# Princeton Univerisy Lectures: Algorithms Part I – Union-Find(3) Quick Union

by Kevin Wayne, Robert Sedgewick

All right so QuickFind is too slow for huge problems. So, how are we going to do better?

Our first attempt is an alternative called, Quick-union. This is so called lazy approach to algorithm design where we try to avoid doing work until we have to. It uses the same data structure or array ID with size M but now it has a different interpretation. We are going to think of that array as representing a set of trees that’s called a forest as depicted at right. So, each entry in the array is going to contain a reference to its parent in the tree. So, for example, 3’s parent is four, 4’s parent is nine. So 3’s entry is four and 4’s entry is nine in the array. Now each entry in the array has associated with it a root. That’s the root of its tree.

Elements that are all by themselves in just, in their own connected component, point to themselves, so one points to itself but also nine points to itself. It’s the root of the tree, containing two, four and three. So, from this data structure we can associate with each item a root, which is representative, say, of it’s connected component.

So that’s the root of three is nine, going up that root.

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Now, once we can calculate these roots, then we can implement the find operation just by checking whether the two items that we’re supposed to check with are connective where they have the same root. That’s equivalent to saying, are they in the same connective component? So that’s some work, going to find the roots of each item but  the union operation is very easy. To merge components containing two different items. Two items that are in different components. All we do is set the ID of P’s route to the ID of Q’s route.  Let’s make P’s tree point to Q. So in this case, we would change the entry of nine to be six to merge three and five. The components containing three and five. And with just changing one value in the array we get the two large components emerged together. That’s the Quick-union algorithm. Because a union operation only involves changing one entry in the array.

Find operation requires a little more work. So let’s look at the Implementation, a demo of that one in operation first. So again we, we start out the same way but now the idea array entry really means that every one of these things is a little tree where the one node each everyone pointing to itself.

It’s the root of it’s own tree so now if we have to put four and three in the same component, then all we do is we take the root, of the component containing the first item and make that a child of the root of the component, component containing the second item.

In this case we just make four as parent three. So now three and eight. So again, we take the first item and make it a child of the root of the tree containing the second item. So now three, four, and eight are in the same component. Six and five six goes below five. Nine and four, So now four is the root of the tree containing four is eight.

And the root of tree containing nine is nine. And so we make nine a child of eight.

Two and one, that’s an easy one. Now if we get our eight and nine connected, we just checked that they have the same root and they both have the same root eight and so they’re connected.

Five and four 4’s root is eight. 5’s root is five. They’re different. They’re not connected.

Five and zero. Five goes to be a child of zero.

Seven and two seven goes to be a child of 2’s root which is one.

Six and one. 6’s route is zero 1’s its own route, so zero becomes a child of one. Each one of these union operations just involves changing one entry in the array.

And finally, seven and three. So seven’s root is one, three’s root is eight, one becomes a child of eight.

Okay and now we have one connected component with all the items together. Alright, so now let’s look at the code for implementing Quick-union. The constructor is the same as the other one. We create the array and then set each element to be it’s own root. Now we have a private method that implements this process of finding the root by chasing parent pointers until we get to the point where I is equal to ID of I, and if it’s not equal, we just move I up one level in the tree, set I equals ID of I and return it.

So starting at any node, you just follow ID equals ID of I until they’re equal and then you’re at a root and that’s a private method that we can use to implement the find operation or the connected operation. You just find the root of P and the root of Q and if you check if they’re equal. And then the union operation is simply find the two roots I and then set the idea the first one could be the second one. Actually less code than for Quick Find, no fore loops. There’s this one wild loop that we have to worry about a little bit. But that’s a quick and elegant implementation of code to solve the dynamic connectivity problem called Quick-union.

So now we’re going to have to look at can this code be effective for large problems? Well unfortunately Quick-union is faster but it’s also too slow. And it’s a little different kind of too slow then for Quick Find, there’s times when it could be fast, but there’s also times when it could be too slow. And the defect for Quick-union is that the trees can get too tall. Which would mean that the find operation would be too expensive. You could wind up with a long skinny tree. Of each object just pointing to next and then to do a find operation for object at the bottom would involve going all the way through the tree. Costing involving in the ray axises just to do the find operation and that’s going to be too slow if you have a lot of operations.

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