Friday, March 08, 2013

Weight Balance for fun and profit

According to Dr. Bender, Weight Balance (different from the wikipedia article on weight-balanced tree) is one of the most important topics in Computer Science, and we were lucky enough to learn it from him! Others might not be so lucky, so let me try and do a job that's hopefully a constant factor (or fraction) as good - since it won't matter because all we care about are the asymptotics.

Pre-requisite: This note on Amortized Analysis.

We'll use weight balance to implement a dictionary structure and examine how the guts of one such structure, the weight-balanced tree work.

A dictionary data structure is one that supports the following dictionary data structure operations:
  1. Insert
  2. Delete
  3. Find
  4. Predecessor
  5. Successor
Points 4 & 5 are what distinguish a dictionary data structure from a keyed data structure such as a Hash Table.

Now, you might have heard of the following data structures that (efficiently) support the operations mentioned above:
  1. Treaps
  2. Skip-Lists
It would surprise (or maybe not) you to know that both these structures work on the principle (guess) of weight-balance!!

So what exactly do we mean when we talk about the weight of a sub-tree in a BST? Well, as it turns out, the weight of the sub-tree in a BST is just the count of the number of nodes in the sub-tree rooted at that node (including the node itself).

For example, the following tree (image courtesy wikipedia) has a weight of 9
and the sub-tree rooted at node 10 has a weight of 3.

A weight-balanced tree rooted at node u is one in which (either):
  1. The weights of the left and right children of a sub-tree are within constant factors of each other:
    weight(Left-Child(u)) + 1 = Θ(weight(Right-Child(u) + 1)
    Note that the +1 is important for pedantic reasons as far as the order-notation is concerned.
  2. The weights of the left and right children of a sub-tree are within constant factors of the weight of the complete sub-tree
    weight(Left-Child(u)) + 1 = Θ(weight(u) + 1) AND
    weight(Right-Child(u)) + 1 = Θ(weight(u) + 1)
It turns out that both these definitions are equivalent.

More realistically, if we stick to the second definition, we have:
weight(Child(u)) + 1 ≥ 0.25 * (weight(u) + 1) AND
weight(Child(u)) + 1 ≤ 0.75 * (weight(u) + 1)

where, Child(u) denotes both the left & right child of u.

For example, if we consider the following example tree, which is clearly out of weight-balance (don't ask me how we got there because this example is made-up), we re-balance it to be perfectly in balance (if we have an odd number of nodes or almost perfectly balanced otherwise).

We should be careful about how we re-balance these N nodes, because if the cost is any worse than Θ(N), then we won't get the update costs that we desire. The easiest way to perform the re-balance with a cost of Θ(N) is to perform an in-order traversal of the subtree rooted at node u, and write out the sorted nodes to an array. We can then re-create a perfectly balanced BST from that array either using recursion or the Day–Stout–Warren algorithm.

This is where the fun starts!!

Q 1. How many nodes need to be inserted under a sub-tree rooted at node v to bring the sub-tree out of balance (assuming it is perfectly balanced to start off with)? Let's assume that the sub-tree originally contains N nodes.
A. You need to insert some constant fraction of the weight of that sub-tree! which is Ω(N).

Q 2. What is the cost to rebalance the sub-tree rooted at node v if we know that that sub-tree has a weight of N?
A. Well, we already answered this above. The answer is Θ(N).

Q 3. How many sub-trees potentially go out of balance when you insert a node?
A. We know that a node is inserted at the leaf level, so potentially all the sub-trees that are rooted at the nodes on the leaf-to-root path with the newly inserted node as the leaf node can potentially go out of balance. This happens to be Θ(log N) nodes.

∴ the amortized cost to insert a new node into the balanced tree is:
Ω(N)/Θ(N) * Θ(log N) = Θ(log N).

Now, that's a fairly straight-forward algorithm to get the same (amortized) costs as the worst-case costs for updates with a more complicated beast such as an RB-Tree or an AVL-Tree. Though, I feel that Treaps are much simpler to implement.

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