Gauss–Jacobi quadrature

From HandWiki

In numerical analysis, Gauss–Jacobi quadrature (named after Carl Friedrich Gauss and Carl Gustav Jacob Jacobi) is a method of numerical quadrature based on Gaussian quadrature. Gauss–Jacobi quadrature can be used to approximate integrals of the form

∫−11f(x)(1−x)α(1+x)βdx

where ƒ is a smooth function on [−1, 1] and α, β > −1. The interval [−1, 1] can be replaced by any other interval by a linear transformation. Thus, Gauss–Jacobi quadrature can be used to approximate integrals with singularities at the end points. Gauss–Legendre quadrature is a special case of Gauss–Jacobi quadrature with α = β = 0. Similarly, the Chebyshev–Gauss quadrature of the first (second) kind arises when one takes α = β = −0.5 (+0.5). More generally, the special case α = β turns Jacobi polynomials into Gegenbauer polynomials, in which case the technique is sometimes called Gauss–Gegenbauer quadrature.

Gauss–Jacobi quadrature uses ω(x) = (1 − x)α (1 + x)β as the weight function. The corresponding sequence of orthogonal polynomials consist of Jacobi polynomials. Thus, the Gauss–Jacobi quadrature rule on n points has the form

∫−11f(x)(1−x)α(1+x)βdx≈λ1f(x1)+λ2f(x2)+…+λnf(xn),

where x1, …, xn are the roots of the Jacobi polynomial of degree n. The weights λ1, …, λn are given by the formula

λi=−2n+α+β+2n+α+β+1Γ(n+α+1)Γ(n+β+1)Γ(n+α+β+1)(n+1)!2α+βPn(α,β)′(xi)Pn+1(α,β)(xi),

where Γ denotes the Gamma function and P(α, β)n(x) the Jacobi polynomial of degree n.

The error term (difference between approximate and accurate value) is:

En=Γ(n+α+1)Γ(n+β+1)Γ(n+α+β+1)(2n+α+β+1)[Γ(2n+α+β+1)]222+α+β+1(2n)!f(2n)(ξ),

where −1<ξ<1.

References

  • Jacobi rule - free software (Matlab, C++, and Fortran) to evaluate integrals by Gauss–Jacobi quadrature rules.
  • Gegenbauer rule - free software (Matlab, C++, and Fortran) for Gauss–Gegenbauer quadrature