Calculus/Integration techniques/Numerical Approximations

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Integration techniques/Numerical Approximations

It is often the case, when evaluating definite integrals, that an antiderivative for the integrand cannot be found, or is extremely difficult to find. In some instances, a numerical approximation to the value of the definite value will suffice. The following techniques can be used, and are listed in rough order of ascending complexity.

Contents

[edit] Riemann Sum

This comes from the definition of an integral. If we pick n to be finite, then we have:

\int_a^b f(x)\ dx\approx \sum_{i=1}^nf(x_i^*)\Delta x

where x_i^* is any point in the i-th sub-interval [xi − 1,xi] on [a,b].

[edit] Right Rectangle

A special case of the Riemann sum, where we let x_i^*=x_i, in other words the point on the far right-side of each sub-interval on, [a,b]. Again if we pick n to be finite, then we have:

\int_a^b f(x)\ dx\approx \sum_{i=1}^nf(x_i)\Delta x

[edit] Left Rectangle

Another special case of the Riemann sum, this time we let x_i^*=x_{i-1}, which is the the point on the far left side of each sub-interval on [a,b]. As always, this is an approximation when n is finite. Thus, we have:

\int_a^b f(x)\ dx\approx \sum_{i=1}^nf(x_{i-1})\Delta x

[edit] Trapezoidal Rule

\int_a^b f(x)\ dx \approx \frac{b-a}{2n}\left[f(x_0)+2\sum_{i=1}^{n-1}(f(x_i))+f(x_n)\right]=\frac{b-a}{2n}(f(x_0) + 2f(x_1) + 2f(x_2) +\cdots+ 2f(x_{n-1}) + f(x_n))

[edit] Simpson's Rule

Remember, n must be even,

\int_a^b f(x)\ dx \approx \frac{b-a}{3n}\left[f(x_0)+\sum_{i=1}^{n-1}\left((3-(-1)^{i})f(x_i)\right)+f(x_n)\right]
=\frac{b-a}{3n}\left[f(x_0)+4f(x_1)+2f(x_2)+4f(x_3)+\cdots+4f(x_{n-1})+f(x_n)\right]

[edit] Further reading

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