%% 24. Rational best approximation
ATAPformats
%%
% Chapter 10 considered best or ``minimax'' approximation by polynomials,
% that is, approximation in the $\infty$-norm, where optimality is
% characterized by an equioscillating error curve. This chapter presents
% analogous results for approximation by rational functions. Much remains
% the same, but a crucial new feature is the appearance in the
% equioscillation condition of a number known as the _defect_, which leads
% to the phenomenon of square blocks of degenerate entries in the ``Walsh
% table'' of best approximations. This complication adds a fascinating
% new ingredient to the theory, but it is a complication with destructive
% consequences in terms of the fragility of rational approximations and the
% difficulty of computing them numerically. An antidote to some such
% difficulties may be the use of algorithms based on linearized
% least-squares and the singular value decomposition, a theme we shall take
% up in Chapters 26 and 27.
%%
% Another new feature in rational approximation is that we must now be
% careful to distinguish real and complex situations, because of a curious
% phenomenon: best rational approximations to real functions are in general
% complex. This effect is intriguing, but
% it has little relevance to practical problems, so for the most part
% we shall restrict our attention to approximations in the space
% ${\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$ consisting of functions in
% ${\cal R}_{mn}$ with real coefficients.
%%
% We will first state the main theorem, then give some examples, and then
% present a proof. To begin the discussion, we must define the defect.
% Suppose $r\in {\cal R}_{mn}$, that is, $r$ is a rational function of type
% $(m,n)$. As discussed in the last chapter, this means that $r$ can be
% written as a fraction $p/q$ in lowest terms with $p$ and $q$ having exact
% degrees $\mu\le m$ and $\nu \le n$. The _defect_ $d$ of $r$ in ${\cal R}_{mn}$
% is the number between $0$ and $n$ defined by
% $$ d = \min \{m-\mu, n-\nu\} \ge 0. \eqno (24.1) $$
% Note that $d$ is a measure of how far _both_ the numerator and the
% denominator degrees fall short of their maximum allowed values. Thus
% $(1-x^2)/(1+x^2)$, for example, has defect $0$ in ${\cal R}_{22}$ or ${\cal R}_{23}$
% and defect $1$ in ${\cal R}_{33}$.
%%
% A special case to be noted is the situation in which $r=0$, that is, $r$
% is identically zero. Recall that in this case we defined $\mu = -\infty$
% and $\nu = 0$, so that $r$ is said to have exact type $(-\infty,0)$. The
% definition (24.1) remains in force in this case, so if $r=0$, we say that
% $r$ has defect $d=n$ in ${\cal R}_{mn}$, regardless of $m$.
%%
% The reason why defects matter has to do with the counting of zeros.
% Suppose $r = p/q\in {\cal R}_{mn}$ has exact type $(\kern 1pt\mu,\nu)$ and $\tilde r =
% \tilde p/\tilde q$ is another function in ${\cal R}_{mn}$. Then we have
% $$ r-\tilde r = {p\over q} - {\tilde p\over \tilde q}
% = {p\tilde q - \tilde p q \over q\tilde q}, $$
% a rational function of type $(\max\{\mu+n, m+\nu\},n+\nu)$. By (24.1),
% this implies that $r-\tilde r$ is of type $(m+n-d,2n-d\kern .7pt )$. Thus $r-\tilde
% r$ can have at most $m+n-d$ zeros, and this zero count is a key to
% equioscillation and uniqueness results.
%%
%
% Here is our main theorem. The study of rational best approximations goes
% back to Chebyshev [1859], including the idea of equioscillation, though
% without a precise statement of what form an alternant must take.
% Existence was first proved by de la Vall\'ee
% Poussin [1911] and Walsh [1931], and equioscillation is due
% to Achieser [1930].
%
%%
%
% {\em {\bf Theorem 24.1. Equioscillation characterization of best
% approximants.} A real function $f\in C([-1,1])$ has a unique best
% approximation $r^* \in {\cal R}_{mn}^{\hbox{\scriptsize\rm\kern.1em real}}$, and
% a function $r \in {\cal R}_{mn}^{\hbox{\scriptsize\rm\kern.1em real}}$ is equal
% to $r^*$ if and only if $f-r$ equioscillates between at least $m+n+2-d$
% extreme points, where $d$ is the defect of $r$ in ${\cal R}_{mn}$.}
%
%%
%
% ``Equioscillation'' here is defined just as in Chapter 10. For $f-r$ to
% equioscillate between $k$ extreme points means that there exists a set of numbers $-1
% \le x_1 < \cdots < x_k \le 1$ such that $$ f(x_j) - r(x_j) = (-1)^{j+i}
% \|f-r\| , \qquad 1 \le j \le k $$ with $i = 0$ or $1$. Here and
% throughout this chapter, $\|\cdot\|$ is the supremum norm.
%
%%
% We now give some examples. To begin with, here is a function with a
% spike at $x=0$:
%%
% \vskip -2em
x = chebfun('x'); f = exp(-100*x.^2);
%%
% Polynomial approximations of this function converge rather slowly. For
% example, it takes $n=20$ to achieve one digit of accuracy:
%%
% \vskip -2em
[p,err] = remez(f,10);
subplot(1,2,1), hold off, plot(f-p), hold on
plot([-1 1],err*[1 1],'--k'), plot([-1 1],-err*[1 1],'--k')
FS = 'fontsize';
title('Error in type (10,0) approx',FS,9), ylim(.3*[-1 1])
[p,err] = remez(f,20);
subplot(1,2,2), hold off, plot(f-p), hold on
plot([-1 1],err*[1 1],'--k'), plot([-1 1],-err*[1 1],'--k')
title('Error in type (20,0) approx',FS,9), ylim(.3*[-1 1])
%%
% \vskip 1pt
%%
% Notice that the extreme points of these error curves are distributed all
% across $[-1,1]$, even though the challenging part of the function would
% appear to be in the middle. As discussed in Chapter 16, this is typical
% of polynomial best approximations.
%%
%
% If we switch to rational approximations, which can also be computed by
% Chebfun's \verb|remez| command [Pach\'on \& Trefethen 2009, Pach\'on 2010],
% the accuracy improves. Here we see error
% curves for approximations of types $(2,2)$ and $(4,4)$, with much smaller
% errors although the degrees are low. Note that most of the extreme points
% are now localized in the middle.
%
%%
% \vskip -2em
[p,q,rh,err] = remez(f,2,2);
subplot(1,2,1), hold off, plot(f-p./q), hold on
plot([-1 1],err*[1 1],'--k'), plot([-1 1],-err*[1 1],'--k')
title('Error in type (2,2) approx',FS,9), ylim(.1*[-1 1])
[p,q,rh,err] = remez(f,4,4);
subplot(1,2,2), hold off, plot(f-p./q), hold on
plot([-1 1],err*[1 1],'--k'), plot([-1 1],-err*[1 1],'--k')
title('Error in type (4,4) approx',FS,9), ylim(.1*[-1 1])
%%
% \vskip 1pt
%%
% The error curves just plotted provide good examples of the role of the
% defect in the characterization of best approximants. The function $f$ is
% even, and so are its best approximations (Exercise 24.1). Thus we expect
% that the type $(2,2)$, $(3,2)$, $(2,3)$ and $(3,3)$ best approximations
% will all be the same function, a rational function of exact type $(2,2)$
% whose error curve has 7 points of equioscillation. For $(m,n) = (2,2)$,
% the defect is $0$ and there is one more equioscillation point than the
% minimum $m+n+2-d=6$. For $(m,n) = (3,2)$ or $(2,3)$, the defect is $0$
% and the number of equiscillation points is exactly the minimum $m+n+2-d$.
% For $(m,n) = (3,3)$, the defect is $1$ and the number of equiscillation
% points is again exactly the minimum $m+n+2-d$.
%%
% Similarly, the error curve in the plot on the right, with 11 extrema,
% indicates that this rational function is a best approximation not only of
% type $(4,4)$ but also of types $(5,4)$, $(4,5)$, and $(5,5)$.
%%
% Here is another example, an odd function:
%%
% \vskip -2em
f = x.*exp(-5*abs(abs(x)-.3));
clf, plot(f), grid on, ylim(.4*[-1 1])
title('An odd function','fontsize',9)
%%
% \vskip 1pt
%%
% If we look for a best approximation of type $(4,5)$, we find that the
% numerator has exact degree $3$:
%%
% \vskip -2em
[p,q,rh,err] = remez(f,4,4); format short, chebpoly(p)
%%
% and the denominator has exact degree $4$:
%%
% \vskip -2em
chebpoly(q)
%%
% The defect is 1, so there must be at least $4+5+2-1= 10$ extreme points
% in the error curve. In fact, there are exactly 10:
%%
% \vskip -2em
plot(f-p./q), hold on, ylim(.04*[-1 1])
plot([-1 1],err*[1 1],'--k'), plot([-1 1],-err*[1 1],'--k')
title('Error curve of type (4,5) approximation','fontsize',9)
%%
% \vskip 1pt
%%
% We conclude that $r$ is the best approximation of types $(4,4), (4,5),
% (3,4)$ and $(3,5)$.
%%
%
% Let us now turn to the proof of Theorem 24.1. For polynomial
% approximations, our analogous theorem was Theorem 10.1, whose proof
% proceeded in four steps:
%
%%
%
% {\em 1. Existence proof via compactness,}
% \vskip -6pt
%%
%
% {\em 2. Equioscillation $\Rightarrow$ optimality,}
% \vskip -6pt
%%
%
% {\em 3. Optimality $\Rightarrow$ equisoscillation,}
% \vskip -6pt
%%
%
% {\em 4. Uniqueness proof via equioscillation.}
%
%%
%
% For rational functions, we shall follow the same sequence. The main
% novelty is in step 1, where compactness must be applied in a subtler way.
% We shall see an echo of this argument one more time in Chapter 27, in the proof
% of Theorem 27.1 for Pad\'e approximants.
%
%%
% _Part 1 of proof: Existence via compactness._
% For polynomial approximation, in Chapter 10, we noted that $\|f-p\|$ is a
% continuous function on ${\cal P}_n$, and since one candidate
% approximation was the zero polynomial, it was enough to look in the
% bounded subset $\{p\in {\cal P}_n: \|f-p\|\le \|f\|\}$. Since this set
% was compact, the minimum was attained.
%%
%
% For rational functions, $\|f-r\|$ is again a continuous function on
% ${\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$, and again it is enough to look in the bounded subset $\{r\in
% {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}: \|f-r\|\le \|f\|\}$, or more simply, the larger bounded set
% $\{r\in {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}: \|r\|\le 2\|f\|\}$. The difficulty is that bounded sets
% of rational functions are not in general compact. To illustrate this
% fact, consider the family of functions
% $$ r_\varepsilon(x) = {x^3 + \varepsilon \over x^2+\varepsilon}, \eqno (24.2) $$
% where $\varepsilon>0$ is a parameter. For each $\varepsilon$,
% $r_\varepsilon(x)$ is a continuous function on $[-1,1]$ with
% $\|r_\varepsilon\|=1$. As
% $\varepsilon\to 0$, however, $r_\varepsilon$ behaves discontinuously:
% $$ \lim_{\varepsilon\to 0} r_\varepsilon(x) = \cases{1 & $x=0,$\cr x & $ x\ne 0.$} $$
% So we cannot find a limit function $r_0$ by taking a limit as
% $\varepsilon\to 0$. What saves us, however, is that the spaces of
% numerators and denominators are both compact, so we can argue that the
% numerators and denominators
% separately approach limits $p_0$ and $q_0$, which in this example would
% be $x^3$ and $x^2$. We then define a limiting rational function by $r_0
% = p_0/q_0$ and argue by continuity that it has the necessary
% properties. This
% kind of reasoning is spelled out in greater generality in [Walsh 1931].
%
%%
%
% Suppose then that $\{r_k\}$ is a sequence of functions in
% $ {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$ with $\|r_k\|\le 2\|f\|$ and
% $$\lim_{k\to\infty} \|f-r_k\| = E =
% \inf_{r\in {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}} \|f-r\|.$$ Write each
% $r_k$ in the form $p_k/q_k$ with $p_k\in {\cal P}_m$, $q_k\in {\cal
% P}_n$, $q_k(x)\ne 0$ for all $x\in [-1,1]$, and $\|q_k\| = 1$, hence
% $\|p_k\|\le \|q_k\|\|r_k\| \le 2\|f\|$. Since $\{p_k\}$ and $\{q_k\}$ lie
% in compact sets, we may assume by passing to a subsequence if necessary
% that $p_k\to p^*$ and $q_k\to q^*$ for some $p^*\in {\cal P}_m$ and
% $q^*\in {\cal P}_n$. Since $\|q_k\|=1$ for each $k$, $\|q^*\| = 1$ too,
% and thus $q^*$ is not identically zero but has at most a finite set of
% zeros on $[-1,1]$. Now define $r^* = p^*/q^* \in
% {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$. For all $x\in[-1,1]$ except
% perhaps the zeros of $q^*$, $|f(x)-r^*(x)| = \lim_{k\to\infty} |f(x) -
% r_k(x)|\le E$. By continuity, the same must hold for all $x\in [-1,1],$
% with $p^*$ having zeros in $[-1,1]$ wherever $q^*$ does. Thus $r^*$ is a
% best approximation to $f$. $~\hbox{\vrule width 2.5pt depth 2.5 pt height
% 3.5 pt}$
%
%%
% _Part 2 of proof: Equioscillation $\Rightarrow$ optimality._ Suppose
% $f-r$ takes equal extreme values with alternating signs at $m+n+2-d$
% points $x_0
% {\em Part 3 of proof: Optimality $\Rightarrow$ equisoscillation.}
% Suppose $f-r$ equioscillates at fewer than $m+n+2-d$ points, and set
% $E=\|f-r\|$. Without loss of generality suppose the leftmost extremum is
% one where $f-r$ takes the value $-E$. Then by a compactness
% argument, for all sufficiently small $\varepsilon>0$, there are numbers $-1< x_1<
% \cdots < x_k< 1$ with $k\le m+n-d$ such that
% $(f-r)(x) -E+\varepsilon$ for $x\in [x_1-\varepsilon,x_2+\varepsilon]
% \cup [x_3-\varepsilon,x_4+\varepsilon] \cup \cdots.$\ \ Let
% $r$ be written in the form $p/q$, where
% $p$ has degree $\mu \le m-d$ and $q$ has degree $\nu \le n - d$, with $p$
% and $q$ having no roots in common. The proof now consists of showing that
% $r$ can be perturbed to a function $\tilde r = (\kern .7pt p+\delta p)/(q+\delta q)
% \in {\cal R}_{mn}$ with the properties that $\|\tilde r - r\|<\varepsilon$ and
% $\tilde r - r$ is strictly negative for $x\in [-1,x_1-\varepsilon] \cup [x_2+\varepsilon,x_3-\varepsilon]
% \cup [x_4+\varepsilon,x_5-\varepsilon] \cup \cdots$
% and strictly positive for $x\in [x_1+\varepsilon,x_2-\varepsilon]
% \cup [x_3+\varepsilon,x_4-\varepsilon] \cup \cdots.$
% Such a function $\tilde r$ will have error
% less than $E$ throughout the whole interval $[-1,1]$. We calculate
% $$ \tilde r = {p+\delta p\over q + \delta q} =
% {(\kern .7pt p+\delta p)(q-\delta q)\over q^2 } + O(\|\delta q\|^2) $$
% and therefore $$ \tilde r - r =
% {q\delta p - p\delta q\over q^2 } + O(\|\delta p\|\|\delta q\| +\|\delta q\|^2). $$
% We are done if we can show that $\delta p$ and $\delta q$ can be chosen
% so that $q\delta p - p\delta q$ is a nonzero polynomial of degree exactly
% $k$ with roots $x_1, \dots , x_k$; by scaling this $\delta p$ and $\delta q$ sufficiently
% small, the quadratic terms above can be made arbitrarily small relative to the
% others, so that the required $\varepsilon$ conditions are satisfied.
% This can be shown by the Fredholm
% alternative of linear algebra. The map from the $(m+n+2)$-dimensional
% set of choices of $\delta p$ and $\delta q$ to the
% $(m+n+1-d\kern .7pt )$-dimensional space of polynomials $q\delta p - p\delta q$ is
% linear. To show the map is surjective, it is enough to show that its
% kernel has dimension $d+1$ but no more. Suppose then that $q\delta p -
% p\delta q$ is zero, that is, $q\delta p = p\delta q$. Then since $p$ and
% $q$ have no roots in common, all the roots of $p$ must be roots of
% $\delta p$ and all the roots of $q$ must be roots of $\delta q$. In
% other words we must have $\delta p = g p$ and $\delta q = g q$ for some
% polynomial $g$. Since $\delta p$ has degree no greater than $m$ and
% $\delta q$ has degree no greater than $n$, $g$ can have degree no greater
% than $d$. The set of polynomials of degree $d$ has dimension $d+1$, so we
% are done.
% $~\hbox{\vrule width 2.5pt depth 2.5 pt height 3.5 pt}$
%
%%
% _Part 4 of proof: Uniqueness via equioscillation._ Finally, to prove
% uniqueness, suppose $r$ is a best approximation whose error curve
% equioscillates between extreme points at $x_0 < x_1 < \cdots <
% x_{m+n+1-d}$, and suppose $\|f-\tilde r\| \le \|f-r\|$ for some $\tilde
% r\in {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$. Then (without loss of
% generality) $(r-\tilde r)(x)$ must be $\le 0$ at $x_0, x_2, x_4,\dots$
% and $\ge 0$ at $x_1, x_3, x_5,\dots .$ This implies that $r-\tilde r$
% has roots in each of the $m+n+1-d$ closed intervals $[x_0,x_1], \dots,
% [x_{m+n-d},x_{m+n+1-d}]$, and since $r-\tilde r$ is a rational function
% of type $(m+n-d,2n-d\kern .7pt )$, the same must hold for its numerator polynomial.
% We wish to conclude that its numerator polynomial has at least $m+n+1-d$
% roots in total, counted with multiplicity, implying that $r=\tilde r$.
% The argument for this is the same as given in the proof of Theorem 10.1.
% $~\hbox{\vrule width 2.5pt depth 2.5 pt height 3.5 pt}$
%%
% We have now finished the substantial mathematics. It is time to look at
% some of the consequences.
%%
% One of the recurring themes in the subject of rational approximation is
% the phenomenon of _square blocks in the Walsh table._ Suppose that a
% real function $f\in C([-1,1])$ is given, and consider the set of all of
% its real rational best approximations of type $(m,n)$ for various $m,n\ge
% 0$. We can imagine these laid out in an array, with $m$ along the
% horizontal and $n$ along the vertical. This array is called the _Walsh
% table_ for $f$ [Walsh 1934].
%%
%
% Generically, all the entries in the Walsh table for a given $f$ will be
% distinct, and in this case we say that $f$ is {\em normal}. Sometimes,
% however, certain entries in the table may be repeated, and in fact this
% is a frequent occurrence because it happens whenever $f$ is even or odd.
% If $f$ is even, then for any nonegative integers $j$ and $k$, all of its
% rational approximations of types $(2j,2k)$, $(2j+1,2k)$, $(2j,2k+1)$ and
% $(2j+1,2k+1)$ must be the same. Similarly, if $f$ is odd, then all of its
% approximations of types $(2j+1,2k)$, $(2j+2,2k)$, $(2j+1,2k+1)$ and
% $(2j+2,2k+1)$ must be the same. We have already seen a number of
% examples.
%
%%
%
% More generally, repeated entries or ``degeneracies'' in the Walsh table
% may take complicated forms. Nevertheless the equioscillation condition
% imposes quite a bit of structure on the chaos. Degeneracies always
% appear precisely in a pattern of square blocks. The following statement
% of this result is taken from [Trefethen 1984], where a discussion of
% various aspects of this and related problems can be found. We shall
% return to the subject of square blocks in Chapter 27, on Pad\'e
% approximation.
%
%%
%
% {\em {\bf Theorem 24.2. Square blocks in the Walsh table.} The Walsh
% table of best real rational approximants to a real rational function
% $f\in C([-1,1])$ breaks into precisely square blocks containing identical
% entries. (If $f$ is rational, one of these will be infinite in extent.)
% The only exception is that if an entry $r=0$ appears in the table, then
% it fills all of the columns to the left of some fixed index $m = m_0$.}
%
%%
% _Proof._
% Given a nonrational function $f$, let $r\ne 0$ be a best approximation in
% ${\cal R}_{\mu\nu}^{\hbox{\scriptsize\kern.1em real}}$ of exact type
% $(\kern 1pt\mu,\nu)$. (The cases of rational $f$ or $r=0$ can be handled
% separately.) By Theorem 24.1, the number of equioscillation points of
% $f-r$ is $\mu+\nu+2+k$ for some integer $k\ge 0$. We note that $r$ is an
% approximation to $f$ in ${\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$ for
% any $m\ge \mu$ and $n\ge \nu$, and the defect is $\min\{m-\mu,n-\nu\}$.
% Thus by Theorem 24.1, $r$ is the best approximation to $f$ precisely for
% those values of $(m,n)$ satisfying $m\ge \mu$, $n\ge \nu$, and
% $\mu+\nu+2+k \ge m + n + 2 - \min\{m-\mu,n-\nu\}$. The latter condition
% simplifies to $n\le \nu+k$ and $m\le \mu+k$, showing that $r$ is the best
% approximation to $f$ precisely in the square block $\mu \le m \le \mu
% +k$, $\nu \le n \le \nu + k$.
% $~\hbox{\vrule width 2.5pt depth 2.5 pt height 3.5 pt}$
%%
% Within a square block in the Walsh table, the defect $d$ is equal to zero
% precisely in the first column and the first row. An approximation with
% $d=0$ is sometimes said to be _nondegenerate_. It can have more points of
% equioscillation than the generic number $m+n+2$, but never fewer.
%%
% As mentioned above, the theory of equioscillation and degeneracies is
% very appealing mathematically. As an example we note a result due to
% Werner [1964], in completion of earlier work of Maehly and Witzgall [1960]: the
% type $(m,n)$ _best approximation operator_, which maps functions $f$ to
% their best approximations $r_{mn}^*$, is continuous at $f$ with respect
% to the supremum norm if and only if $f\in {\cal R}_{mn}$ or the corresponding
% function $r_{mn}^*$ is nondegenerate.
% The essential reason for this effect is that if a function $r^*$
% is the best approximation to $f$ in a nontrivial square block, then a
% small perturbation $f \to \tilde f$ might fracture that block into pieces
% of size $1\times 1$ [Trefethen 1984]. If $(m,n)$ corresponds to a degenerate position in
% the block, with $d>0$, then the best approximation $\tilde r^*$ for such
% an $\tilde f$ would need to have a higher equioscillation number than
% that of $r^*$ for $f$, requiring $\tilde r^*$ to be far from $r^*$ if
% $\|f-r^*\|$ is positive.
%%
%
% These complications hint at some of the practical difficulties of
% rational approximation. For example, the Remez algorithm is based on
% explicit manipulation of alternant sets. If
% the number of extremal points is not known a priori, it is plausible that
% one may expect numerical difficulties in certain circumstances. Indeed,
% this is the case, and so far as I am aware, no implementation of the
% Remez algorithm for rational approximation, including Chebfun's, can be
% called fully robust. Other kinds of algorithms may have better prospects.
%
%%
%
% We finish by returning to the matter of best {\em complex\/} approximations
% to real functions. Nonuniqueness of certain complex rational
% approximations was pointed out by Walsh in the 1930s. Later Lungu [1971]
% noticed, following a suggestion of Gonchar, that the nonuniqueness arises
% even for approximation of a real function $f$ on $[-1,1]$, with
% examples as simple as type $(1,1)$ approximation of $|x|$. (Exercise 24.3
% gives another proof that there must exist such examples.) These
% observations were rediscovered independently by Saff and Varga [1978a].
% Ruttan [1981] showed that complex best approximations are always better
% than real ones in the strict lower-right triangle of a square block, that
% is, when a type $(m,n)$ best approximation equioscillates in no more than
% $m+n+1$ points. Trefethen and Gutknecht [1983a] showed that for every
% $(m,n)$ with $n\ge m+3$, examples exist where the ratio of the optimal
% complex and real errors is arbitrarily small. Levin, Ruttan and Varga
% showed that the minimal ratio is exactly $1/3$ for $n=m+2$ and exactly
% $1/2$ for $1\le n\le m+1$ [Ruttan \& Varga 1989]. None of this has much
% to do with practical approximation, but it is fascinating.
%
%%
%
% \begin{displaymath}
% \framebox[4.7in][c]{\parbox{4.5in}{\vspace{2pt}\sl
% {\sc Summary of Chapter 24.}
% Any real function $f\in C([-1,1])$ has a unique best approximation $r^*
% \in {\cal R}_{mn}^{\hbox{\scriptsize\rm\kern.1em real}}$ with respect to the
% $\infty$-norm, and $r^*$ is characterized by having an error curve that
% equioscillates between at least $m+n+2-d$ extreme points, where $d$ is the
% defect of $r$ in ${\cal R}_{mn}$. In the Walsh table of all best approximations
% to $f$ indexed by $m$ and $n$, repeated entries, if any, lie in exactly
% square blocks.\vspace{2pt}}}
% \end{displaymath}
%
%%
% \small\smallskip\parskip=2pt
% {\bf Exercise 24.1. Approximating even functions.} Prove that if a real
% function $f\in C([-1,1])$ is even, then its real best approximations of all
% types $(m,n)$ are even.
% \par
% {\bf Exercise 24.2. Approximating the Gaussian.} The first figures of
% this chapter considered lower degree polynomial and rational
% approximations of $\exp(-100 x^2)$ on $[-1,1]$. Make a plot of the
% errors in approximations of types $(n,0)$ and $(n,n)$, now taking $n$ as
% high as you can. (You may find that the {\tt cf} command takes you farther
% than {\tt remez}.) How do the polynomial and rational approximations compare?
% \par
% {\bf Exercise 24.3. Complex approximations and nonuniqueness.}
% (a) Suppose a real function $f\in C([-1,1])$ takes both the values $1$
% and $-1$. Prove that no real rational function $r \in
% {\cal R}_{0n}^{\hbox{\scriptsize\kern.1em real}}$, for any $n$, can have
% $\|f-r\|<1$.
% (b) On the other hand, show that for any $\varepsilon>0$, there is a
% complex rational function $r \in {\cal R}_{0n}$ for some $n$ with
% $\|f-r\|<\varepsilon$. (Hint: perturb $f$ by an imaginary constant and
% consider its reciprocal.)
% (c) Conclude that type $(0,n)$ complex rational best approximations in
% $C([-1,1])$ are nonunique in general for large enough $n$.
% \par
% {\bf Exercise 24.4. A function with a spike.} Plot chebfuns of the
% function (24.2) for $\varepsilon = 1, 0.1, \dots, 10^{-6}$ and determine
% the polynomial degree $n(\varepsilon)$ of the chebfun in each case. What
% is the observed asymptotic behavior of $n(\varepsilon)$ as
% $\varepsilon\to 0\kern .7pt $? How accurately can you explain this observation based
% on the theory of Chapter 8?
% \par
% {\bf Exercise 24.5.} $\hbox{\bf de la Vall\'ee Poussin lower bound.}$
% Suppose an approximation
% $r \in {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$
% to $f\in C([-1,1])$ approximately
% equioscillates in the sense that there are points
% $-1\le s_0< s_1 < \cdots < s_{m+n+1-d}\le 1$ at which $f-r$
% alternates in sign with $|f(s_j)-r(s_j)| \ge \varepsilon$
% for some $\varepsilon > 0$, where $d$ is the defect of $r$ in
% ${\cal R}_{mn}$. Show that the best approximation
% $r^* \in {\cal R}_{mn}^{\hbox{\scriptsize\kern.1em real}}$
% satisfies $\|f-r^*\|\ge \varepsilon$. (Compare Exercise 10.3.)
% \par
% {\bf Exercise 24.6. A rational lethargy theorem.}
% Let $\{\varepsilon_n\}$
% be a sequence decreasing monotonically to $0$.
% Adapt the proof of Exercise 10.7 to show that that there is a function
% $f\in C([-1,1])$ such that $\|f-r_{nn}^*\|\ge \varepsilon_n$ for all $n$.
% \par