Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T02:43:03.576Z Has data issue: false hasContentIssue false

A stable finite difference ansatz for higher order differentiation of non-exact data

Published online by Cambridge University Press:  17 April 2009

Bob Anderssen
Affiliation:
Division of Mathematics and Statistics, CSIRO, GPO Box 1965, Canberra ACT 2601, Australia
Frank de Hoog
Affiliation:
Division of Mathematics and Statistics, CSIRO, GPO Box 1965, Canberra ACT 2601, Australia
Markus Hegland
Affiliation:
Computer Sciences Laboratory, RSISE, Australian National University, Canberra ACT 0200, Australia
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

If standard central difference formulas are used to compute second or third order derivatives from measured data even quite precise data can lead to totally unusable results due to the basic instability of the differentiation process. Here an averaging procedure is presented and analysed which allows the stable computation of low order derivatives from measured data. The new method first averages the data, then samples the averages and finally applies standard difference formulas. The size of the averaging set acts like a regularisation parameter and has to be chosen as a function of the grid size h.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 1998

References

[1]Andersen, Chr., ‘The ruler method - an examination of a method for numerical determination of Fourier coefficients’, Acta Polytech. Scand. Math, and Comput. Mach. Ser. 8 (1963), 1973.Google Scholar
[2]Anderssen, R.S., ‘Stable procedures for the inversion of Abel's equation’, J. Inst. Math. Appl. 17 (1976), 329342.CrossRefGoogle Scholar
[3]Anderssen, R.S. and Bloomfield, P., ‘Numerical differentiation procedures for non-exact data’, Numer. Math. 22 (1974), 157182.CrossRefGoogle Scholar
[4]Anderssen, R.S. and de Hoog, F.R., ‘Finite difference methods for the numerical differentiation of non-exact data’, Computing 33 (1984), 259267.CrossRefGoogle Scholar
[5]Conte, S.D. and de Boor, C., Elementary numerical analysis: an algorithmic approach, (2nd ed.) (McGraw-Hill, New York, 1972).Google Scholar
[6]Finney, D.J., Statistics for mathematicians: an introduction (Oliver and Boyd, Edinburgh, 1968).Google Scholar
[7]Hartree, D.R., Numerical analysis (Clarendon Press, Oxford, 1952).Google Scholar
[8]Hegland, M. and Anderssen, R.S., A mollification framework for improperly posed problems, Mathematics Research Report MRR 085–95. (CMA, Australian National University, Canberra, Australia) (submitted).CrossRefGoogle Scholar
[9]Richardson, L.F., ‘The approximate arithmetic solution by finite difference of physical problems involving differential equations with an application to the stresses in a masonary dam’, Philos. Trans. Roy. Soc. London Ser. A 210 (1910), 307357.Google Scholar
[10]Wahba, G., Spline models for observational data (SIAM, Philadelphia, PA, 1990).CrossRefGoogle Scholar