# Three solutions to the Putnam's 2006 statistical problem

Putnam’s 2006 exam included the following statistical problem

Let \(S={1,2,…,n}\) for some integer \(n>1\). Say a permutation \(π\) of \(S\) has alocal maximum at \(k∈S\) if

- \(π(k)>π(k+1)\) for \(k=1\);
- \(π(k−1)<π(k)\) and \(π(k)>π(k+1)\) for \(1<k<n\);
- \(π(k−1)<π(k)\) for \(k=n\).
(For example, if \(n=5\) and \(π\) takes values at 1,2,3,4,5 of 2,1,4,5,3, then \(π\) has a local maximum of 2 at \(k=1\), and a local maximum of 5 at \(k=4.\)) What is the average number of local maxima of a permutation of \(S\), averaging over all permutations of \(S\)?

## Intuitive solution

The problem gets simplified significantly when we realize that each permutation is equally likely. The last statement asks to produce expectation as the “averaging over all permutations of \(𝑆\)” which means that each permutation will be used only once in the average which is the same as saying that each permutation is equally likely.

The problem can be simplified by focusing our attention on short subsequences within the sequence. Initially, we can look at the positions \(i = 2, 3, \ldots, n - 1 \, \forall \, n \ge 3 \) and observe number \(a_i\) along with its neighbours, namely \(a_{i-1}\) and \(a_{i+1}\). We know that these numbers are all different by definition. And because each permutation is equally likely the largest among these number will appear one third of the time in the middle which will produce local maximum and the other two thirds of the time it will be at \(a_{i-1}\) or \(a_{i+1}\) positions. This observation will be true for any position in the sequence except the first and last most places. In left and right most positions we always have only one neighbour and it’s always greater or smaller compared to the observed position. This means that there is a 50% chance of getting a local maximum and 50% change of getting a local minimum. Since we know the probability of getting a local maximum per each position we can combine this information and calculate the average number of local maximums per sequence.

\(E = (n - 2) \cdot \frac{1}{3} + 2 \cdot \frac{1}{2} = \frac{n+1}{3}\)

We can also see that the average number of local maximas is also equal to the average number of local minimums, since we can easily replace each number \(a_i\) with the number \(n - a_i\). This trick will allow us to replace each local maxima with local minima. It’s an interesting observation, since it indicates that there is an equal amount of local maximas and minimas, but there are slightly less non extremum points on average. It happens because of the bias introduced by the left most and right most points. These points cannot be non- extremum points, so they have to be either local minimas or maximas.

Basically, in the sequence there are \(\frac{n+1}{3}\) local maximas, \(\frac{n+1}{3}\) local minimas and \(\frac{n-2}{3}\) non extremum points.

## Improved intuitive solution

Solution above is rather simple, but it’s not very pretty since in each sequence there are always two exceptions that have a slightly different behaviour by definition since they cannot be extremums.

The problem could be redefined in much nicer way. We can modify each sequence by
adding 0 to the beginning and to the end of it. For example, sequence ```
4, 2, 1,
3
```

will be converted to `0, 4, 2, 1, 3, 0`

. Note that this trick increased
number of digits in the sequence, but it didn’t change number of local maximas
in it. Zero cannot force it’s neighbor to change from local maximum to non-
maximum since it’s always smaller than its neighbour. It also cannot create
local maximum from non-maximum since if the number wasn’t a local maximum before
then it means that it’s other neighbor is larger and therefore the value cannot
become local maximum.

Next, instead of looking at the linear sequence we can write these numbers along the circumference of the circle such that the order of the sequences is preserved. And on the circle we will write one zero on top of the other so that there will remain only one zero. This will allow us to view each sequence as a circular sequence. Notice that this trick didn’t change the number of local maximas; each number has exactly the same neighbours as before.

Each possible sequence could be converted to a circular sequence by adding extra zero and it’s easy to show that these sequences are unique, meaning that there is always one circular sequence which could be converted back to the unique sequence from the original set.

Now imagine the following process, we randomly sample a circular sequence, write it on the wheel with a pointer on it and rotate it. When it stops spinning we can see at which number it points. We can look at the number and its neighbours in order to decide whether it’s larger than both of its neighbors or not. By doing this procedure we will observe local maximums one third of the time by using the same logic as in the previous Solution #1. And finally, we can multiply the probability of getting a local maximum by playing the game by the number of digits on the wheel, which is equal to \(n+1\) (plus one for the additional 0 in the sequence). Multiplication will give us the same answer which is \(\frac{n+1}{3}\). In fact, in the circular sequence, local maximas, minimas and non-extream points have exactly the same probability and therefore equal expected number of them in the sequence.

Another way to see why this is true is by noticing that in each sequence there is always at least one global maxima which is equal to \(n\) and one global minima - which is equal \(1\). When we add zero to the sequence it basically replaces the role of the digit 1 and becomes a global minima and now 1 can be either local minima or non-extream point.

## Mathematical solution

Another solution could be found mathematically by noticing a recursive pattern
in the sequence. We can assume that we start with all possible permutations of
the sequences \(n-1\) unique numbers. From the available permutations we can
generate all possible permutations of the sequences with \(n\) unique numbers,
but adding \(n\) in each sequence to every possible position. For example,
from the sequence `1, 3, 2`

we can create 4 new sequences by adding number 4 in
every possible position and we will get `4, 1, 3, 2`

, `1, 4, 3, 2`

, `1, 3, 4, 2`

and `1, 3, 2, 4`

. Notice that it doesn’t matter where we put the largest number
in the sequence, it will always be a global maximum. The new number will either
be added between numbers that aren’t local maximas or near numbers where one of
them is local maxima (but never both). Basically, the new number will either add
one local maxima to the sequence or won’t change the number of local maximas (it
will absorb one of the existing maximas).

Let’s define \(s_{n}\) as a random sequence. We can imagine that there is a deterministic function \(l(s_{n})\) that takes a sequence as an input and returns a number of maximas in it. The expected number of local maximas can be defined as \(\mathbb{E}[l(s_n)]\). By the law of total expectation we can rewrite expectation in the following way

\[\mathbb{E}[l(s_n)] = \mathbb{E}[\mathbb{E}[l(s_n)|s_{n-1}]]\]The conditional expectation is much simpler since by the recursive definition we assume that the number of local maximas is known for the \(s_{n-1}\) sequence. Also, we know that the new number either increases the number of local maximas by one or doesn’t change it at all. We know that the number of local maximas doesn’t change when a new number is being added near one of the maximas (and there is always at least one in each sequence). We assume that there are \(m=l(s_{n-1})\) local maximas in the \(s_{n-1}\) sequence which means that there are \(2m\) places where we can add new number without changing number of local maximas and \(n-2m\) places where number of local maximas will increase. Therefore

\[\begin{align} \mathbb{E}[l(s_n)|s_{n-1}] &= \frac{2\,l(s_{n-1})}{n} l(s_{n-1}) + \left(1 - \frac{2\,l(s_{n-1})}{n}\right) (l(s_{n-1}) + 1) = \\ &= \frac{n-2}{n}l(s_{n-1}) + 1 \\ \end{align}\]And by the linearity of the expectation we can see the recursive dependency between expectations

\[\begin{align} \mathbb{E}[l(s_n)] &= \mathbb{E}[\mathbb{E}[l(s_n)|s_{n-1}]] \\ &= \frac{n-2}{n}\mathbb{E}[l(s_{n-1})] + 1 \end{align}\]And finally, we can prove by induction that the valid formula for expectation is

\[\mathbb{E}[l(s_n)] = \frac{n+1}{3}\]- We know that \(\mathbb{E}[l(s_2)] = 1\)
- Let’s assume \(\mathbb{E}[l(s_n)] = \frac{n+1}{3}\)
- We can show that the same holds for the \(\mathbb{E}[l(s_{n+1})]\)