A Survey of Methods of Numerical Approximation to Functions

  • R.A. Pickering

    Student thesis: Master's ThesisMaster of Philosophy

    Abstract

    This work undertakes a survey of methods of numerical approximation
    to functions. The functions considered are taken to be continuous within
    the range of approximation. Some consideration is given to the types
    of approximating functions in common use and the measurement of goodness
    of fit. It is seen that these two criteria together decide by what method
    the unknown coefficients are to be determined.

    Some properties of orthogonal functions and continued fractions are
    presented. Methods of deriving interpolating functions are described.
    Approximations may often be based on series expansions and this is considered, with reference to Chebyshev series, asymptotic series and Pade’
    approximants.

    The next section deals with approximations derived when the measure
    of fit is chosen as one of the three Holder norms L1, L2, or L.∞. The L,
    problem is shown to be solved in some cases by treatment as an interpolation problem. The least-squares (L2) problem is best treated using orthogonal polynomials. The minimax (L∞,) approximation.is seen to be found
    only by means of an iterative process and is the best approach when finding rational function approximations.

    The method of spline approximations is described. This is basically
    an interpolative approach, the practical method involves representing the
    function between the points of agreement, or knots, by cubic polynomials.

    Finally a general summary covers the types of approximation considered.
    Some techniques, e.g. range reduction, are mentioned which help in
    certain cases with finding efficient approximations. An attempt is made
    to give a general strategy which can be adopted for finding a suitable
    approximation to a given function and which would be workable in all but
    exceptional cases.
    Date of Award1972
    Original languageEnglish

    Keywords

    • numerical approximation
    • functions

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