%% 14. Discussion of high-order interpolation
%%
% As mentioned at various points in this book, high-order polynomial
% interpolation has a bad reputation. For equispaced points this is
% entirely appropriate, as shown in the last chapter, but for Chebyshev
% points it is entirely inappropriate. Here are some illustrative quotes
% from fifty years of numerical analysis textbooks, which we present
% anonymously.
%%
% _We cannot rely on a polynomial to be a good approximation if exact
% matching at the sample points is the criterion used to select the
% polynomial. The explanation of this phenomenon is, of course, that the
% derivatives grow too rapidly._ (1962)
%%
% _However, for certain functions the approximate representation of $f(x)$
% by a single polynomial throughout the interval is not satisfactory._
% (1972)
%%
% _But there are many functions which are not at all suited for
% approximation by a single polynomial in the entire interval which is of
% interest._ (1974)
%%
% \em
% Polynomial interpolation has drawbacks in addition to those of global
% convergence. The determination and evaluation of interpolating
% polynomials of high degree can be too time-consuming for certain
% applications. Polynomials of high degree can also lead to difficult
% problems associated with roundoff error. $(1977)$
% \par\vskip 1em
% We end this section with two brief warnings, one against trusting the
% interpolating polynomial outside [the interval] and one against expecting
% too much of polynomial interpolation inside [the interval]. $(1980)$
%
%%
% _Although Lagrangian interpolation is sometimes useful in theoretical
% investigations, it is rarely used in practical computations._ (1985)
%%
% \em
% Polynomial interpolants rarely converge to a general continuous function.
% Polynomial interpolation is a bad idea. $(1989)$
% \par\vskip 1em
% While theoretically important, Lagrange's formula is, in general, not as
% suitable for actual calculations as some other methods to be described
% below, particularly for large numbers $n$ of support points. $(1993)$
% \par\vskip 1em
% Unfortunately, there are functions for which interpolation at the
% Chebyshev points fails to converge. Moreoever, better approximations of
% functions like $1/(1+x^2)$ can be obtained by other interpolants---e.g.,
% cubic splines. $(1996)$
% \par\vskip 1em
% In this section we consider examples which warn us of
% the limitations of using interpolation polynomials as approximations
% to functions. $(1996)$
% \par\vskip 1em
% Increasing the number of interpolation points, i.e., increasing the
% degree of the polynomials, does not always lead to an improvement in the
% approximation. The {\em spline interpolation} that we will study in this
% section remedies this deficiency. $(1998)$
% \par\vskip 1em
% The surprising state of affairs is that for most continuous functions,
% the quantity $\|f-p_n\|_\infty$ will not coverge to $0$. $(2002)$
%
%%
% _Because its derivative has $n-1$ zeros, a polynomial of degree $n$ has
% $n-1$ extreme or inflection points. Thus, simply put, a high-degree
% polynomial necessarily has many ``wiggles,'' which may bear no relation
% to the data to be fit._ (2002)
%%
% _By their very nature, polynomials of a very high degree do not
% constitute reasonable models for real-life phenomena, from the
% approximation and from the handling point-of-view._ (2004)
%%
% _The oscillatory nature of high degree polynomials, and the property that
% a fluctuation over a small portion of the interval can induce large
% fluctuations over the entire range, restricts their use._ (2005)
%%
%
% {\em In addition to the inherent instability of Lagrange interpolation
% for large $n$, there are also classes of functions that are not suitable
% for approximation by certain types of interpolation. There is a
% celebrated example of Runge$\dots.$} (2011)
%
%%
% A great deal of confusion underlies remarks like these. Some of them are
% literally correct, but they are all misleading. In fact, polynomial
% interpolants in Chebyshev points are problem-free when evaluated by the
% the barycentric interpolation formula. They have the same behavior as
% discrete Fourier series for periodic functions, whose reliability nobody
% worries about. The introduction of splines is a red herring: the true
% advantage of splines, as mentioned in Chapter 9, is not that they
% converge where polynomials fail to do so, but that they are more easily
% adapted to irregular point distributions and more localized, giving
% errors that decay exponentially away from singularities rather than just
% algebraically.
%%
% It is interesting to speculate as to how the distrust of high-degree
% polynomials became so firmly established. I think the crucial
% circumstance is that not one but several combined problems affect certain
% computations with polynomials, a situation complex enough to have
% obscured the truth from easy elucidation. If working with polynomials had
% been the central task of scientific computing, the truth would have been
% worked out nonetheless, but over the years there were always bigger
% problems pressing upon the attention of numerical analysts, like matrix
% computations and differential equations. Polynomial computations were
% always a sideline.
%%
% At the most fundamental level there are the two issues of conditioning
% and stability: both crucial, and not the same. See [Trefethen & Bau 1997]
% for a general discussion of conditioning and stability.
%%
%
% (1) {\em Conditioning of the problem.} The interpolation points must be
% properly spaced (e.g., Chebyshev or Legendre) for the interpolation
% problem to be well-conditioned. This means that the interpolant should
% depend not too sensitively on the data. The Runge phenomenon for equally
% spaced points is the well-known consequence of extreme ill-conditioning,
% with sensitivities of order $2^n$. The next chapter makes such statements
% precise through the use of Lebesgue constants.
%
%%
% (2) _Stability of the algorithm._ The interpolation algorithm must be
% stable ($\hbox{e.g.,}$ the barycentric interpolation formula) for a
% computation to be accurate, even when the problem is well-conditioned.
% This means that in the presence of rounding errors, the computed solution
% should be close to an exact solution for some interpolation data close to
% the exact data. In particular, the best-known algorithm of all, namely
% solving a Vandermonde linear system of equations to find the coefficients
% of the interpolant expressed as a linear combination of monomials, is
% explosively unstable, relying on a matrix whose condition
% number grows exponentially with the dimension (Exercise 5.2).
%%
% These facts would be enough to explain a good deal of confusion, but
% another consideration has muddied the water further, namely crosstalk
% with the notoriously troublesome problem of finding roots of a polynomial
% from its coefficients (to be discussed in Chapter 18). The difficulties
% of polynomial rootfinding were widely publicized by Wilkinson beginning
% in the 1950s, who later wrote an article called the ``The perfidious
% polynomial'' that won the Chauvenet Prize of the Mathematical Association
% of America [Wilkinson 1984]. Undoubtedly this negative publicity further
% discouraged people from the use of polynomials, even though interpolation
% and rootfinding are different problems. They are related, with related
% widespread misconceptions about accuracy: just as interpolation on an
% interval is trouble-free for a stable algorithm based on Chebyshev
% points, rootfinding on an interval is trouble-free for a stable algorithm
% based on expansions in Chebyshev polynomials (Chapter 18). But very few
% textbooks tell readers these facts.
%%
%
% \begin{displaymath}
% \framebox[4.7in][c]{\parbox{4.5in}{\vspace{2pt}\sl
% {\sc Summary of Chapter 14.} Generations of numerical analysis
% textbooks have warned readers
% that polynomial interpolation is dangerous. In fact, if the
% interpolation points are clustered and a stable algorithm is used, it is
% bulletproof.\vspace{2pt}}}
% \end{displaymath}
%
%%
%
% \par\small\smallskip\parskip=2pt
% {\bf Exercise 14.1. Convergence as \boldmath$n\to\infty$.} The 1998 quote asserts
% that increasing $n$ ``does not always lead to an improvement''. Based on
% the theorems of this book, for interpolation in Chebyshev points, for
% which functions $f$ do we know that increasing $n$ must lead to an
% improvement?
% \par
% {\bf Exercise 14.2. Too many wiggles.}
% Using Chebfun's \verb|roots(f,'all')| option, plot all the roots
% in the complex plane of the derivative of the chebfun corresponding to
% $f(x) = \exp(x) \tanh(2x-1)$ on $[-1,1]$.
% What is the error in the argument in the second 2002 quote used to
% show that ``a high-degree polynomial
% necessarily has many wiggles''?
% \par
% {\bf Exercise 14.3. Your own textbook.} Find a textbook of numerical
% analysis and examine its treatment of polynomial interpolation. (a) What
% does it say about behavior for large $n$? If it asserts that this
% behavior is problematic, is this conclusion based on the assumption of
% equally spaced points, and does the text make this clear?
% (b) Does it mention interpolation in Chebyshev points? Does it
% state that such interpolants converge exponentially for analytic
% functions? (c) Does it mention the barycentric formula? (d) Does
% it claim that one should use a Newton rather than a Lagrange
% interpolation formula for numerical work? (This is a myth.)
% \par
% {\bf Exercise 14.4. Spline interpolants.} (a) Use Chebfun's
% \verb|spline| command to interpolate $f(x) = 1/(1+25x^2)$ by a cubic spline
% in $n+1$ equally spaced points on $[-1,1]$. Compare the $\infty$-norm
% error as $n\to\infty$ with that of polynomial interpolation in
% Chebyshev points.
% (b) Same problem for $f(x) = |x+1/\pi|$.
% (c) Same problem for $f(x) = |x+1/\pi|$, but measuring the error
% by the $\infty$-norm over the interval $[0,1]$.
% \par