# Intro to Balanced Search Trees

## Binary Tree Height

The difference in runtime between a worst-case tree and best-case tree is very dramatic.

Worst case: $\Theta(N)$

Best-case: $\Theta(\log N)$ (where $N$ is number of nodes in the tree)

The runtimes are dependent on the structure of the tree. If the tree is really spindly, then its basically a linked list and the runtime is linear. If the tree is bushy, then the height of the tree is $\log N$ and therefore the runtime grows in $\log N$ time.

### A short detour into BigO and worst case

BigO is not equivalent to worst case! Remember, BigO is an upper bound. As long as a function falls within that bound, it is considered to be inside the BigO of that function. Worst-case is more restrictive than BigO.

As an example, think about searching for hotels. If you are searching for a hotel with worst-case $500 rooms, then only a few hotels would fit that requirement, perhaps only the ritz carlton. On the other hand, if you were searching for hotels that are in O(500) or less than$500 rooms, then many hotels would fall under that category from the Motel 6 to the Rodeway Inn to the Ritz Carlton.

Thus, even though we said the worst-case runtime of a BST is $\Theta(N)$, it also falls under $O(N^2)$.

Many people use BigO as shorthand for worst-case, but this is technically not synonymous. We want you guys to be cream-of-the-crop computer scientists, so we are making this distinction!

## BST Performance

Some terminology for BST performance:

• depth: the number of links between a node and the root.
• height: the lowest depth of a tree.
• average depth: average of the total depths in the tree. You calculate this by taking $\frac{\sum_{i=0}^D{d_in_i}}{N}$ where $d_i$ is depth and $n_i$ is number of nodes at that depth.

The height of the tree determines the worst-case runtime, because in the worst case the node we are looking for is at the bottom of the tree.

The average depth determines the average-case runtime.

### BST insertion order

The order you insert nodes into a BST determines its height.

Exercise 11.1.1 Given integers 1-7, what order of inserting would result in the worst-case height? What order would result in the best case?

Answer To get the worst-case height you can just insert in order from 1-7. To get the best case height insert 4 first, then 2 and 6, then 1, 3, 5, 7. Convince yourself of this by actually going through the insertion process.

Yes, having a specific insertion order can help us get a balanced tree. However, we don't even need to be that intentional. If we just insert the nodes in random order, it will actually end up being relatively bushy!

You don't have to know the proof of this, but when we insert randomly into a BST the average depth and height are expected to be $\Theta(log N)$.

However, we won't always be able to insert into a BST in random order. What if our data comes in real-time? Then, we will be forced to insert in the order that data comes to us.

In the next chapter we will learn about a tree that always maintains its balance!