Mid-Atlantic Regional ACM Programming Contest 2013, problem F

In the Mid-Atlantic Regional ACM Programming Contest this year, one of the more interesting problems was the following:

Suppose there are $N \leq 10^5$ stars such that star $i$ has coordinates $x_i, y_i, z_i$, where $|x_i|, |y_i|, |z_i| \leq 10^9$. Given $K \leq 10^9$, count the number of pairs of stars which are within distance $K$ from each other. The cluster of stars is sparse -- that is, there are at most $M = 10^5$ pairs of stars within $K$ of each other.

One simple solution is to think about partitioning the space up into cubes of side length $K$. Then, for each star, it suffices to check the number of stars in the same cube and adjacent cubes and see whether they're within $K$. Surprisingly enough, this actually runs in $O(M)$ operations.

First we consider the case where space is one-dimensional, and our cubes are just intervals of length $K$. Let the intervals contain $a_1, a_2, \ldots, a_n$ elements. Note that two stars within the same interval are guaranteed to be within $K$ of each other. Thus,

$$\sum_{i=1}^n a_i^2 \leq M$$ What is the number of operations that this will take? Well, for each star in interval $i$, we examine all stars in intervals $i - 1$, $i$, and $i + 1$. This is just $$\sum_{i=1}^n a_i (a_{i-1} + a_i + a_{i+1})$$ Note that the $a_i^2$ terms are $\leq M$; furthermore, the $a_ia_{i-1}$ terms and the $a_ia_{i+1}$ terms are symmetric. Thus, it suffices to show that $$\sum_{i=1}^n a_i a_{i-1} \leq M$$ Here we apply something known as the rearrangement inequality*, which roughly says that if you have two sequences of numbers $x_i$ and $y_i$, and you want to rearrange the $x_i$ and $y_i$ to maximise $\sum_i x_i y_i$, then it's best to rearrange them in ascending order. In this case, our sequences are $a_i$ and $a_{i-1}$, and the sequences in ascending order is just $\sum_{i=1}^n a_i^2$, which we already showed was $\leq M$. This means that the whole algorithm takes $$\sum_{i=1}^n a_i (a_{i-1} + a_i + a_{i+1}) \leq 3 M$$

The same argument can be extended to cubes -- however, in order to guarantee that $\sum_{i=1}^n a_i^2 \leq M$, we need to make our cubes be of size $K / \sqrt3$ so that all points within them are guaranteed to be at most $K$ from each other. This implies that if we instead use cubes of size $K$, then $\sum_{i=1}^n a_i^2 \leq 3^3 M$. We can then apply the rearrangement argument as before, except with $27$ adjacent cubes instead of $3$. This gives us an algorithm which runs in $729 M$.

In general, for dimension $D$ we'll need $(3D)^DM$ operations for the same algorithm, so in high dimensions we'd likely need to exploit other tricks, like the fact that the volume of an $n$-ball disappears as $n$ increases. We could then (maybe?) approximate the sphere with small cubes of size $\epsilon K$ instead of simply summing over $3^D$ adjacent, larger cubes of size $K$.

* -- You could also prove this using Cauchy-Schwarz or some other famous inequality. Rearrangement just happened to be the first one I thought of.