Numerical Integration with Generic Concepts

Numerical integration (quadrature) for C++20.

Overview

The definite integral represents the signed area under a curve:

Since most functions lack closed-form antiderivatives, we approximate integrals numerically using quadrature rules—weighted sums of function evaluations:

Different rules choose different nodes x_i and weights w_i, trading off accuracy and computational cost.

Quick Start

#include <integration/integrate.hpp>
#include <cmath>
#include <iostream>

int main() {
    using namespace integration;

    // integral from 0 to pi of sin(x) dx = 2
    double result = integrate([](double x) { return std::sin(x); }, 0.0, 3.14159265);

    std::cout << "integral of sin(x) dx = " << result << "\n";  // ~2.0
}

The Quadrature Zoo

Basic Rules

double m = midpoint_rule(f, a, b);
double t = trapezoidal_rule(f, a, b);
double s = simpsons_rule(f, a, b);

Composite Rules

Divide [a,b] into n subintervals and apply the basic rule to each:

// Error: O(h^2) where h = (b-a)/n
double m = composite_midpoint(f, a, b, 100);
double t = composite_trapezoidal(f, a, b, 100);

// Error: O(h^4) - much more accurate!
double s = composite_simpsons(f, a, b, 100);  // n must be even

Gauss-Legendre Quadrature

The optimal choice: n points exactly integrate polynomials of degree 2n-1.

// 5-point Gauss-Legendre: exact for degree <= 9
double g = gauss_legendre<5>(f, a, b);

Adaptive Integration

Automatically refines where the function is “difficult”:

// Recommended for general use
double result = integrate(f, a, b);              // Default tolerance 1e-10
double result = integrate(f, a, b, 1e-12);       // Custom tolerance

// With error estimate
auto [value, error, evals] = integrate_with_error(f, a, b);

Deriving Simpson’s Rule

Taylor expand \(f(x)\) around the midpoint \(m = (a+b)/2\). Odd powers vanish by symmetry when integrating from \(a\) to \(b\):

Simpson’s rule is the unique combination of endpoint and midpoint values that cancels the \(h^2\) error:

Remarkably, this also cancels the h^3 term—Simpson gets a “bonus degree.”

Why Gauss-Legendre is Optimal

For \(n\) evaluation points, we have \(2n\) free parameters (\(n\) nodes + \(n\) weights). We can match \(2n\) conditions: exact integration of \(1, x, x^2, \ldots, x^{2n-1}\).

The solution: nodes are roots of the \(n\)-th Legendre polynomial \(P_n(x)\). These orthogonal polynomials arise naturally from the optimization.

Generic Programming: The Stepanov Way

This module follows the Stepanov principle: algorithms arise from minimal operations.

The Problem with std::floating_point

A naive implementation constrains all functions to std::floating_point:

// BAD: Excludes custom numeric types
template<std::floating_point T, Integrand<T> F>
T midpoint_rule(F&& f, T a, T b);

This rejects perfectly valid types:

• rational<int> - exact fractions
• dual<double> - automatic differentiation
• interval<double> - interval arithmetic
• User-defined real number types

The Stepanov Solution

Ask: “What operations does this algorithm ACTUALLY need?”

For numerical integration:

• Field operations: +, -, *, /, unary -
• Ordering: < (for adaptive methods)
• Small integer construction: T(0), T(1), T(2) (for weights)

We capture this as the ordered_field concept:

template<typename T>
concept ordered_field =
    has_add<T> && has_sub<T> && has_mul<T> && has_div<T> &&
    has_neg<T> && has_order<T> &&
    requires { T(0); T(1); T(2); };

// GOOD: Works with ANY ordered field
template<typename T, Integrand<T> F>
    requires ordered_field<T>
T midpoint_rule(F&& f, T a, T b);

Powerful Application: Differentiation Under the Integral

With dual numbers, you can compute derivatives of integrals automatically:

#include <integration/integrate.hpp>
#include <dual/dual.hpp>

// Compute d/da integral from 0 to 1 of a*x^2 dx
using D = dual::dual<double>;

D a = D::variable(1.0);  // Parameter to differentiate
auto f = [&a](D x) { return a * x * x; };

D result = composite_simpsons(f, D::constant(0.0), D::constant(1.0), 100);

// result.value() = 1/3 (the integral)
// result.derivative() = 1/3 (d/da of the integral = integral of x^2 dx)

This is Leibniz’s rule implemented through pure algebra!

Choosing a Method