%% 25. Two famous problems
ATAPformats
%%
% In this chapter we discuss two problems of rational approximation that
% have been the focus of special attention over the years: approximation of
% $|x|$ on $[-1,1]$, a prototype of approximation of non-smooth functions,
% and approximation of $e^x$ on $(-\infty,0\kern .5pt ]$, a prototype of
% approximation on unbounded domains. Both stories go back many decades
% and feature initial theorems, later conjectures based on numerical
% experiments, and eventual proofs of the conjectures based on mathematical
% methods related to potential theory. We shall not present the proofs of
% the sharpest results, but we shall show that the essential rates of
% approximation can be achieved by using the trick that appears several
% times in this book: if a function $f(x)$ can be written as an integral
% with respect to a variable $s$, then an approximation $r(x)$ in partial
% fractions form is obtained by applying a quadrature formula (19.3) to the
% integral.
%%
%
% The problem of approximation of $|x|$ on $[-1,1]$ originates at the beginning
% of the 20th century, when polynomial approximations of this function were
% of interest to Lebesgue, de la $\hbox{Vall\'ee}$ Poussin, Jackson, and
% Bernstein. This was an era when the fundamental results of
% approximability were being developed, and $|x|$ served as a function from
% which many other results could be derived. Bernstein's prize-winning
% article on the subject ran for 104 pages [1912{\sc b}] and was followed
% by another of 57 pages [1914{\sc b}]. Among other
% things, Bernstein proved that in best polynomial approximation of $|x|$
% as $n\to\infty$, the errors decrease linearly but no faster, that
% is, at the rate $O(n^{-1})$ but not $o(n^{-1})$.
%
%%
% Why linearly? This is an example of the fundamental fact of
% approximation theory which we mentioned first in Chapter 7: the close
% connection between the smoothness of a function and its rate of
% approximation. The function $f(x) = |x|$ has a derivative of bounded
% variation $V=2$ on $[-1,1]$, so by Theorem 7.2, its Chebyshev projections
% $\{f_n\}$ satisfy
% $$ \| f-f_n \| \le {4\over \pi (n-1)} $$
% for $n\ge 2$, and its Chebyshev interpolants $\{p_n\}$ satisfy the same
% bound with 4 replaced by 8. Thus approximations to $|x|$ converge at
% least at the rate $O(n^{-1})$. What Bernstein showed is that the rate is
% in fact no better than this: no approximations to $|x|$ can beat
% Chebyshev projection or interpolation by more than a constant factor. Or
% to put it another way, convergence of polynomial approximants to a
% function $f$ at a rate faster than $O(n^{-1})$ implies that $f$ is in
% some sense smoother than $|x|$. Such results in the direction
% _approximability_ $\Longrightarrow$ _smoothness_ go by the general name
% of _Bernstein theorems_. In this book we have presented one result of
% this kind: Theorem 8.3, asserting that geometric convergence implies
% analyticity.
%%
%
% It is hard not to be curious about the constants. Bernstein in fact
% proved in [Bernstein 1914{\sc b}] that there exists a number $\beta$ such that
% the best approximation errors satisfy
% $$ E_n (|x|) \sim {\beta\over n} \eqno (25.1) $$
% as $n\to\infty$, and he obtained the bound
% $$ 0.278 < \beta < 0.286. $$
% (Theorem 7.2 gives $\beta \le 4/\pi \approx 1.27$.) He noted as a ``curious
% coincidence'' that
% $1/2\sqrt \pi \approx 0.28209\dots$ falls in this range, and the idea
% that $\beta$ might take exactly this value
% became known as {\em Bernstein's conjecture.} Seventy years later, Varga and
% Carpenter [1985] investigated the problem numerically to great accuracy
% and found that in fact
% $$ \beta \approx 0.28016949902386913303643649\dots. $$
% (Of course the difference between 0.282 and 0.280 would not have the
% slightest practical importance.) Along with this numerical result, which
% was based on Richardson extrapolation, Varga and Carpenter established
% the rigorous bounds
% $$ 0.2801685 < \beta < 0.2801734. \eqno (25.2) $$
%
%%
% \vskip -2.5em
%%
% For example, here are the values of $nE_n(|x|)$ for $n = 1,2,4, \dots,
% 64$, showing quadratic convergence to the limit value. A comparison with
% the much more accurate Table 2.1 of [Varga & Carpenter 1985] indicates
% that the Chebfun results are accurate in all but the last digit or two.
%%
% \vskip -2em
x = chebfun('x'); f = abs(x); limit = 0.280169499023869133;
disp(' n n*err n*err - limit')
for n = 2.^(0:6)
[p,err] = remez(f,n);
fprintf('%14d %16.8f %16.2e\n',n,n*err,n*err-limit)
end
%%
% Now all this is for polynomial approximation. What about rational
% functions? As mentioned in Chapter 23, the dramatic discovery here
% came from Donald Newman, fifty years after Bernstein: best rational
% approximants to $|x|$ converge ``root-exponentially''. Newman's bounds
% were these:
% $$ {1\over 2} \kern .5pt e^{-9\sqrt n\kern 1pt}
% \le E_{nn}(|x|) \le 3\kern .5pt e^{-\sqrt n}. \eqno (25.3) $$
% We have already seen in the second plot of Chapter 23 what an improvement
% in convergent rate this is
% as compared with (25.1). For approximating non-smooth functions,
% rational functions can be far more powerful than polynomials.
%%
% Again mathematicians could not resist trying to sharpen the constants.
% First, Vyacheslavov [1975] found that the exact exponent is midway
% between Newman's bounds of 1 and 9: it is $\pi$. Then Varga, Ruttan and
% Carpenter [1993] performed computations with a version of the Remez
% algorithm to 200 decimal places, leading to numerical evidence for the
% conjecture $$ E_{nn} \sim 8 \kern .5pt e^{-\pi \sqrt n } $$
% as $n\to\infty$. Soon afterwards this result was proved by Stahl [1993].
% Later Stahl generalized the result to approximation of $x^\alpha$ on
% $[0,1]$ for any $\alpha>0$ [Stahl 2003].
%%
% The following theorem summarizes the results we have mentioned.
%%
%
% {\bf Theorem 25.1. Approximation of \boldmath $|x|$ on $[-1,1]$.} {\em
% The errors in best polynomial and rational approximation of $|x|$ on
% $[-1,1]$ satisfy as $n\to\infty$
% $$ E_{n0}(|x|) \sim {\beta\over n}, \quad \beta = 0.2801\dots \eqno (25.4) $$
% and
% $$ E_{nn}(|x|) \sim 8\kern .5pt e^{-\pi\sqrt n}. \eqno (25.5) $$}
% \vskip -1em
%
%%
% _Proof._
% Equation (25.4) is due to Varga and Carpenter [1985] and (25.5) is due to
% Stahl [1993].
% $~\hbox{\vrule width 2.5pt depth 2.5 pt height 3.5 pt}$
%%
% _Why_ can rational approximations of $|x|$ achieve $O(C^{-\sqrt n}\kern 1pt)$
% accuracy? The crucial fact is that the poles of $r$ can be chosen to
% cluster near the singular point $x=0$. In particular, a good choice is
% to make the poles approach $0$ geometrically, for each fixed $n$, with a
% geometric factor depending on $\sqrt n$.
%%
% Here is a derivation of a rational approximation that achieves the right
% root-exponential convergence. (Arguments like this have been made by
% Stenger in various publications; see for example [Stenger 1986].) We
% start from the identity
% $$ {1\over |x|} = {2\over \pi} \int_0^\infty {dt\over t^2 + x^2}, $$
% which is derived in calculus courses. Multiplying by $x^2$ gives
% $$ |x| = {2x^2\over \pi} \int_0^\infty {dt\over t^2+x^2}. \eqno (25.6) $$
% (This formula is perhaps due to Roberts [1980], though the essence of the
% matter dates to Zolotarev in the 1870s.) The change of variables $t =
% e^s$, $dt = e^s ds$ converts this to
% $$ |x| = {2x^2\over \pi} \int_{-\infty}^\infty {e^s ds\over e^{2s}+x^2},
% \eqno (25.7) $$
% which is an attractive integral to work with because the integrand decays
% exponentially as $|s|\to\infty$. We now get a rational approximation of
% $|x|$ by approximating this integral by the trapezoid rule with node
% spacing $h>0$:
% $$ r(x) = {2hx^2\over \pi} \sum_{k = -(n-2)/4}^{(n-2)/4} {e^{kh}\over e^{2kh} + x^2}.
% \eqno (25.8) $$
% Here $n$ is a positive even number, and there are $n/2$ terms in the sum,
% so $r(x)$ is a rational function of $x$ of type $(n,n)$. There are two
% sources of error that make $r(x)$ differ from $|x|$. The fact that the
% sum has been terminated at a limit $n<\infty$ introduces an error on the
% order of $e^{-nh/4}$, and the finite step size $h>0$ introduces an error
% on the order of $e^{-\pi^2/h}$. (The integrand is analytic in the strip
% around the real $s$-axis of half-width $a=\pi/2$, corresponding to a
% convergence rate $e^{-2\pi a/h}$.) Balancing these sources of error
% suggests the condition $e^{-nh/4} \approx e^{-\pi^2/h}$, that is,
% $$ h \approx 2\pi/\sqrt n, \eqno (25.9) $$
% with error of order
% $$ e^{-(\pi/2) \sqrt n}. \eqno (25.10) $$
%%
% We can see these approximations with an experiment.
%%
% \vskip -2em
for n = 2:2:12
r = 0*x; h = 2*pi/sqrt(n);
for k = -(n-2)/4:(n-2)/4
r = r + exp(k*h)./(exp(2*k*h)+x.^2);
end
r = (2*h/pi)*x.^2.*r; err = norm(f-r,inf);
subplot(3,2,n/2), plot(r), ylim([0 1])
ss = sprintf('(%1d,%1d) error = %5.3f',n,n,err);
FS = 'fontsize'; text(-.5,.78,ss,FS,8)
end
%%
% \vskip 1pt
%%
% The poles of (25.8)--(25.9) in the $x$-plane lie at
% $$ \pm i \kern .5pt e^{2\pi k/\sqrt n}. \eqno (25.11) $$
% Here are these numbers (those in the upper half-plane) for the six
% approximations plotted above, showing the wide range of amplitudes
% associated with the exponential spacing.
%%
% \vskip -2em
disp('Poles of rational approximants to |x|:')
for n = 2:2:12
h = 2*pi/sqrt(n); k = -(n-2)/4:(n-2)/4; y = exp(k*h);
fprintf('%8.2ei ',y), disp(' ')
end
%%
% The approximations aren't optimal, but they are close. The convergence
% rate (25.10) as $n\to\infty$ is one-quarter of the optimal rate (25.5) in
% the sense that we need $4$ times as large a value of $n$ to achieve a
% certain accuracy in (25.10) as in (25.5).
%%
% Above, we computed errors for best polynomial approximations to $|x|$
% with the Chebfun command |remez|. In the rational case, |remez| does not
% succeed in computing best approximations beyond a certain low order. This
% difficulty is related to the exponential spacing of the oscillations of
% $f-r^*$ near $x=0$.
%%
% It is worth noting that the problem of approximating $|x|$ on $[-1,1]$ is
% equivalent to certain other approximation problems. If $r(x)$ is a type
% $(m,n)$ approximation to $|x|$ on $[-1,1]$, then normally $r$ will be an
% even function of $x$ and $m$ and $n$ can be taken to be even too. Thus
% $r(x) = \tilde r (x^2)$, where $\tilde r$ is a rational function of type
% $(m/2,n/2)$. Since $\tilde r(x^2)$ approximates $|x|$ for $x\in [-1,1]$,
% $\tilde r(x)$ approximates $\sqrt x$ for $x\in [0,1]$. This reasoning
% holds for any approximations, and in particular, by counting
% equioscillations one finds that best type $(m,n)$ approximation of $|x|$
% on $[-1,1]$ is equivalent to best type $(m/2,n/2)$ approximation of
% $\sqrt x$ on $[0,1]$. The following pair of plots illustrates this
% equivalence. Notice that the error curves are the same apart from the
% scaling of the $x$-axis.
%%
% \vskip -2em
f = abs(x); [p,q,rh,err] = remez(f,2,2); clf
subplot(1,2,1), plot(f-p./q), hold on, ylim(.08*[-1 1])
plot([-1 1],err*[1 1],'--k'), plot([-1 1],-err*[1 1],'--k')
title('Error in type (2,2) approx to |x|',FS,9)
f = chebfun('sqrt(x)',[0,1],'splitting','on');
[p,q,rh,err] = remez(f,1,1);
subplot(1,2,2), plot(f-p./q), hold on, axis([-.03 1 .08*[-1 1]])
plot([-.03 1],err*[1 1],'--k'), plot([0 1],-err*[1 1],'--k')
title('Error in type (1,1) approx to sqrt(x)',FS,9)
%%
% \vskip 1pt
%%
% For applications in scientific computing, the approximation of $\sqrt x$
% on an interval $[a,b]$ is
% particularly interesting because of the case in which $x$ is a matrix
% $A$ with eigenvalues in $[a,b]$, which might come from discretizing a
% differential operator. Rational approximations of the square root lead
% to powerful algorithms for evaluating $A^{1/2} v$ for vectors $v$, as
% described in [Hale, Higham & Trefethen 2008] and [Higham 2008].
% At the other end of the historical spectrum,
% approximation of square roots was the problem addressed by
% Poncelet in the very first paper on minimax approximation [Poncelet 1835].
%%
% We now turn to the second of the famous problems of this chapter:
% approximation of $e^x$ on $(-\infty,0\kern .5pt ]$. This problem was
% introduced in a paper of Cody, Meinardus and Varga [1969], which drew
% attention to the connection of such approximations with the numerical
% solution of partial differential equations, since a rational
% approximation can be used to compute the exponential of a matrix arising
% from a numerical discretization [Moler \& Van Loan 2003].\footnote{The
% Cody--Meinardus--Varga paper was important in my life. As a graduate
% student in the Numerical Analysis Group at Stanford, I happened to come
% across it one evening around 1980 in a pile of Gene Golub's discarded
% reprints---``help yourself''. Its mix of theory and numerical
% calculations appealed to me greatly and led to my computation of the
% constant $9.28903\dots$ a few years later [Trefethen \& Gutknecht
% 1983b].} Curiously, despite that good motivation from applied
% mathematics, the influence of this paper was mainly in theoretical
% approximation theory for quite a few decades, until computers and
% numerical linear algebra had advanced to the point where it became more
% practical to take advantage of algorithms based on rational functions.
%
%%
% The first thing we may note about approximation of $e^x$ on
% $(-\infty,0\kern .5pt ]$ is that polynomials cannot do the job at all.
% Since any non-constant polynomial $p(x)$ diverges to $\pm \infty$ as
% $x\to-\infty$, the only polynomials that can approximate $e^x$ with
% finite error on $(-\infty,0\kern .5pt ]$ are constants, so the minimax
% error can never be less than $1/2$.
%%
%
% Inverse-polynomials of the form $1/p_n(x)$, however, can be chosen to
% converge geometrically. This makes sense when you consider that $e^{x}$
% on $(-\infty,0\kern .5pt ]$ is the same as $1/e^x$ for $x\in[0,\infty)$.
% Cody, Meinardus and Varga noted that to achieve geometric convergence, it
% is enough to consider $1/p_n(x)$, where $p_n$ is the degree-$n$
% truncation of the Taylor series for $e^x$. They showed that these
% approximations converge at a rate $O(2^{-n})$, and then they improved
% this rate to $O(2.298^{-n})$ by a shift of origin. It was later proved
% by Sch\"onhage [1973] that the optimal rate for inverse-polynomials is
% $O(3^{-n})$.
%
%%
%
% Since $1/p_n(x)$ is a rational function of type $(n,n)$, these
% observations tell us that best rational type $(n,n)$ approximations to
% $e^x$ on $(-\infty,0\kern .5pt ]$ converge at least geometrically. Newman
% [1974] proved that the convergence is no faster than geometric. What is
% the optimal rate? With twice as many parameters to work with as with
% inverse-polynomials, one might guess that it should be $O(9^{-n})$, and
% this idea became known in the 1970s as the {\em $1/9$ conjecture.} In fact,
% the optimal convergence rate turned out to be $O(H^{n})$ with $H \approx
% 1/9.28903,$ a number now known as {\em Halphen's constant}, equal to the
% unique positive root of the equation
% $$ h(s) = \sum_{k=1}^\infty {ks^n\over 1 - (-s)^n} = {1\over 8}.
% \eqno(25.12) $$
% This number was conjectured numerically based on Carath\'eodory--Fej\'er
% singular values by Trefethen and Gutknecht [1983{\sc b}], verified to many
% digits by high-precision Remes algorithms by Carpenter, Ruttan and Varga
% [1984], conjectured to have the exact value associated with a certain
% problem of elliptic functions treated by Halphen [1886] by Magnus via the
% Carath\'eodory--Fej\'er method [Magnus 1985, Magnus \& Meinguet 2000],
% and then proved using quite
% different methods of potential theory by Gonchar and Rakhmanov [1989].
% This work represents a fascinating and important line of investigation in
% approximation theory, and for a summary of many of the ideas with
% wide generalizations to related problems, a good place to start is [Stahl \&
% Schmelzer 2009]. Presentations of some of the potential theory underlying
% results in this area can be found in [Saff \& Totik 1997].
%
%%
%
% Following the idea presented earlier for $|x|$ on $[-1,1]$, it is
% interesting to see what can be achieved for this problem by the trapezoid
% rule approximation of a contour integral. Here is a derivation of a
% rational approximation that achieves the rate $O((2.849\dots)^{-n})$,
% adapted from [Weideman \& Trefethen 2007]; such approximations are
% discussed more generally in [Trefethen, Weideman \& Schmelzer 2006]. We
% begin with a Laplace transform identity that is easily proved by residue
% calculus,
% $$ e^x = {1\over 2\pi i} \int {e^t dt\over t-x} $$
% for $x\in (-\infty,0\kern .5pt ]$, where the integral is over any contour
% in the complex plane that starts at $-\infty$ below the $t$-axis, circles
% around $t=0$, and finishes at $-\infty$ above the $t$-axis. Choosing the
% contour to be a parabola, we convert this to an integral over the real
% $s$-axis by the change of variables
% $$
% t = (is+a)^2, \quad dt = 2i(is+a) ds
% $$
% for some constant $a>0$, which gives
% $$ e^x = {1\over \pi} \int {e^{(is+a)^2} (is+a) ds\over (is+a)^2-x} .
% \eqno (25.13) $$
% As in (25.8), we now approximate this integral by the trapezoid rule with
% node spacing $h>0$:
% $$ r(x) = {h\over \pi} \sum_{k = -(n-1)/2}^{(n-1)/2}
% {e^{(ikh+a)^2}(ikh+a)\over (ikh+a)^2 - x}.
% \eqno (25.14) $$
% Here $n$ is a positive even number, and since $x$ rather than $x^2$
% appears in each term we now take $n$ terms in the sum rather than $n/2$
% as in (25.8) to make $r(x)$ a rational function of $x$ of type $(n,n)$.
%
%%
%
% This time, the integral has square-exponential rather than just
% exponential decay as $s\to\infty$, so choosing $h = O(1/\sqrt n\kern
% 1pt)$ is enough to make the errors from endpoint truncation exponentially
% small. We also have the parameter $a$ to play with. By taking $a=
% O(\sqrt n\kern 1pt)$, we can make the errors due to grid spacing
% exponentially small too, and in this fashion we can achieve geometric
% convergence. More precisely, the choices
% $$ a = \sqrt{{\pi n\over 24}}, \quad h = \sqrt{{3\pi\over 2n}} \eqno (25.15) $$
% lead to the convergence rate
% $$ \|f-r_{nn}\| = O(e^{-\pi n /3}) \approx O((2.849\dots)^{-n}). \eqno (25.16) $$
% As before, we can see these approximations with an experiment,
% this time plotting $f-r$ rather than $r$ itself.
%
%%
% \vskip -2em
x = chebfun('x',[-2,-.01]); f = exp(x);
for n = 2:2:8
r = 0*x; h = sqrt(3*pi/(2*n)); a = sqrt(pi*n/24);
for k = -(n-1)/2:(n-1)/2
r = r + exp((1i*k*h+a)^2)*(1i*k*h+a)./((1i*k*h+a)^2-x);
end
r = (h/pi)*real(r); subplot(2,2,n/2), plot(f-r)
err = norm(f-r,inf); ss = sprintf('(%1d,%1d) error = %7.5f',n,n,err);
axis([-2,0,1.3*err*[-1 1]]), text(-1.9,.85*err,ss,FS,8)
end
%%
% \vskip 1pt
%%
% Let us summarize these results with a theorem, which goes further to
% include the precise leading-order asymptotic behavior of the best
% approximation errors as conjectured by Magnus [1994] and proved by
% Aptekarev [2002].
%%
%
% {\bf Theorem 25.2. Approximation of \boldmath$e^x$ on $(-\infty,0\kern .5pt ]$.}
% {\em The errors in best type $(0,n)$ and $(n,n)$ rational
% approximation of $\exp(x)$ on $(-\infty,0\kern .5pt ]$ satisfy
% as $n\to\infty$
% $$ \lim_{n\to\infty} E_{0n}^{1/n} = {1\over 3} \eqno (25.17) $$
% and
% $$ E_{nn} \sim 2 \kern .4pt H^{n+1/2},
% \quad H = 1/9.2890254919208\dots \eqno (25.18) $$}
% \vskip -1em
%
%%
%
% {\em Proof.} Equation (25.17) is due to Sch\"onhage [1973] and (25.18)
% to Aptekarev [2002], extending the earlier result on $n$th root
% asymptotics and the constant $H$ by Gonchar and Rakhmanov [1989].
% $~\hbox{\vrule width 2.5pt depth 2.5 pt height 3.5 pt}$
%
%%
% We finish this chapter by showing that the numerical computation of these
% best approximants is surprisingly easy. The crucial matter is to note
% that the change of variables
% $$ x = a{s-1\over s+1} , \qquad s = {a+x\over a-x} \eqno (25.19) $$
% where $a$ is a positive parameter, maps the negative real axis
% $(-\infty,0\kern .5pt ]$ in $x$ to the interval $(-1,1]$ in $s$. Since
% the mapping is a rational function of type $(1,1)$, it transplants a
% rational function of type $(n,n)$ in $s$ or $x$ to a rational function of
% type $(n,n)$ in the other variable. In particular, for the approximation
% of $f(x) = e^x$ on $(-\infty,0\kern .5pt ]$, let us define
% $$ F(s) = e^{a(s-1)/(s+1)}, \quad s\in (-1,1]. \eqno (25.20) $$
% A good choice of the parameter is $a=9$, which has a big effect
% for numerical computation in improving the conditioning of the
% approximation problem. We now find we have a function that can be
% approximated to machine precision by a Chebyshev interpolating polynomial
% $p(s)$ of degree less than 50:
%%
% \vskip -2em
s = chebfun('s',[-1,1]);
F = exp(9*(s-1)./(s+1));
length(F)
%%
% The Chebyshev series of $F$ decreases at a good exponential rate:
%%
% \vskip -2em
clf, chebpolyplot(F), grid on
title(['Convergence of Chebyshev polynomial ' ...
'interpolants to transplanted e^x'],FS,9)
%%
% \vskip 1pt
%%
% This gives us yet another way to compute rational approximations to $e^x$
% on $(-\infty,0\kern .5pt ]$: truncate this Chebyshev series in $s$, then
% transplant by (25.19) to get rational functions in $x$.
%%
% Alternatively, we can get true best approximations from (25.19) by
% applying the Chebfun |remez| command. Here for example is the error for the
% best approximation of type $(8,8)$ plotted in the $s$ variable, showing
% 18 points of equioscillation.
%%
% \vskip -2em
[P,Q,RH,err] = remez(F,8,8); R = P./Q;
hold off, plot(F-R), hold on
plot([-1 1],err*[1 1],'--k'), plot([-1 1],-err*[1 1],'--k')
xlabel('s',FS,9), ylabel error, ylim(2e-8*[-1,1])
title(['Error in type (8,8) approximation'...
' of transplanted e^x'],FS,9)
%%
% \vskip 1pt
%%
% If we plot the same curve in the $x$ variable, it's hard to see much
% because of the varying scale:
%%
% \vskip -2em
s1 = -.999; s2 = .999; s = chebfun('s',[s1 s2]); x = 9*(s-1)./(s+1);
hold off, plot(x,F{s1,s2}-R{s1,s2}), hold on
xx = [-1e4 -1e-2];
plot(xx,err*[1,1],'--k'), plot(xx,-err*[1,1],'--k'), xlim(xx)
xlabel('x',FS,9), ylabel error, ylim(2e-8*[-1,1])
title('Error in type (8,8) approximation of e^x',FS,9)
%%
% \vskip 1pt
%%
% Putting the $x$ axis on a log scale, however, makes the plot informative
% again:
%%
% \vskip -2em
hold off, semilogx(x,F{s1,s2}-R{s1,s2}), hold on
semilogx(xx,err*[1,1],'--k'), plot(xx,-err*[1,1],'--k'), xlim(xx)
xlabel('x',FS,9), ylabel error, ylim(2e-8*[-1,1])
title('Error in type (8,8) approximation of e^x',FS,9)
%%
% \vskip 1pt
%%
% Here is the analogous plot for type $(12,12)$ approximation:
%%
% \vskip -2em
[P,Q,RH,err] = remez(F,12,12); R = P./Q;
hold off, semilogx(x,F{s1,s2}-R{s1,s2}), hold on
plot(xx,err*[1,1],'--k'), plot(xx,-err*[1,1],'--k'), xlim(xx)
xlabel('x',FS,9), ylabel error, ylim(3e-12*[-1,1])
title('Error in type (12,12) approximation of e^x',FS,9)
%%
%
% These plots are modeled after [Trefethen, Weideman \& Schmelzer 2006],
% where it is shown that Carath\'eodory--Fej\'er approximation is equally
% effective and even faster than the Remes algorithm at computing these
% approximations.
%
%%
%
% \begin{displaymath}
% \framebox[4.7in][c]{\parbox{4.5in}{\vspace{2pt}\sl
% {\sc Summary of Chapter 25.}
% Two problems involving rational functions have attracted special
% attention, highlighting the power of rational approximations near
% singularities and on unbounded domains. For approximating $|x|$ on
% $[-1,1]$, best rational functions converge root-exponentially whereas
% polynomials converge linearly. For approximating $e^x$ on
% $(-\infty,0\kern .5pt ]$, best rational functions converge geometrically
% whereas polynomials do not converge at all. Both rates of approximation
% can be achieved by constructing partial fractions from trapezoid rule
% approximations to certain integrals.\vspace{2pt}}}
% \end{displaymath}
%
%%
% \small
% \parskip=2pt
% {\bf Exercise 25.1. Newton iteration for \boldmath $|x|$.}
% (This problem has roots in [Roberts 1980].)
% (a) Let $x$ be a number, and suppose we want to solve the
% equation $r^2 = x^2$ for the unknown $r$ using Newton iteration.
% Show that the iteration formula is $r^{{(k+1)}} = ((r^{(k)})^2+x^2)/2r^{(k)}$.
% (b) If the initial guess is $r^{(0)} = 1$, then for $k\ge 1$, what is
% the smallest $n$ for which the rational function $r^{(k)}(x)$ is of
% type $(n,n)$?
% (c) Use Chebfun to compute and plot the approximations
% $r^{(0)}(x),...,r^{(5)}(x)$ on the interval $[-1,1]$. What is
% the sup-norm error $\||x|-r^{(k)}(x)\|$, and where is it attained?
% (d) What rate of convergence does this correspond to for
% $\||x|-r^{(k)}(x)\|$ as a function of $n$? How does this compare
% with the optimal rate given by Theorem 25.1?
% (e) Make a semilog plot of $|\, |x|-r^{(5)}(x)| $ as a function of
% $x\in [-1,1]$ and comment further on the nature of these rational
% approximations.
% \par
% {\bf Exercise 25.2. An elementary approximant for \boldmath$e^x$ on $(-\infty,0\kern .5pt ]$.}
% A degree $n$ polynomial $p(s)$ on $[-1,1]$ can be
% transplanted to a type $(n,n)$ rational function $r(x)$ on $(-\infty,0\kern .5pt ]$
% by the map (25.19). Combine this observation with Theorem 8.2
% to show that type $(n,n)$ approximants to $e^x$ on $(-\infty,0\kern .5pt ]$ exist
% with accuracy $O(\exp(-Cn^{-2/3}))$ for some $C>0$ as $n\to\infty$.
% \par
% {\bf Exercise 25.3. Computing Halphen's constant.}
% Write a short Chebfun program that computes Halphen's constant to
% 10 or more digits based on the condition (25.12).
% \par
% {\bf Exercise 25.4. Best approximation errors for \boldmath $e^x$.}
% (a) Using {\tt remez} and the change of variables (25.20), compute best approximation
% errors in type $(n,n)$ approximation of $e^x$ on $(-\infty,0\kern .5pt ]$ for
% $n = 0,1,\dots,13$. Plot the results on a log scale and compare them with
% estimates from the asymptotic formula (25.18). Also on a log scale, plot
% the difference between the estimates and the true errors, and comment on the
% results.
% (b) Repeat the computation with CF instead of {\tt remez}. This time, plot
% the different between the CF and true errors on a log scale, and comment on
% the results.
% \par
% {\bf Exercise 25.5. Behavior of approximants of \boldmath $|x|$ in the complex plane.}
% It is shown in [Blatt, Iserles \& Saff 1988] that the type $(n,n)$ best approximants
% to $|x|$ on $[-1,1]$ have all their zeros and poles on the imaginary axis
% and converge to $x$ for $\hbox{Re}(x)> 0$ and to
% $-x$ for $\hbox{Re}(x)< 0$ as $n\to\infty$.
% Verify this result numerically by plotting $|x-r_{nn}^*(x)|$ against $\hbox{Re}(x)$
% for $x\in [-1+0.5i,1+0.5i]$ for $n = 1,2,3,4$.
% \par
% {\bf Exercise 25.6. Behavior of approximants of \boldmath $e^x$ in the complex plane.} It is stated in
% [Stahl \& Schmelzer 2009] that the poles of best type $(n,n)$
% approximations to $e^x$ on $(-\infty,0\kern .5pt ]$ move off to $\infty$ as
% $n\to\infty$, and the convergence at $n$th-root rate governed by $h
% \approx 1/9.28903$ applies on any compact set in the complex plane. With
% this result in mind, produce contour plots in the complex $z$-plane for
% the errors $|e^z - r_{nn}(z)|$ for the approximations (25.14)--(25.15)
% with $n = 2, 4, 6, 8, 10$. Does it appear likely that
% these approximations too converge on all compact sets in the plane?
%