### Quick description

Suppose that you have a handful of coins in your pocket and want to choose some of them to add up to a given total. The following would be an obvious method: keep selecting the coin of highest value that does not cause the total to become too big. If you have a large supply of coins of all denominations, this will result in an efficient selection. Because at each stage of the procedure, you try to take a step that gets you as close as possible to the target, an algorithm like this is called a greedy algorithm. Although greedy algorithms can seem rather unsophisticated, the concept is surprisingly important in many parts of mathematics.

### Prerequisites

They vary from example to example, but this article is written at a fairly elementary level.

### Example 1

A *proper vertex colouring* of a graph is a function defined on the vertices of such that if and are any two neighbouring vertices, then does not equal . A simple result in graph theory is the following: let be a finite graph such that each vertex has degree at most . Then there is a proper colouring of that uses at most colours. (This means that the function takes at most distinct values.)

To prove this, we choose some ordering of the vertices: and colour them inductively as follows. Having coloured , we would like to colour . By hypothesis, has at most neighbours, so there are at most neighbours of amongst the vertices . Therefore, there are at most colours that we must avoid when choosing a colour for . Since there are colours available to us, we can arbitrarily choose some colour that is not one of the ones we have to avoid. And thus the induction continues.

### General discussion

Why do we call that a greedy algorithm? The reason is that we do not worry about the future when we choose the colour for . For some problems, it turns out that we do not need to worry about the future, while for others it very definitely does. To give an example of a problem of the latter type, suppose that we have a bipartite graph with two vertex sets and , both of size . And suppose that we want to find a *perfect matching*, meaning a bijection such that is joined to for every . A greedy approach would be to choose an ordering of the vertices of and then choose distinct neighbours for each vertex in turn. However, it may well happen that even if a perfect matching exists, we get stuck when we do this process: then we find ourselves wanting to *backtrack*. (It turns out to be possible to devise a systematic way of backtracking that leads to a polynomial-time algorithm for this problem.)

It might be more accurate to call Example 1 a just-do-it proof, since many greedy algorithms have the following further feature: instead of making an *arbitrary* choice at each stage, one makes a choice that is *extreme* in one way or another. The coins example in the quick description has this property: we chose the coin of largest value at each stage, subject to the condition that we didn't go over our target. Another example that illustrates this type of greed is the game of Othello. Here, a greedy strategy for playing the game would be always to place your counter somewhere that causes as many as possible of your opponent's pieces to be turned over. Interestingly, this is virtually the worst strategy you can play, at least until near the end of the game, when obviously it becomes more sensible, though even in the endgame a greedy strategy is far from optimal. (One can also come up with endless real-life examples like this. If you are trying to run a mile as quickly as possible, then a greedy, but very silly, strategy is to run as fast as you possibly can right from the start. If you are a fly trying to escape from a house, a greedy strategy is to fly in the direction of maximally increasing brightness: this is quite a good strategy until you reach a closed window. And so on.)

## Comments

## real analysis

Fri, 24/04/2009 - 08:21 — devinWould the following example be too trivial?

One sees that any open set is the union of disjoint intervals using a greedy algorithm. For an element in let , be the collection of all open intervals lying in and containing . Let and The intervals in are open, connected and they are not disjoint of each other because they all contain . These imply that is also a member of and by its definition it is the maximal element of .

We now enumerate the rationals in and iterate over them as follows. Suppose , ,..., are the intervals chosen upon iterating over the first rationals. For the rational we check if intersects with , ,...,. If there is a nonempty intersection with a then by the maximality of and in this case we complete the step and proceed to the next rational. If is disjoint from , ,..., then we let .

Let be the largest number the index reaches in the above iteration (it may be ). Because the rationals are dense in it follows that .

## I've thought about this, and

Fri, 24/04/2009 - 09:05 — gowersI've thought about this, and in the end I think it isn't really an example, because its algorithmic nature isn't playing a genuine role. The "real" proof that underlies the argument you give is this: take all maximal open subintervals of your set, and check that no two of them can intersect.

## Minimum spanning trees?

Sat, 13/06/2009 - 05:38 — Louis (not verified)I'm not really sure about the etiquette for this site, so I hope this comment isn't too low-level.

In computer science, at least, pretty much the first example of greedy algorithms is for the problem of finding a minimum weight spanning tree in a graph (and then the generalization to the characterization of matroids by greedy algorithms).

Also, sometimes when greedy algorithms don't work optimally, you can still get something useful, like finding a linear-sized independent set in a planar graph or approximating set cover. (There are many examples of this nature.)

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