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  • PROFESSOR: So today we're going to continue

  • our course on the graph theory.

  • It's going to be a mixture of all kinds of topics.

  • We first start off with Euler tour,

  • and then we get into directed graphs

  • and cover the definitions.

  • And we'll talk about a special type, which

  • are called tournament graphs.

  • And we'll do a whole bunch of proofs,

  • and hopefully you will all contribute to make this work

  • and think about how to do this.

  • So next week we will continue with graph theory,

  • and we will discuss a very special type.

  • We will use directed graphs in communication networks.

  • And on Thursday, we'll actually use these special types

  • of graphs that we'll talk about in a moment, DAGs.

  • So let's talk about Euler tour.

  • Euler, a famous mathematician, he asked the question

  • like-- he lived in Konigsberg.

  • And there were seven bridges.

  • He was wondering, can you traverse all the bridges

  • exactly once.

  • So he would start walking and try to cross all those bridges.

  • And apparently this little islands in sort of the river

  • as well, and so on.

  • So how can you do that?

  • This is actually the birth of graph theory.

  • And this particular problem is named after him.

  • So what is an Euler tour?

  • It's actually defined as a special walk.

  • It's a walk that traverses every edge exactly once.

  • So it turns out the you can actually characterize

  • these types of graphs.

  • So we are talking here about undirected graph,

  • so continuation of last time.

  • So the edges that we consider have no direction.

  • We'll come back to that in a moment.

  • We will start defining those later.

  • So Euler tour is a walk that traverses every edge exactly

  • once.

  • And at the same time, it's also a tour.

  • So that means that it actually starts and finishes

  • at the same vertex.

  • Now it turns out that undirected graphs,

  • the graphs that we've been talking about,

  • those that Euler tours can be easily characterized.

  • And that's the theorem that we're going to prove next.

  • The theorem states that actually,

  • if a connected graph has an Euler tour,

  • if and only if, every vertex of the graph has even degree.

  • So that's a very nice and simple and straightforward

  • characterization.

  • So how can we prove this?

  • So let's have a look.

  • So first of all, we have an if and only if statement.

  • So we need to prove two directions.

  • We need to proof that if I have a connected graph that

  • has an Euler tour, I need to show that every vertex has

  • even degree.

  • And also the reverse-- if I have a graph for which every vertex

  • has even degree, I need to show that it has an Euler tour.

  • So the proof consists of two parts.

  • So let's first do this implication,

  • where we assure that we have connected

  • graph that has an Euler tour.

  • So assume we have a graph.

  • So we have G with the vertex at V, edge is at E,

  • and it has an Euler tour.

  • So what does it mean?

  • Well, it's really means that we can walk

  • from some start vertex, V0.

  • We can go all the way around to V1, and some more, and so on,

  • to V, say, k minus 1.

  • And then end vertex is the same as the start vertex.

  • So we have a walk that goes all around the whole graph.

  • So every edge in this walk are exactly the edges

  • that are in the graph.

  • And each edge in the graph is offered exactly once.

  • So what does it mean?

  • So let's write this down actually.

  • So since every edge is traversed once-- every edge in E

  • is traversed once-- what can we conclude from that?

  • Can we say something about, given such as walk of length k,

  • what can we say about the number of edges, for example?

  • What can we say about the degree of the vertices?

  • Because that's what you want to show, right?

  • You want to show that every vertex has even degree.

  • Does anyone know how to go ahead here?

  • So we know that every single edge in E

  • is referred ones within this whole walk.

  • So what kind of properties can we derive?

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: That's true.

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: Maybe someone else can pick up here.

  • So every vertex that I have here, I will enter it.

  • But I also leave it.

  • So if I see a vertex in here, I can

  • see at least two contributing edges

  • that are coming from this.

  • AUDIENCE: Then you know that that note has at least

  • degree 2, but it can't be more than 2

  • because otherwise that would be the endpoint.

  • PROFESSOR: Ah.

  • But is it true though that-- so for example,

  • this particular note here may repeat itself somewhere else.

  • I only have a condition on the Euler tour

  • that every edge occurs exactly ones.

  • So how could I formally sort of continue to prove?

  • AUDIENCE: Well, it will intersect itself again,

  • and you'll leave again and it will still

  • have the even number.

  • PROFESSOR: Yeah.

  • That's true.

  • That's right.

  • Any other additions?

  • AUDIENCE: You can define the number of edges

  • that it will test by how many you enter it,

  • so the number of edges that it has

  • is twice the number of [INAUDIBLE].

  • PROFESSOR: Yeah.

  • That's correct. [INAUDIBLE]

  • AUDIENCE: [INAUDIBLE] traverse once, then every time you enter

  • a node you have to leave it.

  • So you can say that you're never going

  • to leave a node for a node that it already left for from there.

  • And you're never going to enter from a node you already entered

  • from or left to from there.

  • So that's how you can say that you're only going

  • to increment your degree by 2.

  • PROFESSOR: That's true.

  • So what he's essentially saying is

  • that every edge as I start to count

  • here, in this particular tour, well they're all different.

  • So they all counts towards a degree of the node.

  • So everything that you have been saying is right on.

  • What we can conclude here is that if you look at a vertex,

  • say vertex U-- for example, somewhere over here-- well,

  • it may repeat itself over here.

  • And it has an incoming and outgoing edge, incoming

  • and outgoing edge.

  • And I may have it somewhere else,

  • but say it's just at those two.

  • Well, I know that all the edges are different.

  • So I can clearly count this now.

  • I can say that the degree of U must

  • be equal to the number of times that U actually

  • appears in this tour.

  • So in the tour from V0 all the way to Vk minus 1.

  • And then I have to multiply times 2.

  • And what I said, it's exactly what you have been saying,

  • all the edges occur exactly once of the whole graph.

  • So I just count the number of times I see U in here.

  • I come the number of edges that go into it and leave from it.

  • Well, that's twice times the number of times

  • that U actually appears in this tour.

  • So this implication is pretty straightforward

  • because now we actually note that the degree of U is even.

  • So that's great.

  • So let's talk about the other implication

  • and see whether we can find a trick to make that happen.

  • So what do we need to start off with?

  • Well, now we have to assume that every vertex has even degree.

  • So let's write this down.

  • So say we have the graph.

  • And for this graph we actually issue

  • that the degree of every vertex V is even.

  • Well, what can we do?

  • Well, this is sort of creative trick,

  • so let me just continue here.

  • So what we have is if you start to consider a walk, W,

  • that touches, say, a lot of vertices in such a way

  • that W is actually the longest walk.

  • So that's often what you do in graph theory.

  • You think about, say, the shortest path

  • or whatever with a certain property, or the longest walk,

  • or whatever with a certain property.

  • So that's what you're doing here.

  • So let W be the longest walk.

  • And well, we want to prove something about an Euler tour.

  • An Euler tour has a very specific property

  • that all the edges occur only once.

  • So it makes sense to look at walks

  • in which all the edges of which it walks are unique,

  • are occurring only once.

  • So we're interested in the longest walk that traverses

  • no edge more than once.

  • So that's important.

  • Now, let's think about this a little bit.

  • So the first property that you may want to consider it-- you

  • really want to show that W is going to be an Euler tour.

  • That's what we're going to try to prove here.

  • So the first thing we may want to show

  • is that actually it goes all around.

  • So V0 is equal to Vk.

  • That will be great if we can do this.

  • So we want to show that this is true.

  • So what would happen if this would not be the case?

  • So suppose these two would not be equal to one another.

  • Well, actually, I'm skipping a little bit ahead here

  • in the proof, I notice.

  • So this will be the conclusion of another observation.

  • So let me start with the first observation.

  • So first of all, let us consider this end node over

  • here, this end vertex, Vk.

  • Now if there is an edge that is not covered in this walk--

  • and I can lengthen the walk, right?

  • So if I have, say, another edge, which

  • looks like Vk, an edge from Vk to some U, where

  • this edge is not in the walk.

  • Or the other one is not in the walk.

  • Well, it doesn't matter in an indirect graph, of course.

  • So if this is not in the walk, then what can I do?

  • I can just lengthen it, right?

  • I can just say V0 goes to V1 goes to Vk goes

  • to U. Now I have a longer walk.

  • And that's a contradiction.

  • So I know that all the edges in which Vk is participating

  • are actually covered by the walk.

  • So that's a great property.

  • So let's write this down.

  • So all edges that are incident to Vk

  • are actually used-- traversed-- in the walk, W.

  • So now let's have a look whether we

  • can show that it's a walk that goes all the way around.

  • So let's try to prove this statement.

  • So let me repeat this over here.

  • So what we want to show is that Vk equals V0.

  • So given this statement, do you have any idea

  • how we could possibly prove this,

  • also given what we have been doing over here?

  • Suppose this would not be the case, right?

  • So suppose the start and the end node in this walk

  • are not equal to one another.

  • So what can we do here?

  • So maybe there's some suggestions?

  • I already had you once.

  • Maybe someone else?

  • AUDIENCE: It has to attach to another vertex.

  • And you know that all the degrees are even.

  • So it means that Vk must attach to V0.

  • PROFESSOR: So what you're saying, first of all,

  • is that if this would not be the case, then

  • you know that the degrees must be even.

  • But suppose this would not be the case.

  • That would mean actually that the degree would be odd, right?

  • OK.

  • So let's think about that a little bit.

  • So let's write this down.

  • So otherwise the Vk has odd degree.

  • But the only node has odd degree in this particular walk,

  • W. So that's what we do now.

  • Because we really consider this walk.

  • So we see that if I have V0 unequal to Vk and Vk

  • maybe repeated in this walk a number of times,

  • each time it is repeated it has an incoming

  • and an outgoing edge.

  • And all these edges are different because they do not

  • occur more than once.

  • So whenever Vk enters in this middle part over here--

  • it's partaking over here-- it gives an even contribution

  • to the number of edges in this walk,

  • and it has one extra incoming edge over here.

  • So if V0 is not equal to Vk, we will

  • have an odd degree of edges in W,

  • in which Vk is participating.

  • So now we can go ahead and we can have a look over here

  • because we just showed here that all the edges incident to Vk

  • are actually used in W. So we can now conclude that this

  • means that Vk also has odd degree in G

  • because all the edges in G, in which Vk is participating,

  • are actually used in the walk.

  • So I hope that is clear.

  • So let's write this down.

  • So we'll use the first statement over here, so by 1.

  • We have that Vk has odd degree in G.

  • And now we come to what you were saying.

  • We have assumed over here that the degree is even.

  • So this cannot be the case, so that's a contradiction.

  • So we know that the otherwise situation cannot occur.

  • So we know that Vk must be equal to V0.

  • So that's proving that we actually have

  • a walk that goes all around.

  • Now, we are not yet done.

  • Because why would this be an Euler tour?

  • For an Euler tour, we really want

  • that every single edge that G has

  • is actually used in the tour.

  • So we need much more.

  • So let's use this board.

  • So suppose that W is not an Euler tour.

  • We're going to use contradiction.

  • But we already have shown this particular property,

  • so we can use this.

  • So suppose W is not an Euler tour.

  • Well, we know that G is connected.

  • So that's a good thing because that

  • means that if it is not an Euler tour,

  • there is an edge that is not used in W, in the walk,

  • but it is incident to some vertex that is used in W.

  • So this is where the connectivity here

  • comes in play.

  • Now let us call this edge-- so let

  • U-Vi be this particular edge.

  • So U is not used in the walk-- well, it's

  • maybe used in the walk, but the edge is not used in the walk.

  • And Vi is part of the walk.

  • So what can we do here?

  • So does anybody see what we could do here?

  • Can we somehow get a contradiction?

  • So when we started out here, we actually

  • assumed that W is the longest walk in which the edges do not

  • occur more than once.

  • Can we create a longer walk by simply using this?

  • Now, notice that we did prove that V0 goes all around

  • to Vk, and so on.

  • So can we find a longer walk that uses this extra edge?

  • Any ideas?

  • What would happen if we, for example, started a walk with U?

  • Let's see how that would work out.

  • We can have U. We walk to Vi.

  • Well, let's just simply start walking all around.

  • So essentially we have Vi over here and U over here.

  • So we could do is we can start walking like this

  • and then end over here.

  • And notice we have used one extra edge.

  • So we get a longer walk.

  • So we go to Vi plus 1, and we go all the way

  • up to Vk, which is equal to V0.

  • And then we go up to V1, all the way up to Vi.

  • This is a longer walk.

  • And therefore we have a contradiction.

  • And now the proof is finished because that means

  • that this assumption was wrong.

  • So W actually is an Euler tour.

  • So now we have shown the existence of an Euler tour,

  • and that was the other direction of the theorem.

  • So let's now start with a new topic on directed graphs.

  • And later on, we will talk about how Hamiltonian paths.

  • And these are different from an Euler

  • tour in that in an Euler tour, every edge is used exactly

  • once.

  • In a Hamiltonian path, we will have that every vertex

  • is used exactly once.

  • So we'll do that a bit later.

  • So what are directed graphs?

  • Directed graphs are graphs where we have edges

  • that have a specific direction.

  • So we can walk from one vertex to the other.

  • Let me just depict an example.

  • For example, we could have something that looks like this.

  • We have V1.

  • We have V2, V3.

  • We may have an edge that goes like this with an arrow.

  • We use arrows in this case.

  • I can go to V2 and V3.

  • From V2 I may be able to go to V3, and from V3 to V2.

  • So this will be a directed graph, as an example.

  • We also call these digraphs.

  • And as you can see, we may have an edge that goes from V2 to V3

  • and also one that goes from V3 to V2.

  • So these are two separate edges.

  • So every edge has a direction.

  • And we usually say that if we have an edge that

  • points from V2 to, say, V3, then we call this the tail

  • and over here we have the head.

  • So this is some notation that you may use.

  • We also have now a different notion

  • for the degree of a vertex because we have different types

  • of edges, essentially.

  • Take, for example, V2.

  • You have incoming edges and outgoing edges.

  • So that's why we're going to talk

  • about an indegree and an outdegree.

  • So, for example, the indegree of V2 is equal to, well, I

  • have one, two incoming edges.

  • And the outdegree of V2 is actually something different.

  • It's just one outgoing edge.

  • So this is equal to 1.

  • So this is some notation.

  • So let's talk about walks.

  • Let's figure out where we can enumerate

  • walks in a directed graph and compute those.

  • So how many walks do I have, say,

  • of length k that go exactly from, say, V1 to V3.

  • How can I compute this?

  • So I'd like to introduce adjacency matrices.

  • You've probably seen them before.

  • But let's go over this once more because induction is also

  • important.

  • So let's do the theorem.

  • So the theorem is this.

  • Suppose we have a graph.

  • And suppose it has end nodes.

  • And we have the vertices V1 up to Vn.

  • And now we let the matrix that contains the entries aij denote

  • the adjacency matrix for G.

  • And what does it mean?

  • It's actually means that in this case,

  • we say that aij is equal to 1 if we actually

  • have an edge that goes from Vi to Vj--

  • so this is an edge-- and 0 if this is not the case.

  • So this is the adjacency matrix.

  • And now we can state something about the number of directed

  • walks in a directed graph.

  • So it turns out that this is easily

  • computed by taking powers of the adjacency matrix.

  • Let aij with the superscript k be

  • equal to the number of directed walks of length k

  • that go from Vi to Vj.

  • So you want to compute this and it turns out

  • that-- wait a minute.

  • So what did I do here?

  • So actually let p kij be the number

  • of directed walks of length k from phi i to phi j.

  • Then what you can show is if you look at matrix A

  • to the power k, this actually is the matrix that

  • contains all these numbers.

  • So let me give a few examples.

  • So let's take the matrix over here.

  • Look at this ajc matrix, take a few powers

  • and see what happens.

  • So the matrix looks like this.

  • If you just label the rows by, say, V1, V2, and V3.

  • The columns by V1, V2, and V3.

  • Well, V1 has an edge to V1.

  • V1 has an edge to V2 and also one to V3.

  • V2 has only one edge to V3, so we have zeros here.

  • And we have this.

  • So this is the matrix A.

  • For example, if you compute a squared.

  • So how do we do this?

  • How many of you know about matrix multiplication?

  • Everybody knows?

  • Well anyway, so let's assume that you do know actually.

  • So that's pretty simple.

  • So you take the column.

  • You take the inners product with the first row, and so on.

  • And you can easily compute this otherwise

  • you may want to do it yourself later on.

  • And you get this particular matrix.

  • And, for example, a to the power of 3

  • is something very similar-- 1, 3, 3, 0, 0, 1, 0, 1, 0.

  • Well, it turns out that if you look at this graph

  • and we, for example, want to know the number of walks

  • of length 3 that go from V1 to, say, V2,

  • let's see whether we can see those over here.

  • Well, if I travel from V1 to V2 in three steps,

  • I can go like this.

  • I can go one, two, three.

  • So that's one option.

  • I can go one, and another one time,

  • so it's a second step, and three.

  • I can also go directly over here and go back here, and back over

  • here.

  • So it turns out that's I can compute this.

  • So how do we prove this?

  • We'll use induction.

  • And the steps are pretty straightforward.

  • So let's first define, because we

  • want to prove that kth power of a is equal to that matrix

  • up there, where all the entries represent the number of walks.

  • So let's say that aijk, that this denote the i jth

  • entry in a to the power k.

  • And you want to show that this number is actually

  • equal to pijk for all the entries.

  • So if you're going to use induction,

  • it makes sense to assume that the theorem is true for k.

  • So the induction hypothesis could be something like this.

  • The theorem is true for k.

  • And this is really the same as stating

  • that all these entries for all i and j-- aij

  • is equal to the pijk.

  • So this is what you want to prove.

  • And this is pretty straightforward

  • because now what we can do is we can

  • start to look at how we compute walks of length k, or k plus 1.

  • So let's first do the base case.

  • That's how we always start.

  • The base case is k equals 1.

  • And we have essentially two options.

  • So we want to prove this, so take an i and a j,

  • whatever you want.

  • So suppose we has an edge, Vi, that goes to Vj.

  • Well in that case, we know that the number

  • of walks of length 1, from i to j,

  • is exactly 1 because there's this edge.

  • So this is equal to 1.

  • But this is also the definition of my adjacency matrix.

  • So I can just write out here aij 1.

  • So that's great.

  • This case definitely works.

  • Now, if there is no edge of this type,

  • then you know that in one step, we can never achieve Vj from Vi

  • because there's no edge.

  • So we know this is equal to 0.

  • There's no such walk.

  • And this is, by definition of the adjacency matrix,

  • also equal to the aij.

  • So this works.

  • So the base case it's easy.

  • And induction step always starts by issuing pk.

  • As you can see, these types of proofs

  • always have the same structure.

  • In this case, let me again assume pk.

  • We want to prove pk plus 1.

  • So what you want to know is what's

  • the number of walks of length k plus 1.

  • So how can we express those?

  • pij k plus 1.

  • How can we use this assumption over here?

  • Do you have an idea so we can--

  • AUDIENCE: Any walk of length k plus 1

  • can be got by taking one of-- let's say from Vs

  • to Vx. [INAUDIBLE] You go from Vs to V in k steps

  • and then V to Vf in one step.

  • PROFESSOR: That's true.

  • We could do that.

  • So let's write it down.

  • So what you're essentially saying

  • is that you can enumerate all the walks

  • by going of length k plus 1 by first going from i in, say,

  • k steps, to whatever V, and then in one

  • step to, say, Vi to V in k steps and then in one step to Vj.

  • So let's write this down because V can be anything.

  • So what to do is we have a sum over all the Vs

  • such that-- well, we will use indices here

  • let me do that a little bit differently.

  • So let's say I have an h over here.

  • So all the indices h such that Vh to Vj

  • is actually an edge in G.

  • So then we can write out here that we go in k steps to Vh.

  • So how many walks are there?

  • Well, we can use the induction hypothesis now, right?

  • So we can say you go from i to h in k steps.

  • And then, well, we can use this particular edge

  • to complete it to a k plus 1th walk from i to j.

  • So now what is this equal to?

  • Can we simplify this sum?

  • We can, right, because we know that there's

  • an edge if and only if the adjacency matrix has a 1

  • in a particular position.

  • So we could rewrite it and sum over all h from 1 to n.

  • And write pij k times, and then aj-- oh, this should be an h.

  • So we have the same over here.

  • And then we have one edge from h to 2j.

  • So I only count this number over here if this is equal to 1,

  • and that happens exactly if there's an edge.

  • I do not count this if there's a 0 over here,

  • that is as if there's no edge.

  • But now we can use induction hypothesis because I know that

  • these numbers are equal to the a's.

  • So we rewrite this.

  • And we see that we get aih k.

  • So this is where we use the induction step.

  • So it's like we assume pk over here.

  • And I need to finish this formula.

  • So we have this.

  • Now, by the definition of matrix multiplication,

  • we actually see that this is equal to a k plus 1 ij

  • to the ij-th entry in the k plus oneth power

  • of the adjacency matrix.

  • Here we have this represents the matrix of the kth power.

  • This represents the matrix of a.

  • So we multiply essentially a to the power k times a

  • and get a to the power k plus 1.

  • And that's what we see here.

  • So this is the induction step, and so this proves the theorem

  • up here.

  • So let's talk about a few more definitions concerning

  • directed graphs.

  • And then you go a step into a very special type

  • of graph, which are the tournament graphs up here.

  • One of the things that we have in undirected

  • graphs, so where we have no directed edges,

  • we talked last time a lot about cyclicity and stuff like that.

  • And we talked about acyclic connected graphs.

  • And as we categorized those, so we defined them as trees,

  • and they have a very special structure.

  • So what would happen here, if we look at a directed graph,

  • and we wonder, what does it mean to be connected.

  • Can we really talk about that?

  • What does that mean?

  • For example, if I look at this particular example

  • graph over here, I can say, well,

  • V1 has an edge that points towards V1 itself,

  • and towards V2, and towards V3.

  • So maybe I would call this graph connected.

  • But if I look at V2, I only see an edge

  • that goes from V2 to V3, and not to V2.

  • So maybe I do not call it connected.

  • So that's why we define a stronger notion for a digraph.

  • So a digraph, G, VE is called strongly connected

  • if we know that for every pair-- so if for all vertices

  • U and V in the vertex set-- there exists

  • a directed path that starts in U and ends up in V in G.

  • So this is what we would call strongly connected.

  • So now in undirected graphs, we had connectivity

  • and then we said, well, if a connected, undirected graph

  • has no cycles.

  • We have trees and they have special properties

  • and all that.

  • So what about this over here?

  • Suppose you have an acyclic strongly connected digraph.

  • What does that look like?

  • So let's give an example.

  • It's not completely clear what kind of structure it has.

  • So for example, I may have a graph that

  • looks like this, for example.

  • Say this is an acyclic graph.

  • but It does not have at all a tree structure.

  • So actually, the type of graph the we have here

  • is called a directed acyclic graph.

  • As you can see, there are no cycles

  • because I only go forward, essentially.

  • I can never go backward in this particular way

  • that I depicted the graph.

  • So this is an example for directed acyclic graph.

  • But it doesn't look like a tree at all.

  • So it's worth to define it separately,

  • and we will use this on Thursday when we'll

  • talk about partial orderings.

  • And it turns out that, as you can see here-- well,

  • at that case, a directed acyclic graph

  • has really nice properties, and one of them

  • is that you can order these vertices in such a way

  • that you go from, say, left to right in a directed fashion.

  • And that will lead to partial ordering.

  • So that's something that we will talk about as well.

  • So what's the definition?

  • A directed graph is called a directed acyclic graph,

  • and we appreciate this by DAG.

  • We call them DAGs.

  • They're used everywhere, actually.

  • If it does not contain any directed cycles.

  • So these kinds of graphs are used

  • in scheduling and optimization, and we

  • will use them next week in the lecture on partial orderings.

  • Now we have done a lot of definitions

  • concerning directed graphs.

  • So now let's talk about these very special ones, tournament

  • graph, and see whether we can prove a few really nice

  • theorems about them.

  • Let's see whether we can figure that out together.

  • So what is a tournament graph?

  • In a tournament graph, I have a bunch of vertices.

  • And essentially we want to represent like a tournament.

  • So every vertex, say, represents a team.

  • And a team can play against another team,

  • and beat them or lose against the other team.

  • So we want to use the directed edges to indicate

  • who is winning from home.

  • And such types of graphs have very special property.

  • So let me first depict one.

  • So for example, we have E goes to A, goes through B,

  • incoming edge from C, one coming from B.

  • And over here we have this directed edge.

  • We have this one.

  • We have this one.

  • Let me see [INAUDIBLE] don't make any mistakes here.

  • And this one.

  • And over here is another one.

  • So what do we see in this graph?

  • We see that either say, team U, beats team V.

  • And that means that we have a directed edge from U pointing

  • at V. Or it's other way around.

  • V actually beats U, and we have a directed edge from V to U.

  • Let's have a look at this graph and see how this works.

  • So for example, we have that B is beating E,

  • and E is beating A, and so on.

  • So let's have a look.

  • Maybe we can figure out who's the best player in here.

  • So this is sort of a general question

  • if who would like to answer.

  • Maybe you cannot answer this.

  • So let's have an example.

  • Has anybody seen an example where we start with A,

  • then we may beat another vertex, and maybe another vertex,

  • and so on, until we have covered all the different vertices.

  • Do you see a path that works like that?

  • And that could gives us an ordering on who

  • is the best player, like the one at the top,

  • like A, is able to, for example, beat B.

  • And B is, for example, able to beat D and this one, E,

  • and this one, C. So you would say, OK, that's great.

  • Now I know that this one is the strongest player.

  • But there's a little problem here, right?

  • Because I can produce many such paths.

  • And actually, if I look at C, then C beats A as well.

  • So that's kind of weird.

  • So wait a minute.

  • We have that there's a directed edge from C to A.

  • It's like teams beats one another.

  • And it's not very clear how we can talk about a best player.

  • Well, we would have a best player

  • if one player sort of wins from everybody else.

  • But there's many examples here.

  • So let's look at another walk.

  • For example, C can go to B, to D, and then to E, and then

  • to A. So there are many possibilities here.

  • So this leads us to a concept.

  • And we call that's a directed Hamiltonian path.

  • And we're going to show that, in a tournament graph,

  • you can always find such a directed Hamiltonian path.

  • So what's a Hamiltonian path?

  • This is actually an example of it.

  • There's a walk that goes around the graph

  • and visits every vertex exactly once.

  • So we're going to prove that a tournament graph has

  • this beautiful property.

  • So let's first write out a definition of this.

  • A directed Hamiltonian path is a directed walk

  • that visits every vertex exactly once.

  • So as I said already, here we have such an example.

  • We can go from A to B, to D to E, to C. Maybe

  • there are even other examples.

  • I did not actually see them.

  • Maybe you can have a look at them as well.

  • So maybe there's something that starts with B going to E maybe.

  • That's a very different direction, like this.

  • There will be one as well, and so forth.

  • So what's the theorem that you want to prove?

  • The theorem in that you want to show that every tournament

  • graph actually contains such a directed Hamiltonian path.

  • So let's have a look at how we can prove this.

  • What kind of proof technique are we going to use?

  • Do you have an idea?

  • Well, usually we use induction so that's what

  • we're going to do here as well.

  • But what kind of induction hypothesis can we do?

  • So what would we induct on, you think?

  • Someone else?

  • Maybe someone up there?

  • AUDIENCE: The number of nodes.

  • PROFESSOR: The number of nodes.

  • So we use induction on the number of nodes.

  • And why would that be of interest?

  • So let's have a look at how we can think of that.

  • So we start thinking about such a problem, this really

  • sort of-- one parameter here that's the number of nodes

  • in a tournament graph.

  • I also have edges.

  • But if I think about edges, then, the edges

  • is always directly related to the number

  • of nodes in a tournament graph.

  • So I really have just that one parameter.

  • So it makes sense to use induction on number of nodes.

  • So induct on n, where Pn is going to be that every--

  • and essentially, the theorem holds true for a tournament

  • graph on n nodes-- so every tournament graph on n nodes

  • actually contains a directed Hamiltonian path.

  • So this is semi induction hypothesis.

  • So when you think about that, well, I

  • feel pretty confident because if I

  • look at the base case-- that's how we always start-- well,

  • n equals 1.

  • If n equals 1, I have just a single vertex.

  • There's no edges.

  • Everything is fine because the single edge

  • is a directed Hamiltonian path.

  • So this is great.

  • So this works.

  • So what about inductive step?

  • Now with inductive step, we always

  • perceived in the same way.

  • We start to assume that Pn is true.

  • Essentially, the theorem holds for a tournament graph

  • on n vertices.

  • Actually, let me keep this over here.

  • So now let's just think about how we can prove this induction

  • step.

  • I need to prove P of n plus 1.

  • So how do I start?

  • I have to start with a tournament graph

  • on n plus 1 vertices.

  • And somehow I got to be able to use this property

  • because that's what I assume.

  • And the property only holds for a tournament graph

  • on end points.

  • So what will be a really good strategy

  • to do here to sort of proceed our proof?

  • So let me first write out what we want to do here.

  • Maybe you can think about how to advance here.

  • So we have shown Pn.

  • Now we want to actually prove something

  • about tournament graphs on n plus 1 nodes,

  • so let's consider one.

  • Consider a tournament graph on n plus 1 nodes.

  • Now how can I use my induction hypothesis?

  • So I start with this, and I want to use

  • something that talks about the tournament graph on n nodes.

  • So how could I proceed here?

  • Any suggestions?

  • So what do you usually do, right?

  • If I have like an n plus 1 nodes,

  • I have to somehow look at least maybe there

  • exists a subgraph in this bigger graph.

  • So this is really how you always think

  • about these types of proofs or also other problems.

  • So there must be some kind of subgraph

  • that already has this property.

  • Well, let's take out one node and see

  • what happens because then we have one node less

  • and maybe we will be able to apply our induction step.

  • So let's take out one node V.

  • And what can we say about remaining graph

  • if we take out one node?

  • For example, if I take out the node E over here

  • and I just look at all the rest, I

  • can still see that for all the other nodes either,

  • say, U beats V or V beats U. So I still

  • have an edge in one direction between each two nodes.

  • So actually I still have a tournament graph,

  • so that's great.

  • So this gives a tournament graph.

  • So essentially, so far, we really

  • haven't done anything creative or anything

  • that we had to make a big leap in order to prove this theorem.

  • We started out with, if you want to prove something

  • like this you have to really look at the number of vertices.

  • And then we start to write down this stuff over here

  • that makes total sense.

  • And then we are going to figure out where

  • we can use this induction step.

  • And so we just take out one node.

  • And yes, it is a tournament graph on n nodes.

  • So this is very systematic.

  • That's what I try to get at here.

  • So by the induction step because by the induction hypothesis,

  • we know now that we actually have a directed Hamiltonian

  • path.

  • So let V1 to V2 to Vn be such a path.

  • So now that we can use this, we apply it and we get a path.

  • Now what do we want to do?

  • We want to show that we can create a new path, which

  • is also a directed Hamiltonian path, but now one that also

  • includes V in the bigger graph.

  • If we can do that, we are done.

  • So now we have to start really looking

  • at how we can make that happen.

  • In order to do this, we have to see how we can somehow

  • plug V, the vertex that we have removed,

  • in this path over here.

  • If you can do this, we are in really good shape.

  • So far, we haven't used at all-- so there's also something

  • that you can look at if you start

  • solving these types of problems-- we haven't used

  • at all the property that the tournament graph has,

  • which I just wiped out.

  • So let's figure out what we can do here.

  • We should be able to use something.

  • So of course, we have a few simple cases.

  • For example, if V has a directed edge into V1,

  • and I have a Hamiltonian path that goes from V to V1 to V2

  • all the way to Vn, and then I cover all the vertices exactly

  • once.

  • And I will have a direct Hamiltonian path.

  • So this is great.

  • So this is definitely easy.

  • So this is case one.

  • In case two, suppose that V1 has a directed edge to V.

  • And So now we have a little problem because somehow there

  • is an edge like this to V, but they cannot go like this.

  • This is not a Hamiltonian path.

  • I need to have a directed walk that covers

  • all the vertices exactly once.

  • So now we have a little problem.

  • So now we have to start really thinking

  • about how we can solve this.

  • So are there any suggestions to make this happen?

  • So let's think a little bit about this.

  • So somehow if I start thinking about this,

  • I would like to plug V somewhere in this sequence.

  • That will be like a pretty obvious way to-- go ahead.

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: Yeah.

  • That's true.

  • So for example, I may have that for example,

  • V2 beats V as well.

  • But suppose that I have V3 over here, and this one beats V3.

  • Then, as you say, I could sort of plug V in here

  • in this sequence and I may have a longer sequence.

  • Or if this is not the case, then maybe the next one, V4,

  • may have the property and I can plug V in here.

  • So essentially what I want to show is that I can plug V

  • somewhere in the middle of two of these Vi's.

  • And the property that you were using

  • is actually that you said, well, I know that V2 either

  • beats V, or V beats V2.

  • So that's the property of the tournament graph

  • that we're going to use here in order to prove this.

  • So yes, that's a great observation.

  • So the way you formulated it, we can maybe use induction,

  • for example, to prove this.

  • We can sort of go recursively through this

  • until you find the right spot.

  • Maybe they can immediately precisely indicate such a spot.

  • So how do we usually do that?

  • If we are thinking about other theorems

  • that we have tried to prove.

  • AUDIENCE: What's the smallest value of i

  • such that V beats Vi?

  • PROFESSOR: OK.

  • So what's the smallest value of i where V beats Vi.

  • So usually we have words in our mind like largest or smallest,

  • et cetera, and sort of an extreme precision.

  • And then we like to find out that we can make it happen.

  • And then we say, well, if something goes wrong,

  • we violate that smallest condition.

  • So let's do that here.

  • So let's indicate a specific spot.

  • Let's considered the smallest i such that V beats Vi.

  • Well, let's have a look how this would work.

  • So this is a little bit of a different proof

  • than what I had but this should work fine as well.

  • So let's just see how it works.

  • So we have V1.

  • First of all, we notice that, of course, if i equals 1,

  • which that cannot be the case, so we know that i is larger

  • than 1.

  • So there's really somewhere a Vi minus 1--

  • we have to check that, right?

  • If that exists, this index is not

  • equal to zero-- that goes to Vi and then goes all the way up

  • to Vn.

  • So now we say we can use this, that V actually beats Vi.

  • Now what's about-- maybe someone else can help me here--

  • I would like to have that Vi minus 1

  • beats V. That would be fantastic because then I have a path that

  • goes from V1 all the way up here,

  • goes here, goes there, and all the way up to here.

  • So we have a directed Hamiltonian path

  • that covers all the vertices exactly once,

  • and that's what you want to prove.

  • But why would there be an edge that goes this way?

  • So how do we reason about this?

  • Someone else?

  • AUDIENCE: Because Vi is the smallest number that V beats,

  • then anything smaller than i must have beat V.

  • PROFESSOR: Exactly.

  • So if V would beat Vi minus 1, that

  • would contradict the smallest property over here.

  • So that's a contradiction.

  • Now we use a property of the tournament graph.

  • So now I know that Vi minus 1 must beat V.

  • So I really have an edge over here.

  • So that works.

  • So that's the end of the proof.

  • Now another version could have been

  • in which we would do something similar like this.

  • But we could also have used, say,

  • the largest i-- just something that you may want to look at.

  • You can also use the largest i such

  • that Vi beats V. We have a completely symmetrical argument

  • here, but you could use this solution as well.

  • So I'm just trying to sketch here

  • the way of thinking that you may want to consider

  • in these types of problems.

  • So why would this work, by the way?

  • Well, we have the same kind of argument like this.

  • We plug V right after Vi.

  • We know there's an edge from V to Vi plus 1.

  • Why?

  • If it's not the case, there will be a large index, i,

  • that contradicts our assumption that we

  • have the largest i already.

  • It's a tournament graph, so we know that there's

  • an edge from V to Vi plus 1.

  • And we get a directed Hamiltonian path as well.

  • So you may want to look at that as well.

  • So this is about tournament graphs.

  • So let's talk about an interesting tournament graph

  • with a funny game.

  • And this is actually a chicken tournament,

  • like the chickens here represent the vertices

  • and they are pecking one another, but in a certain rule

  • that defines a chicken to be the king chicken.

  • So let's see how that works.

  • So that's a great application of graph theory.

  • So what do we have?

  • We have that either a chicken, U, pecks a chicken, V.

  • And we said that U has a direct edge to V,

  • so we're actually defining a tournament graph here.

  • Or we have a chicken, V, that pecks a chicken, U,

  • and we get V has a direct edge to U.

  • So we have a tournament graph.

  • But now we define something new.

  • We say that U virtually pecks V if one of the two conditions

  • holds-- one of these two conditions-- either U,

  • of course, pecks V. That's great.

  • He's in good shape.

  • Or there exists another chicken, W, such

  • that U actually pecks W and W, in turn,

  • pecks V. So this is very special kind of first relationship.

  • So we are wondering now can we now define something--

  • is there a question?

  • AUDIENCE: [INAUDIBLE] in between?

  • To be virtually pecked, is one chicken

  • in between U and the other one?

  • PROFESSOR: Well, there can be multiple chickens

  • in between here.

  • I have several friends who help me out pecking someone else.

  • So when we were looking at these tournament graphs,

  • we were wondering, can we really indicate a winning player?

  • Well in this case, if you start to talk about virtual pecking,

  • we look at the pecking order.

  • Then we can actually define something like a chicken king.

  • So let me write that down.

  • Let me first explain what I mean here.

  • And I give an example of a graph.

  • So a chicken that is able to virtually peck everyone

  • else, we will call a king.

  • So chicken that virtual pecks every other chicken

  • is called a king chicken.

  • So let's give an example of a graph.

  • So, for example, suppose I have four chickens that

  • know how to pick one another in this order.

  • So who in the pecking party here is going to be king?

  • Do you see some solutions here?

  • So take, for example, this one.

  • So this one pecks this one.

  • Because we talk about virtually pecking,

  • it can also peck this one over here.

  • It does, right?

  • It pecks this one and this one helps out, and can peck

  • both this one and this one.

  • That's cool.

  • So this one is king.

  • And this one, actually-- let's have a look.

  • It pecks this one this one, and it virtually

  • also pecked this one.

  • Yay.

  • He has a friend over here that is doing that for him.

  • So this one is actually also a king.

  • So you can have multiple king chickens in here.

  • What about this one?

  • The same story-- pecks this one, pecks this one,

  • and virtually-- wait a minute.

  • The one on the left-- oh yeah, over here.

  • This, this.

  • So this one is king as well.

  • Now what about this one?

  • Well, it can peck this one.

  • And then in one more step from here,

  • because there's only one outgoing edge,

  • it can virtually peck this one, but not this one.

  • So this one is definitely the loser of the four.

  • So he's not the king.

  • So now what we want to prove this a theorem

  • sort of trans-identify one of the chickens

  • that we know for sure is going to be king.

  • So you can have multiple kings.

  • But maybe there's one chicken that, from our intuition,

  • we may feel is definitely going to be king.

  • So what will be a good intuition?

  • So we're talking here about virtual pecking.

  • We have this definition.

  • So what kind of node in a tournament graph

  • essentially would be-- can we know for sure that it's a king?

  • Do you have an intuition for a theorem

  • that you may want to prove?

  • So that's often what we do in mathematics.

  • We have some kind of funny new structure,

  • and then we want to find out whether we

  • can prove interesting properties about it.

  • So we start to search for actual nice properties and theorems.

  • So in this case, it makes sense that the vertex

  • that has the most outgoing edges may be always king.

  • Can we prove this?

  • So that's what we're going to do.

  • So that's the theorem.

  • And let's see whether we can do this in an elegant way.

  • So the theorem is that even though there

  • are multiple kings-- as indicated

  • in that particular example, that may happen--

  • but I certainly know that the chicken that has the highest

  • outdegree is definitely a king.

  • And the way we're going to prove this is by contradiction.

  • Let's assume that's not the case.

  • That must be really, really weird.

  • If you are the one who has the largest outdegree,

  • it means that you can directly, just by yourself,

  • peck the most others.

  • So suppose you're not king.

  • So by contradiction, first of all,

  • let U have the highest outdegree.

  • And we want to show that this U is king.

  • So let's assume the contrary.

  • So let's suppose that U is not king.

  • So what does that mean?

  • So let's look at a definition over there

  • and see what it means that U is not king.

  • So that means that both those conditions are violated

  • because if one of those two holds,

  • I know there must be one vertex, V,

  • such that U does not virtually peck V. So I know that.

  • So let's see what that implies.

  • So I know that there must be a V such

  • that U does not virtually peck V. So maybe can help me out.

  • What does that mean?

  • It means that both these conditions are not true.

  • So let's look at the first.

  • So U pecks V. If that's not true,

  • and we're in the tournament graph,

  • we know that V must peck U. So we have this.

  • and we also know that the other condition, the second one

  • over here, does not hold.

  • So what's the negation of this second condition?

  • Maybe you can help me out.

  • So we have here, there exists a W such that U pecks

  • W and W pecks V. So how do we negate that logical expression?

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: For all W, what's over there is not through true.

  • So can we formulate that a little bit better?

  • So it's not true that U pecks W and W pecks V. So that

  • means that either U pecks W is not true,

  • or W pecks V is not true.

  • So let's write that down.

  • Let's just write it all out.

  • So not U pecks W or not W pecks V. Well,

  • how can we rewrite this?

  • Well, it's a tournament graph, so we

  • know that W pecks U. Or this particular condition, which

  • is V pecks W.

  • So what do I have here?

  • Is this going in the right direction?

  • Well, I want to prove something about-- if I use contradiction

  • and I suppose that U is not the king,

  • I've assumed that U has the highest outdegree.

  • So I want to show that I somehow violated.

  • So somehow I'm able to construct some vertex, V.

  • And by negating that U is not a king

  • it seems that this vertex, V, makes a really good candidate

  • to show that there's a higher degree outdegree than U.

  • So let's see whether we can do this.

  • We can rewrite this logical expression once more.

  • We can also say that, well, if U pecks W-- so this

  • is not true-- then it must be true

  • that this one holds because if that's not the case,

  • then this condition is not true.

  • So if U pecks W, then this is not true.

  • So then it must be the case that that is true.

  • So V pecks W. So now let's have a look at V.

  • We noticed that for all outgoing edges from U,

  • there exists a similar outgoing edge for V.

  • But V has one more outgoing edge.

  • V, actually, is an outgoing edge to U.

  • So what do we see here?

  • That the outdegree of V is actually at least the outdegree

  • of U-- which is this particular condition over here

  • that we show here, for all the W we

  • have that this is true-- plus and we have an extra one.

  • It's this one.

  • Oh, but now we have a contradiction

  • because we said that U has the highest degree.

  • But it turns out that you have constructed one

  • that has a higher outdegree.

  • So that's a contradiction.

  • That means that our original assumption is actually wrong.

  • So suppose that U is not the king was a wrong assumption,

  • and U must be king.

  • So that's the end of this proof.

  • This is the end of this lecture.

  • So see you tomorrow at recitation

  • and next week we will continue with communication graphs

  • and partial orderings.

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Lec 10|MIT 6.042J コンピュータサイエンスのための数学 2010年秋学期 (Lec 10 | MIT 6.042J Mathematics for Computer Science, Fall 2010)

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    Dou Lin に公開 2021 年 01 月 14 日
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