function [ n_data, n, x, fx ] = cheby_t_poly_values ( n_data )
%*****************************************************************************80
%
%% cheby_t_poly_values() returns values of Chebyshev polynomials T(n,x).
%
% Discussion:
%
% In Mathematica, the function can be evaluated by:
%
% ChebyshevT[n,x]
%
% Chebyshev polynomials are useful as a basis for representing the
% approximation of functions since they are well conditioned, in the sense
% that in the interval [-1,1] they each have maximum absolute value 1.
% Hence an error in the value of a coefficient of the approximation, of
% size epsilon, is exactly reflected in an error of size epsilon between
% the computed approximation and the theoretical approximation.
%
% Typical usage is as follows, where we assume for the moment
% that the interval of approximation is [-1,1]. The value
% of N is chosen, the highest polynomial to be used in the
% approximation. Then the function to be approximated is
% evaluated at the N+1 points XJ which are the zeroes of the N+1-th
% Chebyshev polynomial. Let these values be denoted by F(XJ).
%
% The coefficients of the approximation are now defined by
%
% C(I) = 2/(N+1) * sum ( 1 <= J <= N+1 ) F(XJ) T(I)(XJ)
%
% except that C(0) is given a value which is half that assigned
% to it by the above formula,
%
% and the representation is
%
% F(X) approximated by sum ( 0 <= J <= N ) C(J) T(J)(X)
%
% Now note that, again because of the fact that the Chebyshev polynomials
% have maximum absolute value 1, if the higher order terms of the
% coefficients C are small, then we have the option of truncating
% the approximation by dropping these terms, and we will have an
% exact value for maximum perturbation to the approximation that
% this will cause.
%
% It should be noted that typically the error in approximation
% is dominated by the first neglected basis function (some multiple of
% T(N+1)(X) in the example above). If this term were the exact error,
% then we would have found the minimax polynomial, the approximating
% polynomial of smallest maximum deviation from the original function.
% The minimax polynomial is hard to compute, and another important
% feature of the Chebyshev approximation is that it tends to behave
% like the minimax polynomial while being easy to compute.
%
% To evaluate a sum like
%
% sum ( 0 <= J <= N ) C(J) T(J)(X),
%
% Clenshaw's recurrence formula is recommended instead of computing the
% polynomial values, forming the products and summing.
%
% Assuming that the coefficients C(J) have been computed
% for J = 0 to N, then the coefficients of the representation of the
% indefinite integral of the function may be computed by
%
% B(I) = ( C(I-1) - C(I+1))/2*(I-1) for I=1 to N+1,
%
% with
%
% C(N+1)=0
% B(0) arbitrary.
%
% Also, the coefficients of the representation of the derivative of the
% function may be computed by:
%
% D(I) = D(I+2)+2*I*C(I) for I=N-1, N-2, ..., 0,
%
% with
%
% D(N+1) = D(N) = 0.
%
% Some of the above may have to adjusted because of the irregularity of C(0).
%
% Differential equation:
%
% (1-X*X) * Y'' - X * Y' + N * N * Y = 0
%
% Formula:
%
% T(N)(X) = cos ( N * arccos(X) )
%
% First terms:
%
% T(0)(X) = 1
% T(1)(X) = 1 X
% T(2)(X) = 2 X^2 - 1
% T(3)(X) = 4 X^3 - 3 X
% T(4)(X) = 8 X^4 - 8 X^2 + 1
% T(5)(X) = 16 X^5 - 20 X^3 + 5 X
% T(6)(X) = 32 X^6 - 48 X^4 + 18 X^2 - 1
% T(7)(X) = 64 X^7 - 112 X^5 + 56 X^3 - 7 X
%
% Inequality:
%
% abs ( T(N)(X) ) <= 1 for -1 <= X <= 1
%
% Orthogonality:
%
% For integration over [-1,1] with weight
%
% W(X) = 1 / sqrt(1-X*X),
%
% if we write the inner product of T(I)(X) and T(J)(X) as
%
% < T(I)(X), T(J)(X) > = integral ( -1 <= X <= 1 ) W(X) T(I)(X) T(J)(X) dX
%
% then the result is:
%
% 0 if I /= J
% PI/2 if I == J /= 0
% PI if I == J == 0
%
% A discrete orthogonality relation is also satisfied at each of
% the N zeroes of T(N)(X): sum ( 1 <= K <= N ) T(I)(X) * T(J)(X)
% = 0 if I /= J
% = N/2 if I == J /= 0
% = N if I == J == 0
%
% Recursion:
%
% T(0)(X) = 1,
% T(1)(X) = X,
% T(N)(X) = 2 * X * T(N-1)(X) - T(N-2)(X)
%
% T'(N)(X) = N * ( -X * T(N)(X) + T(N-1)(X) ) / ( 1 - X^2 )
%
% Special values:
%
% T(N)(1) = 1
% T(N)(-1) = (-1)**N
% T(2N)(0) = (-1)**N
% T(2N+1)(0) = 0
% T(N)(X) = (-1)**N * T(N)(-X)
%
% Zeroes:
%
% M-th zero of T(N)(X) is cos((2*M-1)*PI/(2*N)), M = 1 to N
%
% Extrema:
%
% M-th extremum of T(N)(X) is cos(PI*M/N), M = 0 to N
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 16 September 2004
%
% Author:
%
% John Burkardt
%
% Reference:
%
% Milton Abramowitz and Irene Stegun,
% Handbook of Mathematical Functions,
% US Department of Commerce, 1964.
%
% Stephen Wolfram,
% The Mathematica Book,
% Fourth Edition,
% Wolfram Media / Cambridge University Press, 1999.
%
% Input:
%
% integer N_DATA. The user sets N_DATA to 0 before the first call.
%
% Output:
%
% integer N_DATA. On each call, the routine increments N_DATA by 1, and
% returns the corresponding data; when there is no more data, the
% output value of N_DATA will be 0 again.
%
% integer N, the order of the function.
%
% real X, the point where the function is evaluated.
%
% real FX, the value of the function.
%
n_max = 13;
fx_vec = [ ...
0.1000000000000000E+01, ...
0.8000000000000000E+00, ...
0.2800000000000000E+00, ...
-0.3520000000000000E+00, ...
-0.8432000000000000E+00, ...
-0.9971200000000000E+00, ...
-0.7521920000000000E+00, ...
-0.2063872000000000E+00, ...
0.4219724800000000E+00, ...
0.8815431680000000E+00, ...
0.9884965888000000E+00, ...
0.7000513740800000E+00, ...
0.1315856097280000E+00 ];
n_vec = [ ...
0, 1, 2, ...
3, 4, 5, ...
6, 7, 8, ...
9, 10, 11, ...
12 ];
x_vec = [ ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00, ...
0.8E+00 ];
if ( n_data < 0 )
n_data = 0;
end
n_data = n_data + 1;
if ( n_max < n_data )
n_data = 0;
n = 0;
x = 0.0;
fx = 0.0;
else
n = n_vec(n_data);
x = x_vec(n_data);
fx = fx_vec(n_data);
end
return
end