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Simpson's rule

In numerical analysis, Simpson's rule (named after Thomas Simpson) is a way to get an approximation of an integral:

\int_{a}^{b} f(x) dx

by approximating f(x) by the quadratic polynomial P(x) which takes the same values as f(x) at a, b, and the midpoint m=(a+b)/2. One can use Lagrange polynomial interpolation to find an expression for this polynomial,

P(x)=f(a)\frac{(x-m)(x-b)}{(a-m)(a-b)}+ f(m)\frac{(x-a)(x-b)}{(m-a)(m-b)}+ f(b)\frac{(x-a)(x-m)}{(b-a)(b-m)}.

Simpson's rule then follows by an easy (albeit tedious) calculation:

\int_{a}^{b} f(x) dx\approx \int_{a}^{b} P(x) dx =\frac{b-a}{6}\left[f(a) + 4f\left(\frac{a+b}{2}\right)+f(b)\right].


The error in approximating an integral by Simpson's rule is

-\frac{h^5}{90}f^{(4)}(\xi),

with h = (b - a) / 2 and ξ some number between a and b.

We see that Simpson's rule provides an adequate approximation if the interval of integration [a,b] is small, which does not happen most of the time. The obvious solution is to split the interval of integration in small subintervals, apply Simpson's rule on each subinterval, and add up the results. In this way one obtains the composite Simpson's rule

\int_a^b f(x) dx\approx  \frac{h}{3}\bigg[f(x_0)+2\sum_{j=1}^{n/2-1}f(x_{2j})+ 4\sum_{j=1}^{n/2}f(x_{2j-1})+f(x_n) \bigg],

where n is the number of subintervals in which one splits [a,b] with n an even number, h = (b - a) / n is the length of each subinterval, and xi = a + ih for i=0, 1, \dots, n-1, n, in particular, x0 = a and xn = b.

See also: Newton-Cotes formulas.

References

  • Richard L. Burden, J. Douglas Faires (2000), "Numerical Analysis, (7th Ed)", Brooks/Cole. ISBN 0534382169
Last updated: 05-22-2005 00:33:49
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