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Numerical integration

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Numerical integration is a collection of algorithms used to approximate the value of a definite integral. In mathematical analysis, a definite integral represents the area under a curve defined by a function. While mathematicians often seek exact solutions through analytical integration, they frequently rely on numerical methods instead. This process is often called numerical quadrature, especially when dealing with one-dimensional integrals. When applying these methods to more than one dimension, the term cubature is sometimes used. The primary goal is to compute an approximate solution to a specific degree of accuracy.

There are several practical reasons to use numerical integration rather than finding an antiderivative. Sometimes, the integrand is only known at specific points, such as data collected through sampling. This is common in embedded systems and various computer applications. In other cases, a formula for the integrand exists, but finding an elementary antiderivative is impossible. For example, the function e^(-x^2) cannot be written in elementary form. It may also be easier to compute a numerical approximation if the antiderivative is expressed as an infinite series or product. In these situations, numerical methods provide a much faster path to a useful answer.

Most numerical integration methods work by evaluating the integrand at a set of specific points. These are known as integration points. The method then calculates a weighted sum of these values to reach an approximation. The specific points and their corresponding weights depend on the chosen quadrature rule. A key part of analyzing these methods is studying the approximation error. This error is measured against the number of integrand evaluations used. A superior method is one that provides a very small error using only a few evaluations. Reducing evaluations saves time and reduces the total number of arithmetic operations required.

One category of rules uses step functions to approximate the area. The simplest version is the rectangle rule, or midpoint rule. This method approximates the function as a piecewise constant function.

Integration rectangle
Integration rectangle

Another common approach uses interpolating functions, which are usually polynomials. The trapezoidal rule uses a linear function, or a polynomial of degree 1, to connect points.

Integration trapezoid
Integration trapezoid

Simpson's rule is more advanced and uses a polynomial of order 2 to follow the curve.

Integration simpson
Integration simpson

These can be turned into composite rules by breaking the interval into many subintervals and adding the results together. This is often called an iterated or extended rule.

The history of this field is rooted in the geometric problem of quadrature. In Ancient Greece, mathematicians sought to construct a square with the same area as a given plane figure. This was often attempted using only a compass and a straightedge.

Geometric mean
Geometric mean

The ancient Babylonians used a trapezoidal rule to integrate the motion of Jupiter along the ecliptic. Archimedes achieved great success by studying the area of a parabola segment. He discovered that the area of a parabola segment is 4/3 the area of its inscribed triangle.

Parabola and inscribed triangle
Parabola and inscribed triangle

During the medieval period, the method of indivisibles became a popular way to calculate area. Although it was less rigorous, it was highly effective. Galileo Galilei and Gilles de Roberval used it to find the area of a cycloid arch. In 1647, Grégoire de Saint-Vincent investigated the area under a hyperbola. His work, along with that of Alphonse Antonio de Sarasa, helped define the natural logarithm. In 1656, John Wallis published Arithmetica Infinitorum, where he used series to represent what we now call the definite integral. Later, mathematicians like Isaac Barrow and James Gregory made progress with algebraic curves and spirals.

Modern numerical integration also addresses much more complex scenarios. For integrals over infinite intervals, mathematicians use specialized rules like Gauss-Hermite quadrature for the whole real line. Gauss-Laguerre quadrature is used for integrals over the positive reals. Another approach is the Monte Carlo method, which uses randomness to find approximations. Adaptive algorithms can also be used to manage error by changing the step size during calculation. These tools allow us to bridge the gap between theoretical calculus and the practical needs of science and engineering.

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🖼️ Images & Media (6)
File:Integral as region under curve.svg
Integral as region under curve.svg
File:Geometric mean.svg
Geometric mean.svg
File:Parabola and inscribed triangle.svg
Parabola and inscribed triangle.svg
File:Integration rectangle.svg
Integration rectangle.svg
File:Integration trapezoid.svg
Integration trapezoid.svg
File:Integration simpson.svg
Integration simpson.svg
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