The previous post looked at **Euler’s method** for accelerating the convergence of a slowly converging alternating series. Both hypotheses are necessary. The signs must alternate between terms, and applying the method to a series that is already converging quickly can slow down convergence.

This post looks at **Aitken’s method** for speeding up the convergence of a series. It does not matter whether the series is an alternating series or not. Aitken’s method works best when the series is converging approximately like a geometric series, which is often true for power series of analytic functions.

Aitken acceleration estimates the sum of a series by taking the last three partial sums and extrapolating to where the partial sums appear to be going. That is, it estimates the sum as

We’ll use the exponential function as our example, estimating exp(0.3) first by using six terms of the Taylor series for exp(*x*) and then by applying Aitken’s method.

Here’s a little Python code to carry out our example.

from math import exp, factorial from numpy import cumsum def aitken(s0, s1, s2): return s2 - (s2 - s1)**2/(s2 - 2*s1 + s0) def exp_partial_sums(x, N): terms = [x**n/factorial(n) for n in range(N)] return cumsum(terms) x, N = 0.3, 6 partial = exp_partial_sums(x, N) # estimate taking the last partial sum p = partial[-1] # estimate applying Aitken acceleration a = aitken( partial[-3], partial[-2], partial[-1] ) # error analysis error_p = exp(x) - p error_a = exp(x) - a print(error_p, error_a, error_p/error_a)

This says the error simply using partial sums is 1.0576e-06. The error using Aitken acceleration is -2.3498e-07, which is 4.5 times smaller.

If we go back to the example of the Struve function in the previous post, the approximation error using Aitken acceleration is about 3 times smaller than simply using partial sums. So Aikten acceleration would have been more appropriate than Euler acceleration.

For more content like this post, see Computational math.