Synopsis

#include <boost/math/quadrature/gauss.hpp>

namespace boost{ namespace math{ namespace quadrature{

template <class Real, unsigned Points, class Policy = boost::math::policies::policy<> >
struct gauss
{
   static const RandomAccessContainer& abscissa();
   static const RandomAccessContainer& weights();

   template <class F>
   static auto integrate(F f, Real* pL1 = nullptr)->decltype(std::declval<F>()(std::declval<Real>()))

   template <class F>
   static auto integrate(F f, Real a, Real b, Real* pL1 = nullptr)->decltype(std::declval<F>()(std::declval<Real>()))
};

}}} // namespaces

description

The gauss class template performs "one shot" non-adaptive Gauss-Legendre integration on some arbitrary function f using the number of evaluation points as specified by Points.

This is intentionally a very simple quadrature routine, it obtains no estimate of the error, and is not adaptive, but is very efficient in simple cases that involve integrating smooth "bell like" functions and functions with rapidly convergent power series.

static const RandomAccessContainer& abscissa();
static const RandomAccessContainer& weights();

These functions provide direct access to the abscissa and weights used to perform the quadrature: the return type depends on the Points template parameter, but is always a RandomAccessContainer type. Note that only positive (or zero) abscissa and weights are stored.

template <class F>
static auto integrate(F f, Real* pL1 = nullptr)->decltype(std::declval<F>()(std::declval<Real>()))

Integrates f over (-1,1), and optionally sets *pL1 to the L1 norm of the returned value: if this is substantially larger than the return value, then the sum was ill-conditioned. Note however, that no error estimate is available.

template <class F>
static auto integrate(F f, Real a, Real b, Real* pL1 = nullptr)->decltype(std::declval<F>()(std::declval<Real>()))

Integrates f over (a,b), and optionally sets *pL1 to the L1 norm of the returned value: if this is substantially larger than the return value, then the sum was ill-conditioned. Note however, that no error estimate is available. This function supports both finite and infinite a and b, as long as a < b.

The Gaussian quadrature routine support both real and complex-valued quadrature. For example, the Lambert-W function admits the integral representation

W(z) = 1/2Π ∫_-Π^Π ((1- v cot(v) )^2 + v^2)/(z + v csc(v) exp(-v cot(v))) dv

so it can be effectively computed via Gaussian quadrature using the following code:

Complex z{2, 3};
auto lw = [&z](Real v)->Complex {
  using std::cos;
  using std::sin;
  using std::exp;
  Real sinv = sin(v);
  Real cosv = cos(v);

  Real cotv = cosv/sinv;
  Real cscv = 1/sinv;
  Real t = (1-v*cotv)*(1-v*cotv) + v*v;
  Real x = v*cscv*exp(-v*cotv);
  Complex den = z + x;
  Complex num = t*(z/pi<Real>());
  Complex res = num/den;
  return res;
};

boost::math::quadrature::gauss<Real, 30> integrator;
Complex W = integrator.integrate(lw, (Real) 0, pi<Real>());

Choosing the number of points

Internally class gauss has pre-computed tables of abscissa and weights for 7, 15, 20, 25 and 30 points at up to 100-decimal digit precision. That means that using for example, gauss<double, 30>::integrate incurs absolutely zero set-up overhead from computing the abscissa/weight pairs. When using multiprecision types with less than 100 digits of precision, then there is a small initial one time cost, while the abscissa/weight pairs are constructed from strings.

However, for types with higher precision, or numbers of points other than those given above, the abscissa/weight pairs are computed when first needed and then cached for future use, which does incur a noticeable overhead. If this is likely to be an issue, then

Examples

We'll begin by integrating t² atan(t) over (0,1) using a 7 term Gauss-Legendre rule, and begin by defining the function to integrate as a C++ lambda expression:

using namespace boost::math::quadrature;

auto f = [](const double& t) { return t * t * std::atan(t); };

Integration is simply a matter of calling the gauss<double, 7>::integrate method:

double Q = gauss<double, 7>::integrate(f, 0, 1);

Which yields a value 0.2106572512 accurate to 1e-10.

For more accurate evaluations, we'll move to a multiprecision type and use a 20-point integration scheme:

using boost::multiprecision::cpp_bin_float_quad;

auto f2 = [](const cpp_bin_float_quad& t) { return t * t * atan(t); };

cpp_bin_float_quad Q2 = gauss<cpp_bin_float_quad, 20>::integrate(f2, 0, 1);

Which yields 0.2106572512258069881080923020669, which is accurate to 5e-28.