Suppose you take the arithmetic mean and the geometric mean of the first *n* integers. The ratio of these two means converges to *e*/2 as *n* grows [1]. In symbols,

Now suppose we wanted to visualize the convergence by plotting the expression on the left side for a sequence of *n*s.

First let’s let *n* run from 1 to 100.

This isn’t very convincing. Maybe the convergence is just slow, or maybe it’s not actually converging to *e*/2. Let’s make another plot including larger values of *n*. This will require a little additional work.

## Avoiding overflow

Here’s the code that made the plot above.

import matplotlib.pyplot as plt import numpy as np from scipy.special import factorial AM = lambda n: (n+1)/2 GM = lambda n: factorial(n)**(1/n) n = np.arange(1, 100) plt.plot(n, AM(n)/GM(n)) plt.plot(n, 0*n + np.exp(1)/2, 'r--')

This works for *n* up to 170, but it fails when *n* = 171 because at that point factorial overflows.

This is a very common situation. We ultimately want to compute a fairly small number, but we encounter extremely large numbers in an intermediate step.

We need to avoid directly calculating *n*! before taking the *n*th root. The way to do this is to work with logarithms. Now *n*! = Γ(*n*+1) and SciPy has a function for computing the log of the gamma function directly without first computing the gamma function, thus avoiding overflow.

We replace `GM`

above with `GM2`

below:

GM2 = lambda n: np.exp(gammaln(n+1)/n)

Now we compute the geometric mean of the first *n* natural numbers for very large values of *n*. We only need *n* = 1000 to see convergence, but the code could handle much larger values of *n* without overflowing.

## Related posts

- Arithmetic-harmonic mean
- Avoiding underflow in Bayesian calculations
- Computing soft max without overflow

[1] Richard P. Kubelka. Means to an End. Mathematics Magazine, Vol. 74, No. 2 (April 2001), pp. 141–142