The grading is going to be based on the

recognition that there is no silver bullet in optimization.

Even for us, when we are giving a new problem it's

not always obviously clear, you know, what is the best solution?

And what is going to scale and how we model

the problems and, you know, what we can expect, okay?

So in a sense, the assignments

that you are going to see here are intentionally insane, okay?

So they are going to make sure, that

one particular assignment, you know one technique is

not going to work for all part of the

assignments unless you are really, really really clever, okay.

But most of the time, some of the techniques will work on

some of the instances, other techniques will work on other ones, okay.

It's done on purpose.

So that to get a sense of not only exponential growth,

but also the fact that, wow the structure of the problem is

really important. Okay?

And so, so this is the insane part, but we are also very flexible, okay?

So we give you ways to succeed, and different ways to succeed, okay.

And you can for instance take a scalability or, you know

a quality approach, and they will be both fine in the class.

You can say okay, so I'm only focusing

on scalability, and trying to get solutions to everything.

Or I'm going to try to get the best solution

to some of the instance and, and you know,

kind of solutions, you know, maybe not high quality solution, on the other ones.

There are, these two ways are going to be

ways to actually be successful in this particular class.

Let me give you a little bit of a sense of this

of this, of these two ways of actually approaching the problem, okay?

So when you look at the particular problem and let's say,

so typically we six part in every one of these problems.

Let's say that four are reasonably small,

two are really large, okay?

So if you do a, you know a, qualitative, but if you do an approach which

is based on finding high quality solution, you

may get the top grade, like say ten, okay.

On four of them and then a low grade on, on, on two of them.

And that's going to give you an average a,

a value of 46 on that particular assignment.

And that's a, that's a number for which you can get a certificate of completion.

You can also do the opposite thing, which is okay, so I'm going to focus on

scalability, get good solutions, okay, like you

know seven, on all of these problems.

And you get 6 times 7, which is 42 Which is also enough to get you a certificate.

Okay, so these are the two ways to do this, okay.

An, and this is the two way you can actually get a certificate in the class.

If you want a certificate of distinction, you probably need to combine these two.

Now, one of the real thing that I wanted you

to focus on is that first get good grades, okay?

So don't

focus on getting tense everywhere.

You have plenty of opportunities, and I'm going to talk about how to

approach the class from a, you know, time optimization standpoint in a moment.

But try first to get good grades everywhere.

And then beef them up.

You'll see you have a lot of opportunities to do that.

Okay? So, and, this is the key point, okay?

So, so, if you take the first assignment, don't get obsessed, okay?

It's very easy to get obsessed in optimization.

You see this thing, and you want to get a ten.

You see another guy getting a ten and you say, oh, you know, I have to get a ten.

You know, you get, you know, completely frustrated.

Don't.

Okay?

Maybe the person knows more than you do, okay, at this point.

Okay, but this class will give you everything you need to get a ten.

Okay?

It's just going to come over time.

So you can go, do these assignments, get

sevens, so you'll, and be pretty happy, okay?

And then at the end, you say hm, but this techniques, I could apply to this first

problem that I actually solve.

And now I know exactly how to solve, you get back

to it, and in like two minutes, I mean, I'm exaggerating, right?

So you get a time, okay? So this is what this class is all about.

You're going to learn things, and then you can go back

in the past, fix your solution, and get much better grades.

Okay, so don't, don't get stuck on a particular problem.

Don't get completely obsessive, okay?

So in the past, some people have become so

obsessive that, you know, it was like way to,

you know, cool them down, okay? So we don't want that to happen.

Get good grades, come back to the

assignment, and get better grades later on.

Okay?

So, so you will have a lot of time also at the end.

I mean not a lot of time, a reasonable amount of

time to go back and fix the solution at the end.

There is a buffer at the end, just for you to do that.

And a lot of people are basically exploiting this.

Okay, so let me, let me, let, let, let me then

talk about one last topic, which is how you should approach this

class, such that it's, if, you know, you keep a reasonable

level of happiness during, you know, the time you take this class.

You still get a social life, you still are in a good mood,

you don't lose all your friends, your spouse, and all these things, okay?

And so let me give you an analogy, okay.

So the analogy is, you know, last year, well, no, in 2013 when, when

actually Raphael Nadal won Roland Garros, he

was interviewed by, you know, somebody in the

tournament, and they were asking me, how does it feel to win the tournament?

And Nadal, you know, this great tennis champion said, well,

you know, I'm going to be happy for two weeks, it's great.

And so you see this guy training like a beast for

50 weeks and then he's happy two weeks of the year?

This is terrible right?

That, I don't want that to happen to you. Okay, so look at this graph.

This is your level of happiness.

It's also your level of confidence technical

confidence in general. And, so what you will see here is time.

And this is the time during the class, okay, or during an assignment.

And you will usually start with, you know, a lot of confidence, a lot of

happiness, you come there, you design this

amazingly beautiful solution, and you start coding it.

And then as you code it, you know, and you fix things, and you

fix things, your level of happiness is

decreasing, your level of confidence is decreasing.

You kind of get desperate, desperate.

You know, it's a pain to actually work on this, and this, and then at

the very end, wow, you get a big high, because this thing's turned out to work.

Okay.

No, so, this is essentially what Raphael Nadal is experiencing.

He's training like a beast, and then he has these two weeks where he's happy.

For you, it's going to be basically, let's say a week

of work and then, two minutes where you will be happy.

We don't want that. Okay?

Because you will have to, you know, go

that, do the next assignments at that point.

We don't want, that's not what we want

you to do, okay?

So what we want you to do is something like this, okay?

So you start, you know, at the reasonable level of, of happiness and confidence,

and then you start building something which

is easy, let's say a greedy algorithm.

Your confidence decreased, but not very much, because this is pretty simple.

And then you get a solution, and you get a first high.

And you say wow, okay.

So hm, I'm happy, you know, I have something

that works, you know, and you say, good, okay.

Then you start looking around,

you see, oh, but maybe there are people

with better solution, maybe I can improve this.

And you start coding, let's say, local search solution.

And so it takes a little bit of confidence away from your level of happiness.

You have to work a little bit harder. You don't see your friend as much.

But then you have another high.

You know, wow.

We have a really good quality solution at this point.

And you say, oh wow, now this is good, this is good.

Now you have a lot of confidence, see here your confidence is increasing.

And you say,

oh no but now I want some kind of guarantees on how good I am, okay?

And you say oh, let me try a MIP approach to actually get that guarantee.

That's a little bit tougher.

You know, your confidence and your level of happiness is going to decrease.

But you know it's a very short amount of time here, and then wow, a big high.

Now, I know how good I am, right?

And you said, this is good, this is good, another high and, you know, even higher.

But you say, well, but this MIP solution for

this [UNKNOWN] problem is like a dog you know?

Let me

try CP to actually do better in terms of efficiency.

And then you get the final high, where you get this beautiful CP solution at the end.

Okay?

So, this is what this class is about.

This class is about being high all the time, legally high.

Right? All the time, right?

So this is what we want.

The dips are very low, the peaks are high,

and that's the best way to actually approach this class.

Don't wait until the last moment to experience this kind of satisfaction

at the end. Okay?

So do this like that.

Okay, so have fun, you know it's insane, but it's a

lot of fun, especially if you do it the right way.

Okay.

Don't get frustrated, you can always come back,

you have a lot of opportunities in this class.

Okay.

Enjoy it. Thank you very much, guys.

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