%% Condition numbers of various bases
% Nick Trefethen, September 2010
%%
% (Chebfun example linalg/CondNos.m)
% [Tags: #linearalgebra, #polynomials, #qrdecomposition, #conditionnumber]
%%
% [revised June 2019]
%%
% Chebfun can compute the condition number of a set of functions on an
% interval. That's a condition number for continuous functions, not discrete
% approximations.
%%
% For example, here we take the first $12$ Chebyshev polynomials on $[-1,1]$:
N = 11;
A = chebpoly(0:N);
plot(A)
fprintf('Condition no. for Chebyshev polynomials: %8.3f\n',cond(A))
%%
% Legendre polynomials are not much different:
A = legpoly(0:N);
plot(A)
fprintf('Condition no. for Legendre polynomials: %8.3f\n',cond(A))
%%
% Here are the Legendre polynomials normalized by having unit norm rather than
% by taking the value $1$ at $x=1$. Since the functions are orthonormal, the
% condition number is $1$.
A = legpoly(0:N,'norm');
plot(A)
fprintf('Condition no. for normalized Legendre polynomials: %8.3f\n',cond(A))
%%
% All of these condition numbers are fine for numerical work.
% Monomials, by contrast, are exponentially ill-conditioned:
x = chebfun('x');
A = x^(0:N);
plot(A)
fprintf('Condition no. for monomials: %8.3f\n',cond(A))
%%
% Now what exactly do these condition numbers mean? Here is an explanation
% following Chapter 4 of [2]. $A$ is a "quasimatrix", a matrix with $12$
% "columns" that are not vectors but functions of the variable $x$ on
% $[-1,1]$. This quasimatrix represents a mapping from the space $R^{12}$ of
% vectors of dimension $12$ into the $12$-dimensional subspace of degree $11$
% polynomials in $L^2([-1,1])$, the infinite-dimensional space of
% square-integrable functions defined on $[-1,1]$. Now suppose we consider
% the unit ball in $R^{12}$, that is, the set of all $12$-vectors whose
% $2$-norm is $<1$. The quasimatrix $A$ maps this ball into an
% $12$-dimensional hyperellipsoid, a kind of $12$-dimensional pancake. The
% condition number $\mbox{cond}(A)$ is equal to the ratio of the largest
% dimension of the pancake to the smallest. When the columns of $A$ are
% monomials, the pancake is very flat indeed.
%% References
%
% 1. L. N. Trefethen, Householder triangularization of a quasimatrix, _IMA
% Journal of Numerical Analysis_, 30 (2010), 887--897.
%
% 2. L. N. Trefethen and D. Bau, III, _Numerical Linear Algebra_, SIAM, 1997.