# ETOOBUSY ðŸš€ minimal blogging for the impatient

# Be Rational

**TL;DR**

Rational numbers are

compactand might prove better for doing a lot of stuff.

When dealing with numbers with computers, we always have to keep in mind that there is so much we can do. By default.

As an example, a fantastic number like $\pi$ cannot be represented easily in any base, neither base-10 nor base-2 or any power of 2. Considering that numbers are represented by finite strings of bits (or decimal digits on a piece of paper)â€¦ you can only get so much.

As an example, most people I know usually truncate $\pi$ to $3.14$. A few more, I think, go to $3.14159$, me included.

On the other hand, if you have to remember 6 decimal digits overall, itâ€™s probably better to remember $\frac{355}{113}$, because it gets you one digit more (with the rounding, both $\pi$ and that fraction yield $3.141593$).

The fun thing with rational numbers is that they are *compact*. Even if
a *lot* of numbers are not rational, they have this interesting
property that you can get as close as you finitely want to any of them
with a rational representation.

Not sure about it? Letâ€™s see an example with $\pi$ itself.

We start with two fractions that are below and above $\pi$:

\[B_1 = \frac{3}{1} \\ A_1 = \frac{4}{1} \\ \epsilon_1 < \frac{A_1 - B_1}{2} = \frac{1}{2}\]The first one is closer to $\pi$ so this will be our starting
approximation. The error we are doing by choosing these two values is
less than $\frac{1}{2}$, i.e. the distance between our starting
fractions. (It cannot be *equal* to $\frac{1}{2}$ because $\pi$ is
irrational!).

Now letâ€™s consider the mid-point between our starting fractions:

\[M_1 = \frac{A_1 + B_1}{2} > \pi\]Itâ€™s still a rational number, and as it comes itâ€™s better than $A_0$ because itâ€™s closer to $\pi$. So we can build a second iteration like this:

\[B_2 = \frac{3}{1} \\ A_2 = \frac{7}{2} \\ \epsilon_2 = \frac{1}{4} \\ M_2 = \frac{13}{4} > \pi\]Our initial rational approximation still wins, but now we know that the error we are doing must be less than $\epsilon_2 = 0.25$. As before, we can use $M_2$ to get a better upper rational bound and get to the second iteration:

\[B_3 = \frac{3}{1} \\ A_3 = \frac{13}{4} \\ \epsilon_3 = \frac{1}{8} \\ M_3 = \frac{25}{8} < \pi\]Now we have two considerations:

- the upper bound for the error is always halving, because we are always cutting an interval in half
- this new value $M_3$ is lower than $\pi$, so we will update the lower rational bound instead of the upper one.

At this point, the algorithm is simple:

- at iteration $k$, calculate

- if $M_k < \pi$, then the boundary values for iteration $k + 1$ will be:

- otherwise:

This is it. If you fix the maximum finite error $\epsilon$ that you are willing to accept, you can then iterate until $\epsilon_k$ is lower than, or equal to this value, and youâ€™re done.

Nowâ€¦ Iâ€™m not saying that these values will be somehow *optimal* in
terms of how compact the representation will be in terms of digits, just
that you can get as close as you finitely want.

Cheers!

*Comments? Octodon, , GitHub, Reddit, or drop me a line!*