Okay welcome to the summary of module three. So what's happened in the story, Jimmy? >> Well, a few things happened. We've seen how Cao Cao and Liu Bei, they have successfully lead the coalition in several strikes against Dong Zhuo's army. And then also together with Yuan Shao, they have also claimed a new city as well. At the same time, a very important comeback happened between our three heros and Lu Bu. It was a draw, but Lu Bu had to flee. >> Right, yeah. So technically, what are we seeing this week? So this week is all about functions. And we started off looking at the assignment problem, which is about building an injective function. So the assignment problem, we have one set of objects, the domain, and another set of objects, the co-domain. And we're basically trying to assign one of these co-domain objects to each of the objective domain, and that's an injective function. And it really actually is the basis of where the all different constraint comes from because that's what is encoded. >> But assignment problems are easy to solve. >> Yes, so if you just have a pure assignment problem, then actually there's very specific techniques, that's mostly matching techniques, which will solve those problems very, very quickly. The problem is, in the real world, nothing is likely to be a pure assignment problem. And so there'll be side-constraints, and then we'll need to use more general technologies like the discrete optimization technology which we're talking about in this course. >> So you mean a side constraint will essentially break the easiness of and assignment problem. >> Yes, absolutely, the summer prom can become arbitrarily difficult if we just added just a few, what looks like simple side constraints. >> We can also look at the general functions. >> Yes, so we move on to general functions and now we can think of those functions in two different ways. So we can think as a mapping from the domain to the co-domain, just as a normal sort of functional viewpoint. >> Yeah. >> But we can also think of them as a partitioning, where we're basically taking the sets of the domain and partitioning them by the value that they're given in the co-domains. So it's really breaking up the domain into sets all labeled by one of the co-domains. >> Okay, so this module is all about functions. >> Yep. >> We've also learned some modeling techniques as well, right? One of them is common sub-expressions. >> Yes, so common sub-expressions are very important if we have the same thing that we're trying to use in multiple places in our model, and we should often give it a name, give it an intermediate variable, and reuse that variable so that we're telling our solver not to basically compute the same thing twice. >> So comments of expressions are not good things so we should eliminate them. >> Absolutely. Now, many think we'll try to do this for you but it's much better if you do it yourself, then you're guaranteed to know they're eliminated. Sometimes minuting won't be able to determine that something is really the same thing. We also saw a new global constraint called global cardinality. >> So global cardinality is great kind of extension of all different, very much attached to the set partitioning viewer functions, which allows us to count how many values in our domain are being assigned to each partition. >> So we going to see more of global cardinality and its relatives in chapters to come. >> Absolutely, that’s an important global constraint. >> We also saw symmetries, a special kind of symmetries called value symmetries. >> Yes, so value symmetries are very common discreet optimisation problems where two values are interchangeable. We saw it in the clustering problem where the names of the two clusters really didn't matter and in order to defeat these symmetries, we introduced the value proceed chain global constraint. Which is really designed exactly for this, to get rid of asymmetries to only leave one possible answer. >> Actually without the value proceed chain, we could also consider adding some sort of ordering on the values as well, right? That's one other way of breaking values asymmetries. >> Well, value proceed chains effectively does that by ordering where those values appear in our solution. >> Okay. >> So we've seen some applications actually some, we saw rostering, that's a very important discrete optimization problem done by all small and large corporations around the world everyday. >> When we have constraints with our clustering problem, we could make use of discrete optimization technology to solve such problems as well. >> Yeah, so clustering by itself, if we're just doing pure clustering, then there's thousands of clustering methods out there which will be much more efficient and designed for very large problems. But once we add side constraints, again, a lot of these things are not applicable anymore, and then we need to think about using more generic technology like discrete optimization technology. >> Okay, when it comes to the workshop and the assignment we have to introduce a very important person, Diao Chan, who is actually going to break up the friendship of Lu Bu and Dong Zhuo. >> Yeah, so the way she's going to do this is to seduce both of them. So the way she seduces Dong Zhuo is by giving the great feast, so that's what you're going to be working out, it's how she can prepare the best possible feast. And the way she seduces Lu Bu is with a dance. And you're going to have to work out the best dance she can make. And these are complicated problems which have interesting constraints about the sequence of what goes on. In the workshop solution, we'll actually introduce a new global constraint called regular. You can do the workshop without it but when you look at the solution, you'll be able to see that as well. >> It's interesting to see how a feast and a dance can change history. >> Absolutely. >> Okay, we'll see you after the next module.
