Skip to main content
`Ctrl`+`K`
scipy.special.

# roots_hermitenorm

scipy.special.roots_hermitenorm(n, mu=False)

  Gauss-Hermite (statistician’s) quadrature.

  Compute the sample points and weights for Gauss-Hermite quadrature. The sample points are the roots of the nth degree Hermite polynomial, \(He_n(x)\). These sample points and weights correctly integrate polynomials of degree \(2n - 1\) or less over the interval \([-\infty, \infty]\) with weight function \(w(x) = e^{-x^2/2}\). See 22.2.15 in [AS] for more details.

  Parameters:
    n int

      quadrature order.

    mu bool, optional

      If True, return the sum of the weights, optional.

  Returns:
    x ndarray

      Sample points.

    w ndarray

      Weights.

    mu float

      Sum of the weights.

  See also

  `scipy.integrate.fixed_quad`
  `numpy.polynomial.hermite_e.hermegauss`

  Notes

  For small n up to 150 a modified version of the Golub-Welsch algorithm is used. Nodes are computed from the eigenvalue problem and improved by one step of a Newton iteration. The weights are computed from the well-known analytical formula.

  For n larger than 150 an optimal asymptotic algorithm is used which computes nodes and weights in a numerical stable manner. The algorithm has linear runtime making computation for very large n (several thousand or more) feasible.

  References

  [AS]

  Milton Abramowitz and Irene A. Stegun, eds. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. New York: Dover, 1972.