When performing pairwise comparison over 2 sequences, the canonical code (in Java) looks something like:

```
public static <T> List<Pair<T>> pairwise(List<T> list) {
if (list == null) {
return null;
}
int n = list.size();
List<Pair<T>> res = new ArrayList<>();
for (int i = 0; i < n - 1; ++i) {
for (int j = i + 1; j < n; ++j) {
res.add(new Pair<T>(list.get(i), list.get(j)));
}
}
return res;
}
var input = List.of(a);
var output = [];
var input = List.of(a, b);
var output = [(a, b)];
var input = List.of(a, b, c);
var output = [(a, b), (a, c), (b, c)];
var input = List.of(a, b, c, d);
var output = [(a, b), (a, c), (a, d), (b, c), (b, d), (c, d)];
var input = List.of(a, b, c, d, e);
var output = [(a, b), (a, c), (a, d), (a, e), (b, c), (b, d), (b, e), (c, d), (c, e), (d, e)];
```

When considering the algorithm’s complexity
it’s easy to skim the code, see 2 `for`

loops, declare it quadratic, and call it a day. But if
we want a more rigorous proof, we can dissect its components.

The outer loop clearly runs in linear time (\(O(n)\)). But what about the inner loops? Each begins at the \(ith\) element and ends at the last, so it’s upper bound must be \(n\). But does that mean the overall pairwise comparison is sub-quadratic?

Again, it becomes easier to see when we start unrolling the loop iterations:

\[n + (n - 1) + (n - 2) + \cdots + 1\]Rewriting from right-to-left in Sigma notation, the formula becomes:

\[\sum_{k=1}^{n} k = 1 + 2 + 3 + \cdots + n\]Which is more easily recognizable as the sequence summing the first \(n\) integers:

\[\sum_{k=1}^{n} k = 1 + 2 + 3 + \cdots + n = \frac{n(n + 1)}{2}\]The solution to this sequence is most famously attributed to Gauss when in grade school. And in this notation it’s much easier to see that the upper bound is indeed \(O(n^2)\).

A visual interpretation can be done using triangular numbers, where the triangle can be pictured as a “half-square” of objects added together with a copy of itself and rotated to form a rectangle with dimensions \(n(n + 1)\).

Lastly, thinking of this combinatorically, the formula can be written as a binomial coefficient:

\[{n + 1 \choose 2} = \frac{n(n + 1)}{2} = \sum_{k=1}^{n} k = 1 + 2 + 3 + \cdots + n\]