math. 95, 707–734 (2003)

    Article  MathSciNet  Google Scholar 

  27. Shen, J., Wang, L.L.: Sparse spectral approximations of high-dimensional problems based on hyperbolic cross. SIAM J. Num. Anal. 48, 1087–1109 (2010)

    Article  MathSciNet  Google Scholar 

  28. Shen, J., Yu, H.: Efficient spectral sparse grid methods and applications to high-dimensional elliptic problems. SIAM J. Sci. Comput. 32, 3228–3250 (2010)

    Article  MathSciNet  Google Scholar 

  29. Stewart, G.W.: On the early history of the singular value decomposition. SIAM Rev. 35, 551–566 (1993)

    Article  MathSciNet  Google Scholar 

  30. Townsend, A., Trefethen, L.N.: Continuous analogues of matrix factorizations. Proc. R. Soc. Lond. 471, 106–123 (2015)

    Article  MathSciNet  Google Scholar 

  31. Trefethen, L.N.: Cubature, approximation, and isotropy in the hypercube. SIAM Rev. 59, 469–491 (2017)

    Article  MathSciNet  Google Scholar 

  32. Trefethen, L.N.: Multivariate polynomial approximation in the hypercube. Proc. Am. Math. Soc. 145, 4837–4844 (2017)

    Article  MathSciNet  Google Scholar 

  33. Wasilkowski, G.W., Wozniakowski, H.: Weighted tensor product algorithms for linear multivariate problems. J. Complex. 15, 402–447 (1999)

    Article  MathSciNet  Google Scholar 

  34. Xiu, D.: Fast numerical methods for stochastic computations. A review. Commun. Comput. Phys. 5, 242–272 (2009)

    MathSciNet  MATH  Google Scholar 

  35. Xiu, D.: Numerical Methods for Stochastic Computations: A Spectral Method Approach, p. 130. Princeton University Press, Princeton, NJ (2010)

    Book  Google Scholar 

  36. Xiu, D., Hesthaven, J.S.: High-order collocation methods for differential equations with random inputs. SIAM J. Sci. Comput. 27, 1118–1139 (2005)

    Article  MathSciNet  Google Scholar 

  37. Yserentant, H.: On the regularity of the electronic Schroedinger equation in Hilbert spaces of mixed derivatives. Numer. Math. 98, 731–759 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Science Foundation of the U. S. under DMS-1521158 and by Chinese Scholarship Council 201606060017.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John P. Boyd.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Boyd, J.P. Optimal Truncations for Multivariate Fourier and Chebyshev Series: Mysteries of the Hyperbolic Cross: Part I: Bivariate Case. J Sci Comput 82, 34 (2020). https://doi.org/10.1007/s10915-020-01131-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10915-020-01131-1

Keywords

Mathematical Subject Classification

Navigation