How to spot a "greedy" algorithm?

Question

I am reading a tutorial about "greedy" algorithms but I have a hard time spotting them solving real "Top Coder" problems.

If I know that a given problem can be solved with a "greedy" algorithm it is pretty easy to code the solution. However if I am not told that this problem is "greedy" I can not spot it.

What are the common properties and patterns of the problems solved with "greedy" algorithms? Can I reduce them to one of the known "greedy" problems (e.g. MST)?

Farly

Answer 1

Formally, you'd have to prove the matroid property of course. However, I assume that in terms of topcoder you rather want to find out quickly if a problem can be approached greedily or not.

In that case, the most important point is the optimal sub-structure property. For this, you have to be able to spot that the problem can be decomposed into sub-problems and that their optimal solution is part of the optimal solution of the whole problem.

Of course, greedy problems come in such a wide variety that it's next to impossible to offer a general correct answer to your question. My best advice would hence be to think somewhere along these lines:

• Do I have a choice between different alternatives at some point?
• Does this choice result in sub-problems that can be solved individually?
• Will I be able to use the solution of the sub-problem to derive a solution for the overall problem?

Together with loads and loads of experience (just had to say that, too) this should help you to quickly spot greedy problems. Of course, you may eventually classify a problem as greedy, which is not. In that case, you can only hope to realize it before working on the code for too long.

(Again, for reference, I assume a topcoder context.. for anything more realistic and of practical consequence I strongly advise to actually verify the matroid structure before selecting a greedy algorithm.)

Amp

I think you meant "matroid property" and not "monoid property". Also could you be more specific on the "optimal substructure" property? I know this property from dynamic programming, where you decompose your problem into several subproblems and then recombine them. However looking at minimal spanning tree for example I have a hard time seeing how there is a similarity, since the algorithm always just keeps adding to the previous solution. – Surname

You are right, of course I meant matroid here. MST substructure can be seen if you look at the problem as removing the edges and having to connect the remaining graph to the set of already included vertices. – Amp

Answer 2

A part of your problem may be caused by thinking of "greedy problems". There are greedy algorithms and problems where there is a greedy algorithm, that leads to an optimal solution. There are other hard problems that can also be solved by greedy algorithms but the result will not necessarily be optimal.

For example, for the bin packing problem, there are several greedy algorithms all of them with much better complexity than the exponential algorithm, but you can only be sure that you'll get a solution that is below a certain threshold compared to the optimal solution.

Only regarding problems where greedy algorithms will lead to an optimal solution, my guess would be that an inductive correctness proof feels totally natural and easy. For every single one of your greedy steps, it is quite clear that this was the best thing to do.

Typically problems with optimal, greedy solutions are easy anyway, and others will force you to come up with a greedy heuristic, because of complexity limitations. Usually a meaningful reduction would be showing that your problem is in fact at least NP-hard and hence you know you'll have to find a heuristic. For those problems, I'm a big fan of trying out. Implement your algorithm and try to find out if solutions are "pretty good" (ideal if you also have a slow but correct algorithm you can compare results against, otherwise you might need manually created ground truths). Only if you have something that works well, try to think why and maybe even try to come up with proof of boundaries. Maybe it works, maybe you'll spot border cases where it doesn't work and needs refinement.

Gastineau

Answer 3

"A term used to describe a family of algorithms. Most algorithms try to reach some "good" configuration from some initial configuration, making only legal moves. There is often some measure of "goodness" of the solution (assuming one is found). The greedy algorithm always tries to perform the best legal move it can. Note that this criterion is local: the greedy algorithm doesn't "think ahead", agreeing to perform some mediocre-looking move now, which will allow better moves later.

For instance, the greedy algorithm for egyptian fractions is trying to find a representation with small denominators. Instead of looking for a representation where the last denominator is small, it takes at each step the smallest legal denominator. In general, this leads to very large denominators at later steps.

The main advantage of the greedy algorithm is usually simplicity of analysis. It is usually also very easy to program. Unfortunately, it is often sub-optimal." --- ariels (http://www.everything2.com/title/greedy+algorithm?searchy=search)

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