# Princeton Univerisy Lectures: Algorithms Part I – Union-Find(5) Union-Find Applications

by Kevin Wayne, Robert Sedgewick

Alright.

Now that we’ve seen efficient implementations of algorithms that can solve the unifying problem for huge problem instances let’s look to see how that might be applied.

There’s a huge number of applications of Union-find. We talked about dynamic connectivity in networks there’s many other examples in our computational infrastructure. Down at the bottom is one of those important one is in image processing for understanding how to label areas in images. We’ll see later Kruskal’s minimum spanning tree algorithm, which is a graph processing algorithm which uses Union-find as a subroutine. There’s algorithms in physics for understanding physical phenomenon that we’ll look at an example and many others on this list.

So, the one we’re going to talk about now is called percolation. That’s a model for many physical systems I’ll give an abstract model and then just talk briefly about how it applies to physical systems. So let’s think of an n by n grid of squares that we call sites. And we’ll say that each site is open. That’s white in the diagram with probably P or blocked, that’s black of the diagram with probability one – P and we define a system to, we say that a system is percolated if the top and the bottom are connected by open sites. So the system at the left, you can find a way to get from the top to the bottom through white squares, but the system to the right does not percolate, there’s no way to get from the top to the bottom through white squares.

So, that’s a model for many systems. You can think of for electricity. You could think of a vacant site as being a conductor and, and a block site as being insulated. And so if there’s a conductor from top to bottom then the thing conducts electricity. Or, you could think of it as, as water flowing through a porous substance of some kind. Where a vacant side is just empty and a block side has got some material, and either the water flows through from top to bottom, or not. Or you could think of a social network where it’s people connected and either there’s a c onnection between two people or not and these are a way not to get from one group of people to another communicating through that social network. That’s just a few examples of the percolation model.

So if we, we are talking abouta randomized model where the sites are vacant with the given probability. And so it’s pretty clear that if it’s. Probability that a site is vacant is low as on the left, two examples on the left in this diagram, it’s not going to percolate. There’s not enough open site for there to be a connection from the top to the bottom. If the probability is high and there is a lot of open sides, it definitely is going to percolate. There would be lots of ways to get from the top to the bottom. But in the middle, when it’s medium, it’s questionable whether it percolates or not. So the scientific question, or the, mathematical question from this model is, how do we know, whether it’s going to percolate or not?

In this problem and in many similar problems, there’s what’s called a phase transition. Which says that, you know, when it’s low, it’s not going to percolate. When it’s high, it is going to percolate. And actually, the threshold between when it percolates and when it doesn’t percolate is very sharp. And actually there is a value as N gets large that if you’re less than that value it almost certainly will not percolate, if you’re greater it almost certainly will. The question is what is that value. This is an example of a mathematical model where the problem is, is very well articulated. What’s that threshold value but, nobody knows the solution to that mathematical problem. The only solution we have comes from a computational model, where we run simulations to try and determine the value of that probability. And those simulations are only enable by fast union find algorithms, that’s our motivating example for why we might need fast union find algorithms, so let’s look at that.

So what we’re going to run is called a so called Monte Carlo simulation. Where we initialize the whole grid to be block ed all black and then we randomly fill in open sites. And we keep going. And every time we add an open site, we check to see if it makes the system percolate. And we keep going until we get to a point where the system percolates. And we can show that the vacancy percentage at the time that it percolates is an estimate of this threshold value. So what we want to do is run this experiment millions of times, which we can do in a computer, as long as we can, efficiently do the calculation of does it percolate or not. That’s a Monte Carlo simulation, a computational problem that gives us a solution to this, scientifc problem where, mathematical problems nobody knows how to solve yet.

So, let’s, look in a little bit more detail of how we’re going to use our dynam-, dynamic connectivity model to do this.

So, it’s clear that, we’ll create an object corresponding to each site.

And we’ll give’em a name, from zero to N^2-1 as indicated here. And then we’ll connect them together. If they’re connected by open sites. So the percolation model on the left corresponds to the, connection model on the right, according to what we’ve been doing. Now, you might say, well, what we want to do is, connect, check whether any site in the bottom row is connected to any site in the top row, and use union find for that. Problem with that is, that would be a brute force algorithm. Would be quadratic, right on the face of it. Because it would have N^2, calls to find, to check whether they’re connected. For each site on the top, I’d check each site on the bottom. Much too slow.

Instead, what we do is create a virtual site on the top and on the bottom. And then, when we want to know whether this system percolates, we just check whether the virtual top site is connected to the virtual bottom site. So how do we model opening a new site?

Well to open a site we just connect it to all it’s adjacent open sites. So that’s a few calls to Union but that’s easy to implement.

And then with that, simple, relationship we can use the exactly the code that we developed to go ahead and run a simulation for this connectivity problem. And that’s where we get the result that, by running enough simulations for a big-enough n, that this, percolation threshold is about .592746. With this fast algorithm we can get an accurate answer to the scientific question. If we use a slow Union-find algorithm we won’t be able to run it for very big problems and we won’t get a very accurate answer.

So in summary, we took an important problem. The, the dynamic connectivity problem. We modeled the problem to try to understand precisely what kinds of data structures and algorithms we’d need to solve it. We saw a few easy algorithms for solving the problem, and quickly saw that they were inadequate for addressing huge problems. But then we saw how to improve them to get efficient algorithms. And then left us with, applications that, could not be solved without these efficient algorithms. All of this involves the scientific method. For algorithm design where we try to develop mathematical models that help us understand the properties of the algorithms that we’re developing. And then we test those models through experimentation enabling us to improve algorithms iterating, developing better algorithms and more refined models until we get what we need to solve the practical problems that we have of interest. That’s going to be the overall architecture for studying algorithms that we’re going to use throughout the course.

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