# Find the minimum of an array with a non-increasing and a non-decreasing part

# 1 Problem

Consider an array $a$ of $n$ entries from a totally ordered set. There exist a index $j$, such that $a[i]\geq a[i+1]$ for all $i < j$, and $a[i]\leq a[i+1]$ for all $i\geq j$.

How fast can we find such $j$? The worst time is $O(n)$. When all the elements are equal, you must transverse the entire array to figure that out.

If $m$ is the maximum time an element occurs in the array, then we can find an algorithm that solves this problem in $O(m+\log n)$ time.

# 2 Algorithm

The idea is to use ternary search, which works well when all values are distinct, and go for linear search once we figure there are a lot of repeated elements.

First, we present a lemma.

If there exist a index $i < j < k$, such that $a[i]=a[j]=a[k]$, then at least $\min(j-i,k-j)$ positions in the array has the same value as $a[i]$.

^{Image Credit: Vanessa Li.}

Consider we want to find the minima from index $l$ to $r$ (not including $r$), and $r-l>3$. Let $m_1=l+\lfloor \frac{r-l}{3} \rfloor$ and $m_2=r-\lfloor \frac{r-l}{3} \rfloor$ such that $l < m_1 < m_2 < r$.

- If $a[m_1] < a[m_2]$, then we know the minima is in $a[l..m_2]$. Recurse.
- If $a[m_1] > a[m_2]$, then we know the minima is in $a[m_1..r]$. Recurse.
- Otherwise $a[m_1]=a[m_2]$. If $a[l]=a[m_1]$ or $a[r]=a[m_1]$, then by the lemma, at least $1/3$ of the values between position $l$ and $r$ is $a[m_1]$. We can also test if $a[\lfloor \frac{m_1+m_2}{2} \rfloor]=a[m_1]$, if it is, then $1/6$ of the values between position $l$ and $r$ is $a[m_1]$. Since there are so many repeated values, we just do a linear search for the minima.
- Finally, if all above fails, we must have some value between position $m_1$ and $m_2$ take a different value from them. It must be a smaller value, and no value smaller than $a[m_1]$ can exist outside $a[m_1..m_2]$. Recurse on $a[m_1..m_2]$.

Here is the code in Go:

# 3 Complexity

$T(m,n)$ is the time to run the algorithm on an array of length $n$ with $m$ repeats.

$\displaystyle T(m,n) = \begin{cases} \frac{2}{3}T(m,n) + O(1) &\text{if strict inequality} \\ O(n) & \text{if } n \leq \frac{m}{6} \\ \frac{1}{3}T(m,n) + O(1) & \text{otherwise} \end{cases}$ For $n$ larger than $\frac{m}{6}$, the algorithm will have $O(\log \frac{n}{m})$ recursive calls, each one cost $O(1)$ time. Once it reaches small $n$, it will spend $O(n)=O(m)$ time on a linear search. The algorithm spends a total of $O(m+ \log \frac{n}{m}) = O(m+\log n)$ time.

# 4 Notes

Brosef Stalin offered an alternative logic that offers cleaner code.Can we generalize this to first increase then decrease then increase arrays? One can show $O(n)$ is best possible by considering an array with $a[i]=i$ for all $i\neq j$, and $a[j]=-1$. There is no way to find $j$ with out looking over every position.