Skip to main content
Log in

Error estimates and condition numbers for radial basis function interpolation

  • Published:
Advances in Computational Mathematics Aims and scope Submit manuscript

Abstract

For interpolation of scattered multivariate data by radial basis functions, an “uncertainty relation” between the attainable error and the condition of the interpolation matrices is proven. It states that the error and the condition number cannot both be kept small. Bounds on the Lebesgue constants are obtained as a byproduct. A variation of the Narcowich-Ward theory of upper bounds on the norm of the inverse of the interpolation matrix is presented in order to handle the whole set of radial basis functions that are currently in use.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. W.R. Madych and S.A. Nelson, Multivariate interpolation: a variational theory, Manuscript (1983).

  2. W.R. Madych and S.A. Nelson, Bounds on multivariate polynomials and exponential error estimates for multiquadric interpolation. J. Approx. Theory 70 (1992) 94–114.

    Article  MathSciNet  MATH  Google Scholar 

  3. F.J. Narcowich and J.D. Ward, Norm of inverses and condition numbers for matrices associated with scattered data. J. Approx. Theory 64 (1991) 69–94.

    Article  MathSciNet  MATH  Google Scholar 

  4. F.J. Narcowich and J.D. Ward, Norms of inverses for matrices associated with scattered data, in:Curves and Surfaces, eds. P.J. Laurent, A. Le Méhauté and L.L. Schumaker (Academic Press, Boston, 1991) pp. 341–348.

    Google Scholar 

  5. F.J. Narcowich and J.D. Ward, Norm estimates for the inverses of a general class of scattered-data radial-function interpolation matrices, J. Approx. Theory 69 (1992) 84–109.

    Article  MathSciNet  MATH  Google Scholar 

  6. M.J.D. Powell, Univariate multiquadric interpolation: Some recent results, in:Curves and Surfaces, eds. P.J. Laurent, A. Le Méhauté and L.L. Schumaker (Academic Press, 1991) pp. 371–382.

  7. R. Schaback, Comparison of radial basis function interpolants, in:Multivariate Approximation: From CAGD to Wavelets, eds. K. Jetter and F. Utreras, (World Scientific, London, 1993) pp. 293–305.

    Google Scholar 

  8. R. Schaback, Lower bounds for norms of inverses of interpolation matrices for radial basis functions, J. Approx. Theory 79 (1994) 287–306.

    Article  MathSciNet  MATH  Google Scholar 

  9. X. Sun, Norm estimates for inverses of Euclidean distance matrices, J. Approx. Theory 70 (1992) 339–347.

    Article  MathSciNet  MATH  Google Scholar 

  10. Z. Wu and R. Schaback, Local error estimtes for radial basis function interpolation of scattered data, IMA J. Numer. Anal. 13 (1993) 13–27.

    Article  MathSciNet  MATH  Google Scholar 

  11. H.P. Seidel, Symmetric recursive algorthms for curves, Comp. Aided Geom. Design 7 (1990) 57–67.

    Article  MathSciNet  MATH  Google Scholar 

  12. H.P. Seidel, Polar forms for geometrically continuous spline curves of arbitrary degree, ACM Trans. Graphics 12 (1993) 1–34.

    Article  MATH  Google Scholar 

  13. K. Strøm, Splines, polynomials and polar forms. Ph.D. dissertation, University of Oslo, Norway (1992).

    Google Scholar 

  14. K. Strøm, Products of B-patches, Numer. Algor. 4 (1993) 323–337.

    Article  Google Scholar 

References

  1. A.S. Cavaretta, W. Dahmen and C.A. Micchelli,Stationary Subdivision, Memoirs of Amer. Math. Soc., Vol. 93 (1991).

  2. D. Colella and C. Heil, Characterizations of scaling functions: continuous solutions, SIAM J. Matrix Anal. Appl. 15 (1994) 496–518.

    Article  MathSciNet  MATH  Google Scholar 

  3. W. Dahmen and C.A. Micchelli, Translates of multivariate splines, Lin. Alg. Appl. 52 (1983) 217–234.

    MathSciNet  Google Scholar 

  4. G. Deslauriers and S. Dubuc, Symmetric iterative interpolation process, Constr. Approx. 5 (1989) 49–68.

    Article  MathSciNet  MATH  Google Scholar 

  5. I. Daubechies and J.C. Lagarias, Two-scale difference equations: I. Existence and global regularity of solutions, SIAM J. Math. Anal. 22 (1991) 1388–1410.

    Article  MathSciNet  MATH  Google Scholar 

  6. I. Daubechies and J.C. Lagarias, Two-scale difference equations: II. Local regularity, infinite products of matrices and fractals, SIAM J. Math. Anal. 23 (1992) 1031–1079.

    Article  MathSciNet  MATH  Google Scholar 

  7. R.A. DeVore and G.G. Lorentz,Constructive Approximation (Springer, Berlin, 1993).

    Book  MATH  Google Scholar 

  8. S. Dubuc, Interpolation through an iterative scheme, J. Math. Anal. Appl. 114 (1986) 185–205.

    Article  MathSciNet  MATH  Google Scholar 

  9. N. Dyn, Subdivision schemes in computer aided geometric design, in:Advances in Numerical Analysis II — Wavelets, Subdivision Algorithms and Radius Functions, ed. W.A. Light (Clarendon Press, Oxford, 1991) pp. 36–104.

    Google Scholar 

  10. N. Dyn, J.A. Gregory and D. Levin, Analysis of uniform binary subdivision schemes for curve design. Constr. Approx. 7 (1991) 127–147.

    Article  MathSciNet  MATH  Google Scholar 

  11. T. Eirola, Sobolev characterization of solutions of dilation equations, SIAM J. Math. Anal. 23 (1992) 1015–1030.

    Article  MathSciNet  MATH  Google Scholar 

References

  1. J.R. Dormand and P.J. Prince, A family of embedded Runge-Kutta formulae, J. Comput. Appl. Math. 6 (1980) 19–26.

    Article  MathSciNet  MATH  Google Scholar 

  2. K. Gustafsson, Control theoretic techniques for stepsize selection in explicit Runge-Kutta methods, ACM Trans. Math. Software 17 (1991) 533–544.

    Article  MathSciNet  MATH  Google Scholar 

  3. K. Gustafsson, M. Lundh and G. Söderlind, A PI stepsize control for the numerical solution of ordinary differential equations, BIT 28 (1988) 270–287.

    Article  MathSciNet  MATH  Google Scholar 

  4. E. Hairer and G. Wanner,Solving Ordinary Differential Equations II (Springer, Berlin, 1991).

    Book  MATH  Google Scholar 

  5. G. Hall, Equilibrium states of Runge-Kutta formulae, ACM Trans. Math. Software 11 (1985) 289–301.

    Article  MathSciNet  MATH  Google Scholar 

  6. G. Hall and D.J. Higham, Analysis of stepsize selection schemes for Runge-Kutta codes, IMA J. Numer. Anal. 8 (1988) 305–310.

    Article  MathSciNet  MATH  Google Scholar 

  7. D.J. Higham and G. Hall, Embedded Runge-Kutta formulae with stable equilibrium states, J. Comput. Appl. Math. 29 (1990) 25–33.

    Article  MathSciNet  MATH  Google Scholar 

  8. F.T. Krogh, On testing a subroutine for the numerical integration of ordinary differential equations, J. ACM 4 (1973) 545–562.

    Article  Google Scholar 

  9. P.J. Prince and J.R. Dormand, High order embedded Runge-Kutta formulae, J. Comput. Appl. Math. 7 (1981) 67–75.

    Article  MathSciNet  MATH  Google Scholar 

  10. B.C. Robertson, Detecting stiffness with explicit runge-Kutta formulas, Report 193/87, Dept. Comp. Sci., University of Toronto (1987).

  11. L.F. Shampine, Lipschitz constants and robust ODE codes, Technical Report SAND79-0458, Sandia National Laboratories, Albuquerque, New Mexico (March 1979).

    Google Scholar 

References

  1. M. Abramowitz and I.A. Stegun,Pocketbook of Mathematical Functions (Harri Deutsch, Thun, 1984).

    MATH  Google Scholar 

  2. K. Ball, N. Sivakumar and J.D. Ward, On the sensitivity of radial basis interpolation to minimal data separation distance, Constr. Approx. 8 (1992) 401–426.

    Article  MathSciNet  MATH  Google Scholar 

  3. C. de Boor, The quasi-interpolant as a tool in elementary polynomial spline theory in:Approximation Theory, ed. G.G. Lorentz (Academic Press, New York, 1973) pp. 269–276.

    Google Scholar 

  4. C. de Boor,A Practical Guide to Splines (Springer, New York, 1978).

    Book  MATH  Google Scholar 

  5. C. de Boor and G.J. Fix, Spline approximation by quasiinterpolants, J. Approx. Theory 8 (1973) 19–45.

    Article  MATH  Google Scholar 

  6. M.D. Buhmann, Discrete least squares approximation and prewavelets from radial function spaces, Math. Proc. Cambridge Phil. Soc. 114 (1993) 533–558.

    Article  MathSciNet  MATH  Google Scholar 

  7. M.D. Buhmann and C.A. Micchelli, Spline prewavelets for non-uniform knots, Numer. Math. 61 (1992) 455–474.

    Article  MathSciNet  MATH  Google Scholar 

  8. C.K. Chui, K. Jetter, J. Stöckler and J.D. Ward, Wavelets for analyzing scattered data: An unbounded operator approach, ms. (November 1994).

  9. C.K. Chui, K. Jetter and J.D. Ward, Cardinal interpolation with differences of tempered functions, Comp. Math. Appl. 24 (1992) 35–48.

    Article  MathSciNet  MATH  Google Scholar 

  10. I. Daubechies,Ten Lectures on Wavelets, CBMS-NSF Reg. Conf. Series in Appl. Math., vol. 61 (SIAM, Philadelphia, 1992).

    Book  MATH  Google Scholar 

  11. R.A. DeVore and G.G. Lorentz,Constructive Approximation (Springer, New York, 1994).

    Google Scholar 

  12. I.M. Gel’fand and G.E. Shilov,Generalized Functions, vol. 1 (Academic Press, New York, 1964).

    MATH  Google Scholar 

  13. I.M. Gelfand and N.Ya. Vilenkin,Generalized Functions, vol. 4 (Academic Press, New York, 1964).

    Google Scholar 

  14. M.J.D. Powell, Univariate multiquadric approximation: Reproduction of linear polynomials, in:Multivariate Approximation and Interpolation, eds. W. Hau\mann and K. Jetter, ISNM 94 (Birkhäuser, Basel, 1990) pp. 227–240.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Communicated by L.L. Schumaker

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schaback, R. Error estimates and condition numbers for radial basis function interpolation. Adv Comput Math 3, 251–264 (1995). https://doi.org/10.1007/BF02432002

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02432002

Keywords

Navigation