MATLAB Examples

Orthogonal polynomials via the Lanczos process

Pedro Gonnet, 1st November 2011

(Chebfun example approx/OrthPolysLanczos.m)

format short e

The example approx/OrthPolys [2] discusses orthogonal polynomials, defined by the condition

    | w(x) P_j(x) P_k(x) dx = < P_j, P_k > = 0 for all j not equal k

As mentioned there, Chebfun has commands LEGPOLY, CHEBPOLY, LAGPOLY, HERMPOLY for computing some standard cases.

Here, instead of Gram-Schmidt, we construct some of these polynomials via a three-term recurrence relation

  gamma(k+1)*P_k+1(x) = (x - beta(k))*P_k(x) - gamma(k)*P_k-1(x)

where the coefficients gamma(k) and beta(k) are either known in advance or computed from

  gamma(k) = < x*P_k-1 , P_k >,  beta(k) = < x*P_k , P_k >.

Given any positive weight function w(x), we can construct both the polynomials P_k(x) and the recurrence coefficients beta(k) and gamma(k) using the Lanczos algorithm [1].

Whereas the Gram-Schmidt process requires O(n^2) evaluations of the inner product to compute the n first polynomials, the Lanczos process requires only two such evaluations per polynomial. Furthermore, the three-term recurrence coefficients can be used to construct a Gaussian quadrature rule for the given weight function w(x).

We start by initializing the parameters of this set of polynomials, e.g. the variable x in the interval [-1,1], the weight function in that same interval as well as the highest-degree polynomial we wish to construct.

x = chebfun( 'x' , [-1,1] );
w = exp(pi*x);
N = 5;

We then initialize the recursion by constructing the first two polynomials and their recurrence coefficients explicitly.

P = chebfun( 1./sqrt(sum(w)) , domain(x) );
v = x.*P;
beta(1) = sum(w.*v.*P);
v = v - beta(1)*P;
gamma(1) = sqrt(sum( w.*v.^2 ));
P(:,2) = v / gamma(1);

The remaining polynomials and coefficients can be computed iteratively using the Lanczos process.

for k=2:N
    v = x.*P(:,k) - gamma(k-1)*P(:,k-1);
    beta(k) = sum(w.*v.*P(:,k));
    v = v - beta(k)*P(:,k);
    gamma(k) = sqrt(sum( w.*v.^2 ));
    P(:,k+1) = v / gamma(k);

We can now plot these polynomials

title('Orthogonal polynomials on [-1,1] wrt w = exp(pi*x)');

and verify that they are indeed orthogonal with respect to w.

W = repmat(w,1,N+1);
I = P'*(W.*P);
err = norm(I-eye(N+1))
err =

Since, along with the polynomials, we have also computed the coefficients beta and gamma of the three-term recurrence relation, we can also construct Gaussian quadrature rules with respect to the weight function w using the Golub-Welsch algorithm [3]. For N points, this is

J = diag(beta) + diag(gamma(1:N-1),-1) + diag(gamma(1:N-1),+1)
[V,D] = eig( J );
xi = diag( D );
wk = (2*V(1,:).^2)';
[ xi , wk ]
J =
   6.8543e-01   3.0631e-01            0            0            0
   3.0631e-01   1.5836e-01   4.9306e-01            0            0
            0   4.9306e-01  -2.4896e-02   5.1638e-01            0
            0            0   5.1638e-01  -1.7956e-02   5.0738e-01
            0            0            0   5.0738e-01  -6.5923e-03
ans =
  -8.2685e-01   8.3551e-03
  -2.9103e-01   6.4620e-02
   2.7149e-01   3.2433e-01
   6.9835e-01   8.2708e-01
   9.4239e-01   7.7562e-01

We can verify that the nodes are indeed the roots of the Nth orthogonal polynomial:

norm( xi - roots(P(:,N+1)) )
ans =




[3] G. H. Golub and J. H. Welsch, Calculation of Gauss quadrature rules, Math. Comp. 23 (1969), 221--230.