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  • PROFESSOR: So welcome to this week.

  • We are going to talk about number theory.

  • Actually, before I forget, there are

  • some handouts at the very back.

  • Please raise you hand if you don't have any, then one of us

  • can actually come over and hand you

  • out this sheet, which contain some facts

  • about the visibility.

  • Thanks a lot.

  • And we will be using these throughout the lecture.

  • So today we're going to talk about number theory.

  • And this is a really different way of thinking, actually.

  • But we will use the same concepts

  • as you have learned before, like induction, and invariance,

  • stuff like that, to prove whole theorems.

  • So what is number theory?

  • Well, first of all, it's a very old science.

  • One of the oldest mathematical disciplines.

  • And only recently it actually got

  • to have some more practical applications.

  • So what this number theory-- it's actually

  • the study of the integers.

  • And what are the integers?

  • Well, these are the numbers 0, 1, 2 3, and so on.

  • So number theory got-- Oh, there's some more over here.

  • Another handout over there.

  • So number theory got used actually in cryptography

  • only about 40 years ago.

  • And at the end of the second lecture,

  • we will be talking about this application into cryptography.

  • There are many application in cryptography.

  • But we'll be talking about one of them

  • to show you how useful this actually is.

  • Now cryptography is the study and practice of hiding numbers.

  • So you can imagine how important that is.

  • We have like medical data that we need

  • to store outside in the cloud.

  • Right?

  • So, gee.

  • Do we really want that?

  • We actually want to hide our information.

  • We do not want others who are not

  • allowed to see my private information to see it.

  • So this art of hiding information

  • is extremely important, especially nowadays.

  • And number theory actually will help us with this.

  • So number theory is something, you'll be very surprised,

  • that can be used to save-- oops.

  • I have to put this on.

  • To save New York City in the Die Hard number 3, I believe.

  • So let me start up again.

  • So let's see where it plays.

  • Maybe not.

  • [VIDEO PLAYBACK]

  • -Yeah, go ahead and grab it.

  • -You're the cop.

  • -Simon said you're supposed to be helping with this.

  • -I'm helping.

  • -Well, when you going to start helping?

  • -After you get the bomb.

  • Careful.

  • -You be careful.

  • -Don't open it.

  • -What?

  • I got to open it.

  • And it's going to be all right.

  • [BEEPING]

  • [ELECTRONIC CHIRPING]

  • Shit.

  • -Shit!

  • I told you not to open it.

  • [PHONE RINGING]

  • [PHONE RINGING]

  • -I thought you'd see the message.

  • It has a proximity circuit, so please don't run.

  • -Yeah, I got it.

  • We're not going to run.

  • How do we turn this thing off?

  • -On the front there should be two jugs.

  • Do you see them?

  • A give gallon, and a three gallon.

  • Fill on of the jugs with exactly four gallons of water

  • and place it on the scale, and the timer will stop.

  • You must be precise.

  • One ounce or lower less will result in demolition.

  • If you're still alive in five minutes, we'll speak again.

  • -Wait!

  • Wait a sec.

  • I don't get it.

  • You get it?

  • -No.

  • -Get the jugs.

  • Obviously, we can't fill the three gallon jug will

  • four gallons of water, right?

  • -Obviously.

  • -I know.

  • There we go.

  • We fill the three gallon jug exactly to the top, right?

  • -Uh-huh.

  • -OK.

  • Now we pour that three gallons into the five gallon jugs,

  • giving us exactly 3 gallons in the five gallon jug, right?

  • -Right.

  • Then what?

  • -Now, we take the three gallon jug,

  • fill it a third of the way up--

  • -No, no.

  • He said be precise.

  • Exactly four gallons.

  • -Every cop in 50 miles is running his ass off,

  • and I'm out here playing kids games in a park.

  • -Hey.

  • You want to focus on the problem at hand?

  • [END PLAYBACK]

  • [LAUGHING]

  • PROFESSOR: All right.

  • You can imagine what we are going to do right here, right?

  • So.

  • You can imagine what's below this table is a bomb.

  • [LAUGHING]

  • You guys have to save 6042.

  • [LAUGHING]

  • So we have the fountain here.

  • Each tennis ball is one gallon of water.

  • We have a big jug, five gallons and three gallons.

  • So you all got to help me out here.

  • So who has an idea of what we can do?

  • So.

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: All right.

  • Let's first do that.

  • Fill up the three gallons.

  • AUDIENCE: And pout it into the five.

  • PROFESSOR: Let's pour it into five.

  • Maybe someone else can-- can continue.

  • Over there.

  • AUDIENCE: If we do the same again,

  • we'll end up with just one gallon in the three gallon.

  • PROFESSOR: Uh-huh.

  • So, let's do that.

  • Because that's true, right.

  • You can only fill it up to five gallons.

  • So only, at more, two gallons can add

  • to this, exactly two gallons.

  • And one gallon is left.

  • All right, next one.

  • You?

  • Would you like to--

  • AUDIENCE: Take out the five.

  • PROFESSOR: Take out the five.

  • All right.

  • And then what?

  • AUDIENCE: Pour the one over there.

  • PROFESSOR: Pour the one over here?

  • AUDIENCE: [INAUDIBLE]

  • AUDIENCE: Then fill the three gallon,

  • and put it into the five.

  • PROFESSOR: All right.

  • That's great.

  • And I fill it up right here.

  • Fantastic.

  • So we actually have four gallons here.

  • And luckily, they are safe.

  • Right?

  • So you say, thank god.

  • 6042

  • So we can continue.

  • So this is actually pretty amazing, though.

  • How can we get four gallon out of three gallon jug,

  • and a five gallon jug?

  • And that's what we are going to talk about in more

  • generality, actually.

  • And if you would just change it a little bit, right?

  • Then things would get more difficult. For example,

  • if you would change the five gallon jug into a six gallon

  • jug, can we still get four gallons?

  • No.

  • Why not?

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: Everything has to be multiples of three.

  • That's exactly right.

  • This is a multiple of 3.

  • 1 times 3.

  • This is 2 times 3.

  • So if I do combinations with those,

  • like pouring one into the other completely, or emptying,

  • of filling up, we always will have

  • a multiple of three gallons in either one of those, or both.

  • So we can never have four gallons.

  • So this is something that we would like

  • to analyze a little bit more.

  • And to do that, we're going to first a all

  • start with a definition.

  • Actually you can put up the screen over here.

  • So let me take that out.

  • Can someone up there pull up the screen?

  • Maybe not?

  • Maybe later.

  • All right.

  • So let's go with a definition.

  • We say n denote by m and a bar, and a, we mean m defines a.

  • And how do you define this?

  • Well, we say that n defines a, if and only

  • if there exists an integer k, such that a can

  • be written as some multiple m, mainly k times m.

  • So if you look at this definition

  • we, for example, have that 3 divides 6,

  • like what we just discussed.

  • There's something interesting going on.

  • Suppose a is equal to 0.

  • Well, any integer will define a, will define 0.

  • Why is that?

  • Because I can't take k to be equal to 0,

  • so this is equal to 0 times any integer m.

  • So m defines 0 for all integers.

  • So this is kind of the exception, right?

  • And we are going to use this to set up a theorem,

  • and analyze this whole situation over here.

  • Now in order to do that, we will need to sort of define

  • what we can do with all this.

  • So there are states.

  • We will define a state machine.

  • We will see what kind of possible transitions

  • we can have.

  • And once we have modeled all this very precisely,

  • we can start proofing stuff.

  • Now let me first of all write out what our assumptions are.

  • So suppose we have an a gallon jug.

  • So in our case, a equals 3.

  • And we have also b gallon jug.

  • And in our case, b equals 5, right?

  • And we issue that a is at most b.

  • That is sort of the situation that we are working with.

  • And he would like to prove a theorem.

  • Exactly what we notice over here, that three defines both.

  • The three gallon jug, and the six gallon jug,

  • we would like to prove something like this.

  • If m defines a, and also m defines be,

  • well, then m should define any results

  • that I can get with the pouring, and emptying and filling

  • those jugs.

  • So this is the theorem, if you'd like to prove.

  • And we can only do that if you start to have

  • a proper model for this.

  • So let's go for that.

  • And--

  • And, well, the state machine that we're going to use here

  • looks like this.

  • First of all, the states that we have

  • are the number of gallons that are in these two jugs.

  • So we will denote those by pairs.

  • Pairs x, comma y.

  • And x denotes the number of gallons in the a gallon jug.

  • The number of gallons m m that we abbreviate as by the a jug,

  • and y is the number of gallons in the b jug.

  • So these are the states.

  • And the start state it exactly as it is right there.

  • We have nothing in either of the jugs.

  • So that's the pair 0, comma 0.

  • So now we start to build up some mathematics here, right?

  • So we express the state of this whole situation

  • by a pair of number.

  • Now we need to find out what they can do with it.

  • So what are the transitions?

  • The transitions are, as we have seen, right?

  • We can just fill one of the jugs.

  • We can empty those.

  • And the other possibility is that we

  • can pour one jug over into the other one as much as we can.

  • So let's write all of those out.

  • We can do emptying.

  • Well, how does that change the state?

  • If we have x gallons in this jug,

  • and y-- and y gallons in that one,

  • we can transition this into, for example,

  • emptying the a gallon jug.

  • So be y of 0.

  • Or we can empty the b jug.

  • Well, filling is something similar.

  • But now we are actually pouring more water from the fountain,

  • essentially.

  • Right?

  • All those tennis balls here.

  • And we can fill up say the a gallon up to a gallons,

  • and leave the b jug as it is.

  • Or w we can fill up the b gallon jug, and leave the a gallon jug

  • as it is.

  • So these are these two transitions.

  • And the pouring of one-- of one jug into the other

  • is actually a little bit more complex.

  • So let's have a look.

  • So how does pouring work?

  • Well, suppose we start with x and y.

  • So let's have a look here.

  • Um, I don't know.

  • Suppose we have 2 balls in here, and 2 balls in here.

  • Well, in that case, I can say pour all of these over in here.

  • Right?

  • So that's easy.

  • But there's also another possibility, better

  • when I pour all of these over in here.

  • But hey, I can only put in 1 ball,

  • because it's only a three gallon jug.

  • So I'm left with only 1.

  • A gallon in this jug.

  • So these are two-- these are two situations that we

  • need to explain.

  • So let's first do the first example that I just did.

  • I pour everything over into the other jug.

  • So we have 0 gallons left in here,

  • and x plus y gallons left in the other jug.

  • And this can happen if there's sufficient space, right?

  • So this can only happen if x plus y is at most b.

  • Which is the capacity of this b gallon jug.

  • Now if that's not the case, then I

  • can pour in just a little bit, like just say 1 ball.

  • Like just one of these can go in here.

  • So that's the other case.

  • So x, y we'll actually go to-- well,

  • let's just see how this works.

  • How many gallons are left in this b gallon jug to fill up?

  • Well, we have b minus y gallons left, right?

  • Space left.

  • So we can take b minus y gallons out of this one

  • to fill up this one.

  • So let's do it.

  • We take b minus y gets out of the a jug,

  • and put it all in here, and it makes it completely filled up.

  • So we have b gallons over here.

  • So this is really equal to x plus y minus b, comma b.

  • And this only is possible if-- if you are essentially

  • in the complimentary case.

  • So we have that x plus y is at least b,

  • such that there is enough gallons in the a jug

  • to be poured over to fill up the b jug.

  • So these are the two kinds of cases.

  • And, of course, by symmetry we can

  • do also the pouring from the other jug into the first.

  • So let's write all those out, as well.

  • So x, y can actually go to x plus y, comma 0.

  • I pour everything from here to there.

  • And this only holds if x plus y is at most a.

  • The other possibility is where, exactly as in this case,

  • we can only pour a minus x gallons over from y

  • into this particular jug.

  • And then this one is completely filled up.

  • And then I have a few gallons left over here.

  • So how does that look?

  • Well, we completely fill this up to its capacity.

  • And what is left this is y minus-- how much did

  • we have to pour in here?

  • Well, that's a minus x.

  • And we again have a similar formula.

  • But it now looks a little bit different.

  • X plus y minus a.

  • And this is only for the case where x plus y is at least a.

  • OK.

  • So these are all the cases.

  • So maybe there are some questions about this.

  • Is this clear, that we have these different possibilities?

  • Like when we look at these jugs we can either

  • empty them, filling them up.

  • Or we can pour say only 1 ball over up

  • to the full capacity of this jug.

  • Or we can just pour everything over into, say, this jug.

  • So those are the different cases that are now fully described

  • by this state machine.

  • So now we can start to prove this theorem over here.

  • So how do we go ahead?

  • How are we going to use what you've learned like induction,

  • and invariance?

  • So let's do it.

  • But before, actually, we do this,

  • let's take this example that we had

  • and see how we can describe all the transitions that we just

  • did, as far as I remember them.

  • So we have that a equals 3, b equals 5.

  • Right?

  • We start with empty jugs.

  • We need to filled up the five gallon jug, right?

  • Then we started pouring the five gallon jug as much

  • as we could into the three gallon jug.

  • So it's one of those rules.

  • We've got 3 into 2.

  • Then we emptied the three gallon jug.

  • We got 0 and 2.

  • Then we did-- What did we did next?

  • Oh yeah, we poured everything into this one.

  • So we have 2, 0 as the next state.

  • We filled up-- actually I forgot exactly what we did next.

  • But I think we filled up the five gallon jug.

  • And then we simply poured over as much

  • as he could from the five gallon jug.

  • And we got 3 and 4, and here we are.

  • We got 4 gallons.

  • So what we just did is fully describe this state machine.

  • So let's not try to prove this theorem.

  • So as I said, we're going to use induction.

  • So you always would like to write this out

  • if you solve your problems.

  • What are we going to assume?

  • Well, we assume actually that m defines a,

  • and m defines also b.

  • That's the assumption of the theorem,

  • and now we need to prove that defies any result that you can

  • achieve in this state machine.

  • So what's the invariance that we are thinking about?

  • Invariance is going to be--

  • Oops.

  • It's a predicate.

  • And it says something like, if the state xy--

  • if this is the state after n transitions--

  • Then we would like to conclude that m the fights both x,

  • and m defines y.

  • So this is our-- our invariance.

  • And we like to use this to prove our theorem.

  • So how do we start usually, right?

  • So we always start with-- with a base state.

  • Great.

  • So let's do it.

  • The base case is-- well, we start with the all 0s,

  • like the empty jugs.

  • It's-- well, and we also-- have paid a little bit of extra

  • attention to what we mean by division over here.

  • We said that all integers actually divide 0.

  • So in particular, m. m divides 0.

  • m, 0.

  • So the very initial state, 0 comma 0,

  • is indeed complying to is particular invariant.

  • So let's write it out.

  • So we have the initial state 0, 0.

  • We know that m divides 0.

  • And therefore, we know that p 0 is true.

  • So that's great.

  • So the inductive step.

  • How do we start the inductive step-- step all the time?

  • And we will assume, actually, p of n, right?

  • So lets assume that.

  • And now we would like to prove p, and then n plus 1.

  • So what do we really want to do?

  • We want to say, well, we know that we reached a certain state

  • x comma y, for which m divides x, and m divides y.

  • Now we would like to show that if we transition

  • to a next state, we again have that same property, that m

  • divides the number of gallons in both jugs once more.

  • And then we can con-- can conclude p, n plus 1.

  • So that's how we always proceed.

  • So let's see where we can write it out

  • in a bit more formal way.

  • OK.

  • So how do we go ahead?

  • Suppose that x, y is the state after n transitions.

  • Well, what can we conclude?

  • Well, we have the predicate pn, the invariant.

  • So we know that n divides x, and n divides y.

  • And we concluded that because pn is true.

  • So after another transition-- what

  • happens after another transition?

  • So we can conclude that the jugs are

  • filled by the different types of numbers

  • that we see here this is state machine.

  • So let's write them out.

  • So after another transition, um, each of the jugs

  • is actually filled-- Um, are filled with-- well,

  • either if I've emptied it, say a 0, 0 gallons, a, b, x and y.

  • I see appearing over here.

  • And I also notice that I see x plus y.

  • And x plus y, minus b.

  • And x plus y, minus a.

  • Those are all the different number

  • of gallons that can be in jug.

  • Yes, please?

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: In our example-- Yeah, that's a good question.

  • So in our example problems of 3 and 5,

  • it turns out that the only number that

  • divides 5 both the three gallon jug, and the five gallon jugs

  • is actually one.

  • So in our example, we would have that m equals 1.

  • So over here we have that only 1 divides a,

  • as well as 1 divides b.

  • So m equals 1 in our case.

  • But for example, in the three gallon jug, and the six

  • gallon jug-- Right?

  • We have that m equals 3, like 3 divides 3, And 3 divides 6.

  • So those are the two cases that you sort of look at right now.

  • But you put into a much more general setting, right,

  • we are distracted away from the actual numbers.

  • And use a and b as representations.

  • Are any other questions?

  • So after another transition, each of the jugs

  • are filled with, well, either 0 gallons, if we

  • have a completely emptied them.

  • Or we have filled the first a gallon jug, or it can be b.

  • We also noticed that it can be-- it can be of x, of course.

  • It can be y, because that's the state that we are in.

  • And we can have x plus y, minus a, which appears over here.

  • And x plus y, minus b.

  • So these are all the different number-- possible number

  • of gallons.

  • The x plus y.

  • That's also present.

  • Is that true?

  • Yeah.

  • That's right. x plus y.

  • So we also have x plus y.

  • Actually, it's good to check that again.

  • So we have 0, x, y, a, b.

  • Got those.

  • X plus y, and x plus y, minus p, and x plus y minus b.

  • Yeah.

  • So now we can start using our-- our assumptions.

  • So what our they?

  • We have that in order to prove this-- right?

  • At the top over here, we assume that m divides, and m divides

  • b.

  • So we know that first of all, m divides 0, of course.

  • But we know that m divides a.

  • We know that m divides b.

  • We have concluded that m divides x.

  • And also m divides y.

  • So if you now use some facts about divisibility

  • on your handout, which we will not prove now.

  • But I think most of them will be on your problem set, actually.

  • We can conclude that also linear combination of a, b x and y

  • will be divisible by m.

  • In particular, m will divide x plus y.

  • m will divide x plus y minus a, and also x plus y, minus b.

  • So we will conclude that m actually

  • divides any possible results.

  • So divides any of the above.

  • And now we're done.

  • Why is that?

  • Because we have shown now that after the next transition--

  • after we have reached x, y after n steps,

  • then in our n plus 1-th step, all that you can achieve

  • is divisible by m.

  • So that's exactly the invariance.

  • So we conclude that p, n plus 1 is true.

  • And so now we're done.

  • Are any questions about this proof?

  • So this is like the standard technique

  • that we tried to use all the time here in this class.

  • We will use it in all the other areas, as well.

  • In graph theory, in particular.

  • And especially in number theory, will also use it,

  • especially in this class.

  • OK.

  • So let's apply this to theorem.

  • Let's I think about this movie that we saw,

  • this Die Hard number 3.

  • Die Hard number 4 came out.

  • And then the cast got stuck in Die Hard number 5.

  • There's was a problem, because the rumors

  • were that in Die Hard number 5, they had like a 33 gallon jug.

  • That's a lot.

  • And a 55 gallon jug.

  • So Bruce has in training his muscles,

  • because you can imagine those are pretty heavy.

  • So if you want the pour one into the other, my goodness.

  • So-- but the question is, is he training the right muscles?

  • So can we apply this theorem now, and showed that--

  • Oh, I should to tell you what is the problem.

  • Well, again, he has to get say 4 gallons out

  • of this-- out of these two jugs.

  • So is that possible?

  • It's not.

  • I see someone shaking his head.

  • Do you want to explain why?

  • AUDIENCE: A and b are both divisible by 11.

  • PROFESSOR: Yeah.

  • AUDIENCE: So any other configuration will also

  • have to be divisible by 11.

  • And 4 is not divisible by 11.

  • PROFESSOR: Exactly.

  • 4 is not divisible by 11, so the whole cast got blown up

  • in Die Hard number 5.

  • And so we have no Die Hard number 6, as well.

  • OK, so-- so now all of this stuff

  • actually helps us to define a new concept, as well.

  • So let's do that.

  • I'll put it up here.

  • We will use the terminology GCD of a and b

  • as being the greatest common divisor of a and b.

  • So, for example, if we are looking at a equals 3,

  • and b equals 5, well then the GCD of 3 and 5

  • is actually equal to 1.

  • There's no other larger integer that divides both 3 and 5.

  • In other examples are, for example, if we have the GCD of

  • say 52 and 44.

  • Well, what's this equal to?

  • Well, this actually is 4 times 13.

  • This is 4 times 11.

  • So 4 divides both this, and both this one.

  • But nothing larger can divide both of those.

  • So we have that this is equal to 4.

  • We will have a separate definition

  • that talks about this very special case where

  • two numbers-- if you look at their greatest common divisor--

  • when that greatest common divisor is equal to 1,

  • we actually define those two numbers to be relatively

  • prime to one another.

  • So let's put that out over here.

  • So that's another definition.

  • We say that a and b are relatively

  • prime if the greatest common divisor is actually equal to 1.

  • Now today we will not really use his definition so much,

  • but it's actually very important.

  • And we'll come back to this next lecture.

  • So if we now look at this particular thing them

  • over here, can we see a nice corollary of this?

  • Like a result, if you think about this greatest

  • common divisor.

  • Well, the greatest common divisor off a an b

  • divides both a and b.

  • So the greatest common divisor of a and b

  • will divide any result that we can generate by playing

  • this game with the jugs.

  • So the corollary here is that the GCD of a and b

  • divides any result.

  • OK, so that's really cool.

  • So this already tells us quite a bit about this game

  • that we have here.

  • So now what we would like to do is to find out

  • what exactly we can be reached?

  • We have a property that we have shown here.

  • But what else can we do here?

  • Now it turns out that you can say much more,

  • and we would like to prove the following theorem

  • to make-- to analyze this whole thing much better.

  • I don't think I need the state machine anymore.

  • So let's take that off.

  • The theorem that we would like to prove

  • is that any linear combination of the-- let's

  • change this into the 3 and 5 again.

  • Any linear combination of 3 and 5,

  • I can make with these 3 and the 5 a gallon jug.

  • So let's write it out.

  • So any linear combination l, which

  • we writes as some integer s times a, plus some integer

  • t times b.

  • So any linear combination of a and b, with-- well, of course,

  • the number of gallons should fit the largest the jug.

  • So with 0 is, at most l.

  • Is it mostly can be reached.

  • So this theorem we would like to prove now.

  • And in order to do that, we would

  • like to already think about some kind of a property

  • that we have.

  • So when we talk about linear combinations, the s and the t

  • can be negative, or positive.

  • We really don't care.

  • So for example, we could have like,

  • I don't know, minus 2 times-- so for example,

  • 4 is equal to minus 2, times 3, plus-- actually, is that true?

  • Yeah.

  • Plus 2, times 5.

  • So here we have s to be equal to minus 2, and t is equal to 2.

  • And of course, a is equal to 3, right?

  • And be is equal to 5.

  • So 4 is a linear combination of these two.

  • And according to the theorem, we can create that number

  • of gallons in this jug.

  • And we already saw that, because we did it.

  • But for our theorem, in order to prove this,

  • we really would like s to be positive.

  • So how can we do that?

  • If anybody has an indea what we could do?

  • AUDIENCE: Let's assume that b is greater than m.

  • PROFESSOR: Yeah.

  • We have still that a is supposed to be-- We will assume that

  • throughout the whole lecture.

  • Thanks.

  • So in order to prove this, we really

  • would like to have s to be positive.

  • So let's just play around a little bit

  • with linear combinations to get a little bit

  • of feeling for that.

  • How could we write 4 differently,

  • as a linear combination of 3 and 5,

  • such that we have actually a positive number over here?

  • Does anybody see another way to see that?

  • AUDIENCE: [INAUDIBLE]

  • PROFESSOR: Yeah, that's true.

  • 3 times 3, minus-- minus 5.

  • So-- and how did we do that?

  • Well, we can just say 5 times 3 to this one,

  • and then subtract the same again, minus 3 times 5,

  • over here.

  • And if he adds those things together,

  • he will see 5 minus 2, is 3 times 3, as you said.

  • And we have minus 3 plus 2 is actually minus 1 times 5.

  • And this will be a different linear combination of 4.

  • So what we can do here, we can sort of play around and make

  • this s over here, which we now say call s prime, is positive.

  • Actually, it's larger than 0.

  • So let's start the proof for this theorem.

  • It's pretty amazing to me, actually,

  • that you can do so much a game like this,

  • and see so much happening.

  • So let's figure out how this works.

  • OK.

  • So let's first formalize this particular trick over here.

  • And how do we go ahead with it?

  • Ah, well, notice that we can rewrite

  • L, which is equal to s times a, plus t times b.

  • s, you know, we can just add a multiple of b over here.

  • n times b, say m times a.

  • And we can subtract the same amount over here,

  • minus n times a, times b.

  • So do you see what I did over here?

  • I have added n times b, times a, and subtracted n, times a times

  • b.

  • And we did something similar over here, not exactly

  • the same.

  • But that's what we did.

  • And you can imagine that we can choose m,

  • such that s plus n times b will be larger than 0.

  • We can do that.

  • So essentially this proved to us that there exists an x prime,

  • and also the t prime, such that L

  • can be rewritten as a linear combination,

  • s prime, times a, plus t prime, times b.

  • But now with you extra property, that s prime

  • is actually positive.

  • Now this is really important, because we're

  • going to create an algorithm of playing

  • with those jugs that can achieve this particular linear

  • combination.

  • And that's how we're going to prove this theorem.

  • So let's assume that 0 is less than L, is less than b.

  • I know that we, in the theorem, we also

  • consider the case is L equals 0, and L equals b.

  • But those are obvious, right?

  • You could either empty the jugs, or just

  • fill up with the bigger one.

  • So we will consider just this case.

  • All right.

  • So what's the algorithm going to do for us?

  • The algorithm is going to repeatedly fill and pour

  • our jugs in a very special way.

  • And miraculously we will be able to get

  • the desired linear combination every single time.

  • And of course, we're going to use induction again

  • to prove this property.

  • OK.

  • So how does the algorithm work?

  • Well, to obtain L gallons we're going to repeat

  • s prime times, which is the number that we have over here.

  • The following algorithm-- we first of all,

  • we will fill the a jug.

  • This one.

  • After we have done this, we are going

  • to pour this into the b jug.

  • So how do we go ahead?

  • We pour- oops.

  • This into the b jug.

  • And when this b jug becomes full, we are going pour it out.

  • So let's write it out.

  • So when it becomes full, it will actually empty it out.

  • And we will continue pouring the a jug into the b jug.

  • So we'll continue this process.

  • So let's take an example to see how that works.

  • So we keep on doing this until the a jug is actually empty.

  • So let's take an example.

  • So let's see.

  • Let's do that over here.

  • Actually we can do the tennis balls, too.

  • Let's do that first.

  • See how that works.

  • So essentially, in order to get 4 gallons,

  • we just fill up the three gallon jug.

  • We empty it all in here.

  • We fill it up again.

  • You pour in as much as we can.

  • That's-- that's it.

  • We have to empty this one.

  • Oops.

  • We have to keep on pouring.

  • Put this in here.

  • Fill this one up, and then pour over into the five gallon jug.

  • And now we've got 4 gallons over here.

  • So what did we do?

  • So let's write it out.

  • So for our special linear combination over here,

  • we have that 4 equals 3, times 3, minus 1, times 5.

  • So we need to repeat this process three times.

  • So let's do that.

  • In our first loop we will do the following.

  • We start with the start state, the pair 0, 0.

  • We're going to fill up the very first jug all the way up

  • to its capacity, 3.

  • And we put it all over into the b jug.

  • What happens in the second loop?

  • The second loop, we again fill up the a jug.

  • So we have-- we start at 0, 3.

  • We fill it up.

  • We get 3, 3, the pair 3, 3.

  • We pour everything in here, as much as we can.

  • That give us 1, 5.

  • Only 2 gallons are poured into the bigger gallon.

  • We empty the bigger gallon, the bigger jug.

  • We get 1, 0.

  • And we keep on pouring, and you get 0, 1.

  • So now in the third loop-- and that's

  • where we should get the 4 gallons.

  • We start off with 0,1.

  • Um, we fill up the a jug.

  • We pour everything over into the bigger jug, and we get 0, 4.

  • And that's the end result.

  • So this algorithm seems to work for this particular example.

  • Of course we would like to prove it for the general situation.

  • So how do we do it?

  • Well, we're going to just to analyze the algorithm

  • in the following way.

  • We can notice that in this algorithm,

  • we fill up s prime times the a jug,

  • and we essentially pour everything out into the b jugs,

  • and we sometimes empty the b jug.

  • So let's try to think about this a little bit,

  • and see how we could try to formalize this.

  • So let's write it out.

  • We have filled the a gallon jug s prime times.

  • We also know that the b jug has been emptied

  • a certain number of times.

  • So let's-- let's just assume-- suppose that the b jug is

  • actually emptied, say, u times.

  • I do not know how many times.

  • But I say, well, let's assume it's u times,

  • and try to figure out whether we can

  • find some algebraic expression.

  • So at the very end of the algorithm,

  • let r be what is in the b jug.

  • So let r be the remainder, in the b gallon jug.

  • So now we can continue.

  • We know if r is what left in the b gallon jug, well,

  • we know already some property of it.

  • Actually, let's put that on the next board.

  • We know that 0 is at most r, and at most b,

  • because that's what's left in the b gallon jug, right?

  • So we know these bounds.

  • We have assumed that 0 is less than L, is less than b,

  • which we put over there.

  • We know that r must be equal to what

  • kind of linear combination of s prime, and u?

  • So-- Well, we have been filling of s prime times.

  • So this is what we added in water to the whole system,

  • you can say, s prime times a.

  • And we poured out water.

  • Well, we did that u times from the b gallon jug.

  • So we poured out u times b gallons.

  • So this is the remainder that this left in this bigger jug,

  • right?

  • So are there any questions about this?

  • So-- OK.

  • So we also know that L is equal to s prime,

  • times a, plus t prime, times b.

  • And this is the linear combination

  • that we would try to prove of, that it

  • is left at the very end.

  • So what we want to show is that r equals L.

  • So how do we do that now?

  • How are we going to show that r can be expressed

  • in L, in a special way.

  • So let's have a look.

  • So these are all tricks in the sense

  • that I'm giving you this proof, but how do

  • you come up with this yourself?

  • Sometimes you play a lot with these kinds of things,

  • and you get a feeling of what kind of-- sort of pattern

  • exists, and what kind of intuition

  • you need in order to write down a proof like this.

  • So let's rewrite this.

  • I'm going add t prime times b.

  • And I'm going to subtract it again.

  • So I have s prime times a, plus t prime times b.

  • I subtract it again, and I still have this amount left open

  • here.

  • So what is this equal to?

  • Well this part is equal to L. So this is equal to minus--

  • and I have a multiple of b, which is t prime,

  • plus u times b.

  • Hm.

  • Now this is very interesting.

  • Does anybody see how we could continue here?

  • So we have r expressed as L, minus a multiple of b.

  • And I also know that L is in this range.

  • I also know that r is in this range.

  • So that's kind of interesting, right?

  • So how can that be?

  • What should be the case here?

  • Does anybody see what kind of property t prime plus u

  • must have in order to make that happen?

  • So let's have a look here.

  • We have L. It's in this range.

  • So let's just draw an axis.

  • So at 0, we have b.

  • And somehow in this range, we have L. Now

  • if I subtract like actually b, or something more than b,

  • or I add more than b.

  • I will jump out of this range, and I go somewhere over here,

  • or I go somewhere over there.

  • Right?

  • So if I said suppose L is over here,

  • then L minus b would be over here, which would be negative.

  • Or if I add b, it will be over here,

  • which would be more than b.

  • Now we know that this is equal to r, but r is in this range.

  • So that's not really possible.

  • So let's write it out.

  • So if t prime plus u is unequal to 0,

  • so we're actually really subtract

  • or add a multiple of b.

  • Then I know that r is either smaller than 0,

  • or r is larger than b.

  • Now we know that cannot be the case,

  • so we can conclude that t prime plus u equals 0.

  • Now that implies that t prime equals minus u,

  • or maybe other way around, because that's easier

  • to see what's happening.

  • So u equals minus t prime.

  • If you plug that in here, well, we

  • get exactly the same expression.

  • You see?

  • Minus, minus t prime is equal to plus t prime.

  • And we get the exact same linear combination.

  • So we conclude that r equals L.

  • And now we're done.

  • Why is that?

  • Well, we have shown that the very last number of gallons

  • that is left after this procedure,

  • after this algorithm, is actually

  • exactly equal to the linear combination

  • that we wanted to achieve.

  • So now we got the proof for this theorem that tells us

  • that any linear combination is actually-- of a and b

  • can actually be reached by pouring gallons over and back,

  • and emptying and filling those jugs.

  • All right let's continue.

  • So there was a question over here

  • that I would like to-- that I would like to address.

  • So maybe I did not make so clear what the s prime,

  • and the t prime is over here.

  • And in this proof, we started off

  • with this linear combination.

  • I would like to have an algorithm

  • of pouring that creates L gallons in say the bigger jug.

  • So in order to do that, I want to find, say,

  • a linear combination that makes this L such that this

  • s prime is an integer-- positive integer.

  • Why do I want to have a positive integer?

  • Because in this algorithm, I'm going to repeat something

  • s prime times.

  • If s prime is negative, I cannot do it, right?

  • So s prime has to be a positive integer.

  • In order to create such a positive integer,

  • I can just add like 1,000 times b times,

  • and subtract 1,000 times a times b.

  • That's OK I could just add a lot.

  • And if I add enough, I can make s plus n times b positive.

  • Even if s is, say, minus 100, well, if I add 1,000 times 5,

  • I will get a positive number.

  • So that's sort of the reason this proof

  • that we want to rewrite the linear combination

  • to a new one, such that s prime is positive.

  • And if we have s prime positive, then we

  • can actually talk about this algorithm,

  • because we can only repeat something s prime times,

  • if s prime is say 1, or 2, or 3, or something positive.

  • All right.

  • So let's-- I'll talk about say the next part.

  • So we have gone-- We have proved two theorems.

  • But in the end we would like to have a characterization

  • of the greatest common divisor.

  • That's the goal of this lecture.

  • So let's do it.

  • Um.

  • In order to do this, let's first of all

  • look at our five gallon, and three gallon example.

  • We know that the greatest common divisor is equal to 1.

  • We know that 1 can be rewritten as a linear combination, as 2

  • times 3, minus 1 times 5.

  • So that means that according to the theorem that we

  • have up here, we can actually make exactly 1

  • gallon in one of these jugs.

  • So that means that we can also have any multiple of those.

  • So we can reach any multiple 1.

  • That's very special.

  • So this particular case, we know that any multiple

  • of 1, any number of gallons can be reached.

  • So can we sort of generalize this a little bit

  • by using the greatest common divisor?

  • So the greatest common divisor 3 and 5 is equal to 1.

  • And we have shown that the greatest common divisor defies

  • any result. Can we say something more?

  • Can we say that the greatest common divisor

  • can be maybe written as a linear combination

  • of this type over there?

  • And that's how we are going to proceed now.

  • So let's set talk about the very special algorithm which

  • is called Euclid's algorithm.

  • And I think in the book it's also called The Pulverizer.

  • And you will have a problem on this

  • just to see how that works, and to really understand it.

  • So let's explain what we want here.

  • So first of all, we know that for any b and a,

  • there exists a unique quotient and remainder r.

  • So let's write it out.

  • There exists unique q, which we will call the quotient.

  • And r.

  • We call this the remainder.

  • Such that b equals q times a, plus r.

  • With the property that 0 is at least r, and at most a.

  • So we're not going to prove this statement.

  • It's actually like a theorem, right?

  • But let's just assume it for now.

  • And in the book you can read about it.

  • We're going to use this to prove the following lemma that we

  • will need to give a characterization

  • of the greatest common divisor, as a linear combination

  • of integers.

  • Oh, before I forget, you will denote this remainder

  • as rem of b, a.

  • And this is the notation that we use in this lecture.

  • So what's the lemma?

  • The lemma is that the greatest common divisor of a and b,

  • is equal to the greatest common divisor of the remainder of b

  • and a.

  • With a.

  • So what did we do?

  • Let's give an example to see how this works.

  • For example, let's take-- actually

  • let's do it on this white board.

  • So, let's see.

  • For example, let's see whether we

  • can use this to calculate the greatest common divisor 105,

  • and 224.

  • So how can we go ahead?

  • Well, according to this lemma, we

  • can rewrite this as the greatest common divisor

  • of first the remainder of 224, after dividing out

  • as many multiples of 105 as possible.

  • And 105.

  • So what are we going to use here?

  • We're going to use that 224 is actually equal to 2 times 105,

  • plus 14.

  • So we had the GCD of 14 and 105.

  • Now why can I do this?

  • Well, I'm essentially just subtracting like 2 times 135

  • from 224.

  • Well, the greatest common divisor

  • that divides 105 and 224 also divides 105,

  • and a linear combination of 105, 224.

  • That's essentially what we are using.

  • And that's actually stated in this lemma,

  • and that's what we would like to prove.

  • So let's continue with this process,

  • and do the same trick once more.

  • So we can say that we can rewrite this as the greatest

  • common divisor of, well, the remainder of 105

  • after taking out this many multiples of 14 as possible,

  • and 14.

  • So what are we going to use over here?

  • We are going to use that 105 is equal to 7 times 14, plus 7.

  • So this is the greatest common divisor of 7, and 14.

  • Now if you just continue this process,

  • we can see that this is equal to the greatest common divisor,

  • again, of the remainder of now 14,

  • after dividing out as many multiples of 7 with 7.

  • Now this is equal to 0, 7.

  • Why is that?

  • Because 14 is equal to 2 times 7, plus 0.

  • So 0 is the remainder after dividing out

  • 7 as many possible times as possible.

  • OK.

  • So we have the greatest common divisor of 0, and 7.

  • What's the largest integer that can divide both 0 and 7?

  • Well, any integer can divide 0.

  • So we know that this is equal to 7.

  • So essentially, what we have done here,

  • we have repeatedly used this particular lemma

  • to compute in the end, the greatest common divisor of 105

  • and 224.

  • And we have been very methodol-- we have used a specific method.

  • We used the lemma, and we worked it out.

  • We used the lemma again, and we just

  • plugged in the actual numbers.

  • Used to lemma again.

  • Plugged in the actual numbers, and so on.

  • And this is what is called Euclid's algorithm.

  • And in the book it's also called The Pulverizer.

  • And there's, I think, a few other names.

  • But I like this one.

  • So this is an example of Euclid's algorithm.

  • So now let's have to look whether we

  • can have prove this particular lemma,

  • and actually I will-- Yep.

  • We're going to prove this lemma.

  • OK.

  • So how do we do the proof?

  • Well, first before we know that if- yeah.

  • Well, how do we do this?

  • You would like to prove that if the great-- well, if n

  • divides a and b, in particular, the greatest

  • common divisor divides a and b.

  • We would like to show that it's dividing also

  • the remainder of b, after dividing out a, and a itself.

  • If you can show that, then we know

  • that the greatest common divisor of this thing

  • is at least what we have over here.

  • So I said a lot right now.

  • So let's try to write it out a little bit.

  • So suppose that m is any divisor of a.

  • And at the same time, m also divides b.

  • Well, then I know that m also divides

  • b minus, say, the quotient, q that we had over here, times a.

  • And-- and this is actually equal to the remainder of b and a.

  • Now we also note that m divides a.

  • So what did we show here?

  • We showed that if m divides, and m divides b, then m

  • also divides the remainder of b and a.

  • And n divides a.

  • So what does is prove?

  • Well, it proves that, in particular,

  • the greatest common divisor over here divides this one.

  • That's interesting.

  • That essentially means that we have shown this inequality.

  • Because if this one divides this,

  • well, that means that this number over here

  • must be at least what we have over here.

  • OK.

  • So let's continue.

  • We consider two cases.

  • If the remainder of b and a is unequal to 0, well,

  • what can we say now?

  • We can say that if I know that m divides

  • this remainder of b and a, which can be rewritten as b minus q,

  • times a.

  • And I also note that-- if I also know that n divides a,

  • then this actually implies the reverse of this statement,

  • that n divides a, and divides b.

  • Now why is that?

  • Well, we're actually using the fact

  • that if n divides b, minus q, times a, and m divides a,

  • then m also defies any linear combination of these two.

  • In particular, this plus q, times a, which is b.

  • m divides b.

  • So maybe I'm going a little bit fast here, I notice.

  • This all also has to do with all the lecture handouts.

  • You see a few facts on the divisibility.

  • And in particular, item number three that talks about the fact

  • that I'm using here.

  • If a divides b on your handout, and a divides c, then I

  • know that a divides any linear combination of b and c.

  • So that's essentially what I'm using here repeatedly.

  • OK

  • So let's look at the other case.

  • If the remainder is equal to 0, well, then I actually

  • know that b minus q, times a is equal to 0.

  • Well, if I know that m divides a, well,

  • then since 0 equals b minus q, times a, I know that b equals

  • q, times a.

  • So if m divides a, I also now that m divides b.

  • So this is one argument.

  • This is another one.

  • And this was-- These are the three arguments that

  • now show that anything that divides these two also

  • divides a and b.

  • So now we have the reverse argument, right?

  • So this greatest common divisor divides this one here,

  • and this one.

  • And we just proved that it divides a and b,

  • and so it must divides the greatest

  • common divisor of a and b.

  • So now we have shown the other inequality,

  • and this proves equality.

  • So you should definitely look this up in your lecture notes.

  • So now we can finally prove this beautiful theorem

  • a that will help us to characterize the-- actually,

  • let me put this over here.

  • So the final theorem that we prove here

  • is that the greatest common divisor of a and b

  • is actually a linear combination of a and b.

  • So we're going to use this algorithm that you have

  • over here, Euclid's algorithm.

  • And we are going to do a proof, again, by induction.

  • And we use an invariance.

  • So we use a similar kind of strategy, of course.

  • The invariance that we are going to use

  • says-- well, if Euclid's algorithm reaches the greatest

  • common divisor of x and y-- so for example,

  • it's reach, say, 7 or 14, and 105, for example.

  • Then, say, after n steps then both x and y are

  • linear combinations of a and b.

  • So then x and y are linear combinations of a and b.

  • And at the same time, we also know

  • that the greatest common divisor of a and b

  • is equal to the greatest common divisor of x and y.

  • So this is my invariance.

  • And the way I will go ahead is to simply do what you do always

  • in these situations.

  • So we start with the base case.

  • And we can immediately see that after 0 steps in the Euclidean

  • algorithm, I've done absolutely nothing.

  • So obviously after 0 steps, x equals a.

  • y equals b.

  • So of course, they are linear combinations of a and b.

  • And this equality holds, as well.

  • So for the base case--

  • So after 0 steps, we immediately know that p 0 is true.

  • Now for the inductive step, we have to do a little bit more.

  • As usual, right?

  • We always assume p n.

  • And now we would like to prove p n plus 1.

  • So how do we do this?

  • Well, we notice that there exists a q such

  • that the remainder of y and x is equal to y minus q, times x.

  • So we assume p n.

  • We have reached some state, x, y.

  • We know that the remainder of y, x equals y minus q,

  • times x, for some quotient q.

  • We know that y is a linear combination of a and b,

  • and x is, as well.

  • So that means that this one is actually also

  • a linear combination of a and b.

  • So now when we look at this , algorithm we can see that--

  • that if you look at the remainder that appears in here,

  • that's still a linear combination of a and b.

  • So after a extra step, we notice that what we have reached

  • are still in combinations of a and b.

  • And of course, the lemme has showed--

  • has shown us that what we reach is still equal--

  • the greatest common divisor is still equal to what we

  • originally started out with.

  • So this proves p of n plus 1.

  • So n-- let's finish this particular proof.

  • So for the very last step, if you now

  • look at this particular-- so if you look at the very end,

  • we notice that in every step the remainder

  • is getting smaller, and smaller, and smaller.

  • Right?

  • And you can use a similar kind of proof technique

  • to show that after a finite number of steps,

  • we will reach a GCD of 0, y.

  • Something like this.

  • So in the very last step of Euclid's algorithm

  • we achieve something off this form.

  • We now use our predicate over here,

  • and say that y is a linear combination of a and b,

  • but the greatest common divisor of 0, y

  • is also equal to the original greatest common divisor

  • that we want to characterize.

  • So now we have proved the theorem

  • that says that the greatest common divisor of a and b

  • is actually a linear combination.

  • So now we're going to combine all those three

  • theorems in one go.

  • And that will show us the final result,

  • which is that the theorem that the greatest common divisor

  • of a and b is actually the smallest

  • positive linear combination of a and b.

  • So we're going to combine all of these together.

  • We know that the greatest common divisor divides any result.

  • The theorem up there says that any linear combination

  • can be reached.

  • And also just showed-- have shown

  • that the greatest common divisor is a linear combination of a

  • and b.

  • So we can combine those three to get this theorem.

  • So how do we do it?

  • Well, let's just look 0 all the way up to b.

  • Suppose these are all the results that we

  • can reach in our problem.

  • We know that the greatest common divisor divides all of those.

  • At the same time, it's also linear combination

  • that's over here.

  • Since it's a linear combination, it can also be reached, right?

  • By the theorem that we have.

  • So suppose that this is the greatest common divisor.

  • But we also know that the greatest common divisor

  • is dividing all of these points here that can be reached.

  • So therefore, it must be the smallest one.

  • And I will leave you with some homework

  • to think about this very carefully.

  • And you can show for yourself that you can now

  • combine those three arguments together, and see

  • that the greatest common divisor must be the smallest positive

  • linear combination.

  • So, I will see next Thursday.

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