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**ten years**of experience with

**SaaS projects**. If that’s something you need help with, we should get in touch!

I found a pretty strange piece of code in my code directory recently. The file was a few months old. It was short (around 15 loc). Even though it was called `compute.py`

, I had no idea what it was meant to compute:

```
from math import sqrt
a_n = 1.0
b_n = 1.0/sqrt(2.0)
t_n = 1.0/4.0
p_n = 1.0
for i in range(4):
a_n1 = (a_n+b_n)/2.0
b_n1 = sqrt(a_n*b_n)
t_n1 = t_n - p_n * ((a_n - a_n1) * (a_n - a_n1))
p_n1 = 2*p_n
print((a_n1 + b_n1)**2 / (4*t_n1))
a_n, b_n, t_n, p_n = a_n1, b_n1, t_n1, p_n1
```

So this is some math stuff, but what was I thinking about when I wrote that ? It was easy to see that there was no malware in this, so I ran it.

```
$ python compute.py
3.1405792505221686
3.141592646213543
3.141592653589794
3.141592653589794
```

The memory came back. A few months ago, I was interested in finding decimals of pi, so… I wrote a program to do so. That’s where this piece of code is **awesome**.

It’s a very short program. It expected computing pi to be a complicated task, then I found the Gauss-Legendre algorithm. It’s trivial to implement. As you can see, 15 loc are enough and it could be golfed a bit more.

It’s also beautiful that, despite its simplicity, the algorithm has **quadratic convergence** : the number of correct digits doubles with each iteration of the algorithm. That’s way better than Monte-Carlo’s algorithm I spoke about earlier, where you need 100 times more iterations to get one more digits.

The core idea is that we will compute the **arithmetic–geometric mean of two numbers cleverly chosen and a relationship between all the values will let us approximate pi**. The Wikipedia page has more details on the derivation of this result.

As for us, with specific initial values for a0, b0, t0 and p0:

we can compute the next values of the sequence:

and out of these values, with n large enough, we can compute an approximation of pi:

Due to the quadratic convergence, n = 4 is already enough to have a good enough approximation.