The Newton-Cotes formulas reference article from the English Wikipedia on 24-Apr-2004
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Newton-Cotes formulas

In numerical analysis, Newton-Cotes formulas are a group of formulas for numerical integration (also called quadrature). These formulas are named after Isaac Newton and Roger Cotes. These formulas are also called Newton-Cotes rules.

It is assumed that the value of a function f is known at equally spaced points xi, for i = 0, ..., n. (If the evaluation points are not assumed to be equally spaced, another class of formulas, called Gaussian quadrature formulas, can be derived.) There are two types of Newton-Cotes formulas, the "closed" type which uses the function value at all points, and the "open" type which does not use the function values at the end points. The closed Newton-Cotes formula of degree n is stated as

where xi = h i + x0, with h (called the step size) equal to (xn - x0)/n. The weights, , are derived from Lagrange polynomials as follows.

One can see that the depend only on the , and not on the function f.

The open Newton-Cotes formula of degree n is stated as

The weights are found in a manner similar to the closed formula.

A Newton-Cotes formula of any degree can be constructed. Some of the formulas of low degree are known by conventional names. This table lists some of the Newton-Cotes formulas of the closed type.

Degree Common name Formula Error term
1 Trapezoid rule
2 Simpson's rule
3 3/8 rule
4 Bode's rule

The exponent of the step size h in the error term shows the rate at which the approximation error decreases. The derivative of f in the error term shows which polynomials can be integrated exactly (i.e., with error equal to zero). Note that the derivative of f in the error term increases by 2 for every other rule.

This table lists some of the Newton-Cotes formulas of the open type.

Degree Common name Formula Error term
0 Rectangle rule
1
2
3

It is interesting to note that the rectangle rule is exact for the same class of functions as the trapezoidal rule. What's more, the error term of the trapezoidal rule is twice as great as that of the rectangle rule. However, the trapezoidal rule is important as a building block in the construction of extrapolation methods.


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