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Gauss–Legendre quadrature is optimal in a very narrow sense for computing integrals of a function f over [−1, 1], since no other quadrature rule integrates all degree 2n − 1 polynomials exactly when using n sample points. However, this measure of accuracy is not generally a very useful one---polynomials are very simple to integrate and ...
Gauss–Legendre methods are implicit Runge–Kutta methods. More specifically, they are collocation methods based on the points of Gauss–Legendre quadrature. The Gauss–Legendre method based on s points has order 2s. [1] All Gauss–Legendre methods are A-stable. [2] The Gauss–Legendre method of order two is the implicit midpoint rule.
This exact rule is known as the Gauss–Legendre quadrature rule. The quadrature rule will only be an accurate approximation to the integral above if f (x) is well-approximated by a polynomial of degree 2n − 1 or less on [−1, 1]. The Gauss–Legendre quadrature rule is not typically used for integrable functions with endpoint singularities ...
The Gauss–Legendre methods use the points of Gauss–Legendre quadrature as collocation points. The Gauss–Legendre method based on s points has order 2s. [2] All Gauss–Legendre methods are A-stable. [3] In fact, one can show that the order of a collocation method corresponds to the order of the quadrature rule that one would get using the ...
These are named after Rehuel Lobatto [7] as a reference to the Lobatto quadrature rule, but were introduced by Byron L. Ehle in his thesis. [8] All are implicit methods, have order 2 s − 2 and they all have c 1 = 0 and c s = 1.
The term numerical quadrature (often abbreviated to quadrature) is more or less a synonym for "numerical integration", especially as applied to one-dimensional integrals. Some authors refer to numerical integration over more than one dimension as cubature ; [ 1 ] others take "quadrature" to include higher-dimensional integration.
These zeros play an important role in numerical integration based on Gaussian quadrature. The specific quadrature based on the P n {\displaystyle P_{n}} 's is known as Gauss-Legendre quadrature . From this property and the facts that P n ( ± 1 ) ≠ 0 {\displaystyle P_{n}(\pm 1)\neq 0} , it follows that P n ( x ) {\displaystyle P_{n}(x)} has n ...
An enhancement to the Chebyshev pseudospectral method that uses a Clenshaw–Curtis quadrature was developed. [18] The LPM uses Lagrange polynomials for the approximations, and Legendre–Gauss–Lobatto (LGL) points for the orthogonal collocation. A costate estimation procedure for the Legendre pseudospectral method was also developed. [19]