Srivastava code

In numerical analysis, Gauss–Jacobi quadrature is a method of numerical quadrature based on Gaussian quadrature. Gauss–Jacobi quadrature can be used to approximate integrals of the form

$\underset{-1}{\overset{1}{\int }}f(x)(1-x{)}^{\alpha }(1+x{)}^{\beta }\mathrm{d}x$

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, Chebyshev–Gauss quadrature arises when one takes α = β = ±½. 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

$\underset{-1}{\overset{1}{\int }}f(x)(1-x{)}^{\alpha }(1+x{)}^{\beta }\mathrm{d}x\approx {\lambda }_{1}f(x_1)+{\lambda }_{2}f(x_2)+\cdots +{\lambda }_{n}f(x_n),$

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

${\lambda }_i=-\frac{2n+\alpha +\beta +2}{n+\alpha +\beta +1}\frac{\mathrm{\Gamma }(n+\alpha +1)\mathrm{\Gamma }(n+\beta +1)}{\mathrm{\Gamma }(n+\alpha +\beta +1)(n+1)!}\frac{2^{\alpha +\beta }}{P{\text{'}}_n(x_i)P_{n+1}(x_i)},$

where Γ denotes the Gamma function and P_n the Jacobi polynomial of degree n.

References

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External links

• 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