IMA Journal of Numerical Analysis · 2015 · 39 citations · 16 references
ComputingComputer ScienceMathematicsAlgorithmsComputational GeometryScientific ComputingMatrix ComputationNumerical AlgorithmsNumerical AnalysisNumerical IntegrationNumerical Linear AlgebraGauss QuadratureGauss-chebyshev QuadratureGauss-legendre QuadratureQuadrature RulesQuadrature Theory
A fast and accurate algorithm for the computation of Gauss–Hermite and generalized Gauss–Hermite quadrature nodes and weights is presented. The algorithm is based on Newton's method with carefully selected initial guesses for the nodes and a fast evaluation scheme for the associated orthogonal polynomial. In the Gauss–Hermite case, the initial guesses and evaluation scheme rely on explicit asymptotic formulas. For generalized Gauss–Hermite, the initial guesses are furnished by sampling a certain equilibrium measure and the associated polynomial evaluated via a Riemann–Hilbert reformulation. In both cases, the |$n$| -point quadrature rule is computed in |${\mathcal O}(n)$| operations to an accuracy that is close to machine precision. For sufficiently large |$n$| , some of the quadrature weights have a value less than the smallest positive normalized floating-point number in double precision and we exploit this fact to achieve a complexity as low as |${\mathcal O}(n^{1/2})$| .
16
Numerical recipes in C: the art of scientific computing
Choice Reviews Online · 1993 · 18.1K citations · Full text
Calculation of Gauss quadrature rules
Gene H. Golub, John H. Welsch · Mathematics of Computation · 1969 · 1.6K citations
Barycentric Lagrange Interpolation
Jean‐Paul Berrut, Lloyd N. Trefethen · SIAM Review · 2004 · 1.2K citations