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The topic for today is --

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Today we're going to talk,
I'm postponing the linear

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equations to next time.
Instead, I think it's a good

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idea, since in real life,
most of the differential

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equations are solved by
numerical methods to introduce

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you to those right away.
Even when you see the compute

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where you saw the computer
screen, the solutions being

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drawn.
Of course, what really was

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happening was that the computer
was calculating the solutions

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numerically, and plotting the
points.

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So, this is the main way,
numerically,

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it is the main way differential
equations are actually solved,

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if they are of any complexity
at all.

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So, the problem is,
that initial value problem,

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let's write up the first-order
problem the way we talked about

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it on Wednesday.
And now, I'll specifically add

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to that, the starting point that
you used when you did the

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computer experiments.
And, I'll write the starting

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point this way.
So, y of x0 should be y0.

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So, this is the initial

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condition, and this is the
first-order differential

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equation.
And, as you know,

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the two of them together are
called an IVP,

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an initial value problem,
which means two things,

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the differential equation and
the initial value that you want

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to start the solution at.
Okay, now, the method we are

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going to talk about,
the basic method of which many

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others are merely refinements in
one way or another,

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is called Euler's method.
Euler, who did,

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of course, everything in
analysis, as far as I know,

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didn't actually use it to
compute solutions of

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differential equations.
His interest was theoretical.

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He used it as a method of
proving the existence theorem,

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proving that solutions existed.
But, nowadays,

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it's used to calculate the
solutions numerically.

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And, the method is very simple
to describe.

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It's so naive that you probably
think that if you that living

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300 years ago,
you would have discovered it

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and covered yourself with glory
for all eternity.

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So, here is our starting point,
(x0, y0).

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Now, what information do we
have?

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At that point all we have is
the little line element,

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whose slope is given by f of
(x, y).

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So, if I start the solution,

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the only way the solution could
possibly go would be to start

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off in that direction,
since I have no other

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information.
At least it has the correct

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direction at (x0,
y0).

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But, of course,
it's not likely to have the

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correct direction anywhere else.
Now, what you do,

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then, is choose a step size.
I'll try just two steps of the

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method.
That's, I think,

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good enough.
Choose a uniform step size,

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which is usually called h.
And, you continue that solution

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until you get to the next point,
which will be x0 + h,

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as I've drawn it on the
picture.

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So, we get to here.
We stop at that point,

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and now you recalculate what
the line element is here.

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Suppose, here,
the line element,

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now, through this point goes
like that.

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Well, then, that's the new
direction that you should start

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out with going from here.
And so, the next step of the

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process will carry you to here.
That's two steps of Euler's

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method.
Notice it produces a broken

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line approximation to the
solution.

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But, in fact,
you only see that broken line

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if you are at a computer if you
are looking at the computer

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visual, for example,
whose purpose is to illustrate

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for you Euler's method.
In actual practice,

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what you see is,
the computer is simply

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calculating this point,
that point, and the succession

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of points.
And, many programs will just

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automatically connect those
points by a smooth looking curve

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if that's what you prefer to
see.

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Well, that's all there is to
the method.

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What we have to do now is
derive the equations for the

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method.
Now, how are we going to do

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that?
Well, the essence of it is how

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to get from the nth step to the
n plus first

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step?
So, I'm going to draw a picture

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just to illustrate that.
So, now we are not at x0.

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But let's say we've already
gotten to (xn,

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yn).
How do I take the next step?

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Well, I take the line element,
and it goes up like that,

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let's say, because the slope is
this.

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I'm going to call that slope A
sub n.

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Of course, A sub n is the value
of the right hand side at the

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point (xn, yn),
and we will need that in the

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equation, --
-- but I think it will be a

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little bit clearer if I just
give it a capital letter at this

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point.
Now, this is the new point,

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and all I want to know is what
are its coordinates?

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Well, the x n plus one
is there.

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The y n plus one is here.

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Clearly I should draw this
triangle, complete the triangle.

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This side of the triangle,
the hypotenuse has slope An.

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This side of the triangle has
length h.

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h is the step size.
Perhaps I'd better indicate

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that, actually put that up so
that you know the word step.

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It's the step size on the
x-axis, how far you have to go

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to get from each x to the next
one.

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What's this?
Well, if that slope has this,

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the slope An,
this is h.

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Then this must be h times An,
the length of that side,

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in order that the ratio of the
height to this width should be

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An.
And, that gives us the method.

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How do I get from,
clearly, to get from xn to x n

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plus one,
I simply add h.

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So, that's the trivial part of
it.

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The interesting thing is,
how do I get the new 

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y n plus one?
And so, the best way to write

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it as, that y n plus one minus
yn divided by h,

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well, sorry,
y n plus one minus yn is this

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line, the same as the line h
times An.

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So, that's the way to write it.
Or, since the computer is

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interested in calculating y n
plus one itself,

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put this on the other side.
You take the old yn,

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the previous one,
and to it, you add h times An.

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And, what, pray tell,

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is An?
Well, the computer has to be

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told that An is the value of f.
So, now, with that,

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let's actually write the Euler
program, not the program,

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but the Euler-- the Euler
method equations,

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let's just call it the Euler
equations.

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What will they be?
First of all,

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the new x is the old x plus h.

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The new y is just what I've
written there,

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the old y plus h times a
certain number,

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An, and finally,

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An has the value--
It's the slope of the line

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element here,
and therefore by definition,

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that's f of (xn,yn).

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So, if these three equations
which define Euler's method.

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I assume in 1.00 you must be
asked to, at some point,

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as an exercise in the term at
one point to program the

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computer in C or whatever
they're using,

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Java, now, I guess,
to do Euler's method.

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And, these would be the
recursive equations that you

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would put in to do that.
Okay, let's try an example,

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then.
So, what would be a good color

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for Euler?
Well, a purple.

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I assume nobody can see purple.
Is that correct?

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Can anyone in the back of the
room see that that's purple?

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Okay.
Sit closer.

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So, let's calculate.
The example,

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I'll use a simple example,
but it's not entirely trivial.

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My example is going to be the
equation, x squared minus y

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squared on the 
right hand side.

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And, let's start with y of zero
equals one,

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let's say.
And so, this is my initial

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value problem,
that pair of equations.

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And, I have to specify a step
size.

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So, let's take the step size to
0.1.

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You choose the step size,
or the computer does.

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We'll have to talk about that
in a few minutes.

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Now, what do you do?
Well, I say this is a

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nontrivial equation because this
equation, as far as I know,

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cannot be solved in terms of
elementary functions.

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So, this equation would be,
in fact, a very good candidate

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for a numerical method like
Euler's.

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And, you had to use it,
or maybe it was the other way

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around, I forget.
On your problem set,

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you drew a picture of the
direction field and answered

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some questions about the
isoclines, how the solutions

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behave.
Now, the main thing I want you

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to get, this is not just for
Euler's, talking about Euler's

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equations.
But in general,

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for the calculations you have
to do in this course,

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it's extremely important to be
systematic because if you are

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not systematic,
you know, if you just scribble,

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scribble, scribble,
scribble, scribble,

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you can do the work,
but it becomes impossible to

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find mistakes.
You must do the work in a form

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in which it can be checked,
which you can look over it and

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find, and try to see where
mistakes are if,

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in fact, there are any.
So, I strongly suggest,

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this is not a suggestion,
this is a command,

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that you make a little table to
do Euler's method by hand,

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I'd only ask you for a step or
two, but since I'm just trying

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to make sure you have some idea
of these equations and where

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they come from.
So, first, the value of n,

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then the value of xn,
then the value of the yn,

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and then, a couple of more
columns which tell you how to do

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the calculation.
You are going to need the value

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of the slope,
and it's probably a good idea,

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also, because otherwise you'll
forget it, to put in h An

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because that occurs in the
formula.

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All right, let's start doing
it.

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The first value of n is zero.
That's the starting point.

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At the starting point,
(x0, y0), x has the value zero,

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and y has the value one,
so, zero and one.

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In other words,
starting, I'm carrying out

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exactly what I drew pictorially
only now I'm doing it

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arithmetically using a table and
substituting into the formulas.

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Okay, the next thing we have to
calculate is An.

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Well, since An is the value of
the right hand side,

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at the point zero one,
you have to plug that in.

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The right-hand side is x
squared minus y squared.

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So, it's 0 squared minus 1
squared.

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The value of the slope,

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there, is minus one,
negative one.

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Now, I have to multiply that by
H.

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h is 0.1.
So, it's negative,

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I'll never learn that.
The way you learn to talk in

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kindergarten is the way you
learn to talk the rest of your

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life, unfortunately.
In kindergarten,

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we said minus.
Negative 0.1.

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n is one now.
What's the value of xn?

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Well, to the old value I add
1/10.

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What's the value of y?
Well, at this point,

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you have to do the calculation.
It's the old value of y.

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To get this new value,
it's the old value plus this

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number.
Well, that's this plus that

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number is nine-tenths.
An, now I have to calculate the

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new slope at this point.
Okay, that is one-tenth squared

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minus nine-tenths squared.
That's 0.01 minus 0.81,

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which makes minus 0.80,
I hope.

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221

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Check it on your calculators.
Whip them out and press the

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buttons.
I now multiply that by h,

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which means it's going to be
minus 0.08, perhaps with a zero

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after.
I didn't tell you how many

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decimal places.
Let's carry it out to two

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decimal places.
I think that will be good

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enough.
And finally,

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the last step,
2, here, add another one-tenth,

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so the value of x is now
two-tenths.

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And finally,
what's the value of y?

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Well, I didn't tell you where
to stop.

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Let's stop at y of 0.2
because there's no

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more room on the blackboard.
About approximately how big is

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that?
In other words,

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this is, then,
this is going to be the old y

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plus this number,
which seems to be 0.82 to me.

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So, the answer is,
the new value is 0.82.

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Okay, we got a number.
We did what we are supposed to

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do.
We got a number.

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Next question?
Now, let's ask a few questions.

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One of the first,
most basic things is,

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you know, how right is this?
How can I answer such a

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question if I have no explicit
formula for that solution?

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That's the basic problem with
numerical calculation.

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In other words,
I have to wander around in the

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dark to some extent,
and yet have some idea when

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I've arrived at the place that I
want to go.

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Well, the first question I'd
like to answer,

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is this too high or too low?
Is Euler, sorry,

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he'll forgive me in heaven,
I will use him.

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By this, I mean,
is the result,

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let me just say something
first, and that I'll criticize

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it.
Is Euler too high or too low?

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In other words,
is the result of using Euler's

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method, i.e.
is this number too high or too

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low?
Is it higher than the right

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answer, what it should be?
Or, is it lower than the right

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answer?
Or, God forbid,

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is it exactly right?
Well, it's almost never exactly

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right.
That's not an option.

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Now, how will we answer that
question?

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Well, let's answer it
geometrically.

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Basically, if the solution were
a straight line,

265
00:17:24 --> 00:17:30
then the Euler method would be
exactly right all the time.

266
00:17:29 --> 00:17:35
But, it's not a line.
Then it's a curve.

267
00:17:34 --> 00:17:40
Well, the critical question is,
is it curved?

268
00:17:38 --> 00:17:44
Is the solution?
So, here's a solution.

269
00:17:41 --> 00:17:47
Let's call it y1 of x,
and let's say here

270
00:17:45 --> 00:17:51
was the starting point.
Here, the solution is convex.

271
00:17:49 --> 00:17:55
And, here the solution is
concave, right?

272
00:17:52 --> 00:17:58
Concave up or concave down,
if you learn those words,

273
00:17:57 --> 00:18:03
but I think those have,
by now, I hope pretty well

274
00:18:01 --> 00:18:07
disappeared from the curriculum.
Call it, if you haven't up

275
00:18:06 --> 00:18:12
until now, what mathematicians
call it, convex is that,

276
00:18:10 --> 00:18:16
and the other one is concave.
Well, how do Euler's solutions

277
00:18:13 --> 00:18:19
look?
Well, I'll just sketch.

278
00:18:15 --> 00:18:21
I think from this you can see
already, when you start out on

279
00:18:19 --> 00:18:25
the Euler's solution,
it's going to go like that.

280
00:18:22 --> 00:18:28
Now you are too low.
Well, let's suppose after that,

281
00:18:25 --> 00:18:31
the line element here is
approximately the same as what

282
00:18:28 --> 00:18:34
it is there, or roughly
parallel.

283
00:18:32 --> 00:18:38
After all, they are not too far
apart.

284
00:18:34 --> 00:18:40
And, the direction field is
continuous.

285
00:18:37 --> 00:18:43
That is, the directions don't
change drastically from one

286
00:18:40 --> 00:18:46
point to another.
But now, you see it's still too

287
00:18:43 --> 00:18:49
low.
It's even lower as it

288
00:18:45 --> 00:18:51
pathetically tries to follow.
It's losing territory,

289
00:18:49 --> 00:18:55
and that's basically because
the curve is convex.

290
00:18:52 --> 00:18:58
Exactly the opposite what would
happen if the curve were

291
00:18:55 --> 00:19:01
concave, if the solution curve
were concave.

292
00:19:00 --> 00:19:06
Now it's too high,
and it's not going to be able

293
00:19:04 --> 00:19:10
to correct that as long as the
solution curve stays concave.

294
00:19:09 --> 00:19:15
Well, that's probably too
optimistic.

295
00:19:13 --> 00:19:19
It's probably more like this.
So, in other words,

296
00:19:17 --> 00:19:23
in this case,
if the curve is convex,

297
00:19:21 --> 00:19:27
Euler is going to be too high,
sorry, too low.

298
00:19:25 --> 00:19:31
Let's put E for Euler.
How about that?

299
00:19:30 --> 00:19:36
Euler is too low.
If it's concave,

300
00:19:32 --> 00:19:38
then Euler is too high.
Okay, that's great.

301
00:19:36 --> 00:19:42
There's just one little problem
left, namely,

302
00:19:40 --> 00:19:46
if we don't have a formula for
the solution,

303
00:19:44 --> 00:19:50
and we don't have a computer
that's busy drawing the picture

304
00:19:49 --> 00:19:55
for us, in which case we
wouldn't need any of this

305
00:19:54 --> 00:20:00
anyway, how are we supposed to
tell if it's convex or concave?

306
00:20:01 --> 00:20:07
Back to calculus.
Calculus to the rescue!

307
00:20:04 --> 00:20:10
When is a curve convex?
A curve is convex if its second

308
00:20:08 --> 00:20:14
derivative is positive because
the first to be convex means the

309
00:20:12 --> 00:20:18
first derivative is increasing
all the time.

310
00:20:16 --> 00:20:22
And therefore,
the second derivative,

311
00:20:18 --> 00:20:24
which is the derivative of the
first derivative,

312
00:20:22 --> 00:20:28
should be positive.
Just the opposite here;

313
00:20:25 --> 00:20:31
the curve, the slope is,
the first derivative,

314
00:20:29 --> 00:20:35
is decreasing all the time and
therefore the second derivative

315
00:20:33 --> 00:20:39
is negative.
So, all we have to do is decide

316
00:20:38 --> 00:20:44
what the second derivative of
the solution is.

317
00:20:41 --> 00:20:47
We should probably call it a
solution.

318
00:20:43 --> 00:20:49
y of x is a little too
vague.

319
00:20:46 --> 00:20:52
y1 means the solution started
at this point.

320
00:20:49 --> 00:20:55
So, in fact,
probably it would have been

321
00:20:52 --> 00:20:58
better from the beginning to
call that y1,

322
00:20:54 --> 00:21:00
except there's no room,
y1, let's say.

323
00:20:57 --> 00:21:03
That means the solution which
started out at the point,

324
00:21:01 --> 00:21:07
(0, 1).
So, I'm still talking about at

325
00:21:04 --> 00:21:10
a solution like that.
All right, so I want to know if

326
00:21:08 --> 00:21:14
this is positive,
the second derivative is

327
00:21:10 --> 00:21:16
positive at the starting point,
zero, or it's negative.

328
00:21:14 --> 00:21:20
Now, again, how you can
regulate the second derivative,

329
00:21:17 --> 00:21:23
if you don't know what the
solution is explicitly,

330
00:21:20 --> 00:21:26
then the answer is you can do
it from the differential

331
00:21:24 --> 00:21:30
equation itself.
How do I do that?

332
00:21:26 --> 00:21:32
Well: easy.
y prime equals x squared minus

333
00:21:29 --> 00:21:35
y squared.
Okay, that tells me how to

334
00:21:34 --> 00:21:40
calculate y prime if I
know the value of x and y,

335
00:21:39 --> 00:21:45
in other words,
the 0.01.

336
00:21:41 --> 00:21:47
What would be the value of y
double prime?

337
00:21:45 --> 00:21:51
Well, differentiate the
equation.

338
00:21:48 --> 00:21:54
It's two x minus two y y prime.

339
00:21:52 --> 00:21:58
Don't forget to use the chain
rule.

340
00:21:55 --> 00:22:01
So, if I want to calculate at
(0, 1), in other words,

341
00:22:00 --> 00:22:06
if my starting point is that
curve convex or concave,

342
00:22:04 --> 00:22:10
well, let's calculate.
y of zero equals one.

343
00:22:10 --> 00:22:16
Okay, what's y prime of zero?

344
00:22:12 --> 00:22:18
Well, I don't have to repeat

345
00:22:15 --> 00:22:21
that calculation.
Using this, I've already

346
00:22:18 --> 00:22:24
calculated that it was negative
one.

347
00:22:20 --> 00:22:26
And now, the new thing,
what's y double prime of zero?

348
00:22:24 --> 00:22:30
Well, it is this.

349
00:22:26 --> 00:22:32
I'll write it out.
It's two times zero minus two

350
00:22:29 --> 00:22:35
times negative y,
which is one,

351
00:22:31 --> 00:22:37
two times one times y prime,
which is negative one.

352
00:22:36 --> 00:22:42
You want to see we are pulling

353
00:22:41 --> 00:22:47
ourselves up by our own boot
straps, which is impossible.

354
00:22:45 --> 00:22:51
But, it is not impossible
because we are doing it.

355
00:22:49 --> 00:22:55
So, what's the answer?
Zero here, two,

356
00:22:52 --> 00:22:58
I've calculated without having
the foggiest idea of what the

357
00:22:57 --> 00:23:03
solution is or how it looks.
I've calculated that its second

358
00:23:03 --> 00:23:09
derivative at the starting point
is two.

359
00:23:07 --> 00:23:13
Therefore, my solution is
convex at the starting point.

360
00:23:13 --> 00:23:19
And therefore,
this Euler approximation,

361
00:23:17 --> 00:23:23
if I don't carry it out too
far, will be too low.

362
00:23:22 --> 00:23:28
So, it's convex Euler,
too low.

363
00:23:25 --> 00:23:31
Now, you could argue,
yeah, well, what about this?

364
00:23:32 --> 00:23:38
[LAUGHTER] So,
you could go like this,

365
00:23:36 --> 00:23:42
and then you can see it catches
up.

366
00:23:39 --> 00:23:45
Well, of course,
if the curve changes from

367
00:23:44 --> 00:23:50
convex to concave,
then it's really impossible to

368
00:23:49 --> 00:23:55
make any prediction at all.
That's a difficulty.

369
00:23:54 --> 00:24:00
So, all this analysis is only
if you stay very nearby.

370
00:24:02 --> 00:24:08
However, I wanted to show you,
the main purpose of it in my

371
00:24:06 --> 00:24:12
mind was to show you how do you
use, it's these equations,

372
00:24:11 --> 00:24:17
how to use the differential
equation itself to get

373
00:24:14 --> 00:24:20
information about the solutions,
without actually being able to

374
00:24:19 --> 00:24:25
calculate the solutions?
Now, so that's the method,

375
00:24:23 --> 00:24:29
and that's how to find out
something about it.

376
00:24:27 --> 00:24:33
And now, what I'd like to talk
about is errors.

377
00:24:32 --> 00:24:38
How do I handle,
right?

378
00:24:34 --> 00:24:40
So, in a sense,
I've started the error

379
00:24:38 --> 00:24:44
analysis.
In other words,

380
00:24:40 --> 00:24:46
the error, by definition, the
error is this difference, e.

381
00:24:46 --> 00:24:52
So, in other words,

382
00:24:48 --> 00:24:54
what I'm asking here,
is the error positive?

383
00:24:52 --> 00:24:58
It depends which we measure it.
Usually, you take this minus

384
00:24:59 --> 00:25:05
that.
So, here, the error would be

385
00:25:03 --> 00:25:09
considered positive,
and here it would be considered

386
00:25:07 --> 00:25:13
negative, although I'm sure
there's a book somewhere in the

387
00:25:13 --> 00:25:19
world, which does the opposite.
Most hedge by just using the

388
00:25:18 --> 00:25:24
absolute value of the error plus
a statement that the method is

389
00:25:23 --> 00:25:29
producing answers which are too
low or too high.

390
00:25:27 --> 00:25:33
The question,
then, is, naturally,

391
00:25:30 --> 00:25:36
this is not the world's best
method.

392
00:25:35 --> 00:25:41
It's not as bad as it seems.
It's not the world's best

393
00:25:38 --> 00:25:44
method because that convexity
and concavity means that you are

394
00:25:42 --> 00:25:48
automatically introducing a
systematic error.

395
00:25:45 --> 00:25:51
If you can predict which way
the error is going to be by just

396
00:25:49 --> 00:25:55
knowing whether the curve is
convex or concave,

397
00:25:51 --> 00:25:57
it's not what you want.
I mean, you want to at least

398
00:25:55 --> 00:26:01
have a chance of getting the
right answer,

399
00:25:57 --> 00:26:03
whereas this is telling you
you're definitely going to get

400
00:26:01 --> 00:26:07
the wrong answer.
All it tells you is,

401
00:26:03 --> 00:26:09
and it's telling you whether
your answer is going to be too

402
00:26:07 --> 00:26:13
high or too low.
We've like a better chance of

403
00:26:12 --> 00:26:18
getting the right answer.
Now, so the question is,

404
00:26:17 --> 00:26:23
how do you get a better method?
A search is for a better

405
00:26:23 --> 00:26:29
method.
Now, the first method,

406
00:26:26 --> 00:26:32
which will occur,
I'm sure, to anyone who looks

407
00:26:30 --> 00:26:36
at that picture,
is, look, if you want this

408
00:26:35 --> 00:26:41
yellow line to follow the white
one, the white solution,

409
00:26:40 --> 00:26:46
more accurately,
for heaven's sake,

410
00:26:43 --> 00:26:49
don't take such big steps.
Take small steps,

411
00:26:50 --> 00:26:56
and then it will follow better.
All right, let's draw a

412
00:26:59 --> 00:27:05
picture.
Excuse me.

413
00:27:02 --> 00:27:08
My little box of treasures,
here.

414
00:27:08 --> 00:27:14
[LAUGHTER]
So, use a smaller step size.

415
00:27:13 --> 00:27:19
And the picture,
roughly, which is going to

416
00:27:17 --> 00:27:23
justify that,
will look like this.

417
00:27:21 --> 00:27:27
If the solution curve looks
like this, then with a big step

418
00:27:26 --> 00:27:32
size, I'm liable to have
something that looks like that.

419
00:27:33 --> 00:27:39
But, if I take a smaller step
size, suppose I have the step

420
00:27:38 --> 00:27:44
size.
How's it going to look,

421
00:27:40 --> 00:27:46
then?
Well, I better switch to a

422
00:27:43 --> 00:27:49
different color.
If I have the step size,

423
00:27:47 --> 00:27:53
I'll get a littler,
goes like that.

424
00:27:50 --> 00:27:56
And now it's following closer.
Of course, I'm stacking the

425
00:27:55 --> 00:28:01
deck, but see how close it
follows?

426
00:28:00 --> 00:28:06
I'm definitely not to be
trusted on this.

427
00:28:02 --> 00:28:08
Okay, let's do the opposite,
make really big steps.

428
00:28:05 --> 00:28:11
Suppose instead of the yellow
ones I used the green one of

429
00:28:08 --> 00:28:14
double step size.
Well, what would have happened

430
00:28:11 --> 00:28:17
then?
Well, I've started out,

431
00:28:13 --> 00:28:19
but now I've gone all the way
to there.

432
00:28:15 --> 00:28:21
And now, on my way up,
of course, it has a little

433
00:28:18 --> 00:28:24
further to go.
But, if for some reason,

434
00:28:21 --> 00:28:27
I stop there,
you could see,

435
00:28:22 --> 00:28:28
I would be still lower.
In other words,

436
00:28:24 --> 00:28:30
the bigger the steps size,
the more the error.

437
00:28:29 --> 00:28:35
And, where are the errors that
we are talking about?

438
00:28:33 --> 00:28:39
Well, the way to think of the
errors, this is the error,

439
00:28:38 --> 00:28:44
that number the error.
You can make it positive,

440
00:28:42 --> 00:28:48
negative, or just put it
automatically an absolute value

441
00:28:47 --> 00:28:53
sign around it.
That's not so important.

442
00:28:51 --> 00:28:57
So, in other words,
the conclusion is,

443
00:28:54 --> 00:29:00
that the error e,
the difference between the true

444
00:28:59 --> 00:29:05
value that I should have gotten,
and the Euler value that the

445
00:29:04 --> 00:29:10
calculation produced,
that the error e,

446
00:29:08 --> 00:29:14
depends on the step size.
Now, how does it depend on the

447
00:29:14 --> 00:29:20
step size?
Well, it's impossible to give

448
00:29:17 --> 00:29:23
an exact formula,
but there's an approximate

449
00:29:20 --> 00:29:26
answer, which is,
by and large,

450
00:29:22 --> 00:29:28
true.
So, the answer is,

451
00:29:24 --> 00:29:30
e is going to be a function of
h.

452
00:29:27 --> 00:29:33
What function?
Well, asymptotically,

453
00:29:30 --> 00:29:36
which is another way of putting
quotation marks around,

454
00:29:34 --> 00:29:40
what did I say?
It's going to be a constant,

455
00:29:37 --> 00:29:43
some constant,
times H.

456
00:29:40 --> 00:29:46
[LAUGHTER]
It looks like this,

457
00:30:12 --> 00:30:18
and for this reason it's called
a first order,

458
00:30:17 --> 00:30:23
the Euler is a first-order
method.

459
00:30:21 --> 00:30:27
And now, first-order does not
refer to the first order of the

460
00:30:28 --> 00:30:34
differential equation.
It's not the first order

461
00:30:34 --> 00:30:40
because it's y prime equals f of
(x, y).

462
00:30:38 --> 00:30:44
The first order means the fact

463
00:30:41 --> 00:30:47
that h occurs to the first
power.

464
00:30:44 --> 00:30:50
The way people usually say this
is since the normal way of

465
00:30:49 --> 00:30:55
decreasing the step size,
as you'll see as is you try to

466
00:30:53 --> 00:30:59
use a computer visual that deals
with the Euler method,

467
00:30:58 --> 00:31:04
which I highly recommend,
by the way, so highly

468
00:31:02 --> 00:31:08
recommended that you have to do
it, is that the way to say it,

469
00:31:07 --> 00:31:13
each new step has the step
size.

470
00:31:12 --> 00:31:18
That's the usual way to do it.
If you have the step size,

471
00:31:20 --> 00:31:26
since this is a constant,
if I have the step size,

472
00:31:27 --> 00:31:33
I have the error,
approximately.

473
00:31:33 --> 00:31:39
Have the step size,
have the error.

474
00:31:36 --> 00:31:42
That tells you how the error
varies with step size for

475
00:31:42 --> 00:31:48
Euler's method.
Please understand,

476
00:31:46 --> 00:31:52
that's what people say,
and please understand the

477
00:31:51 --> 00:31:57
grammatical construction.
Since everyone in the math

478
00:31:56 --> 00:32:02
department has a cold these days
except me for the moment,

479
00:32:03 --> 00:32:09
everyone goes around chanting
this mantra.

480
00:32:09 --> 00:32:15
This is totally irrelevant.
This whole mantra,

481
00:32:13 --> 00:32:19
feed a cold,
starve a fever.

482
00:32:15 --> 00:32:21
And if you asked them what it
means, they say eat a lot if you

483
00:32:21 --> 00:32:27
have a cold.
And if you have a fever,

484
00:32:24 --> 00:32:30
don't eat very much,
which is not what it means at

485
00:32:28 --> 00:32:34
all.
Grammatically,

486
00:32:30 --> 00:32:36
it's exactly the same
construction as this.

487
00:32:33 --> 00:32:39
What this means is if you have
the step size,

488
00:32:37 --> 00:32:43
you will have the error.
That's what feed a cold,

489
00:32:40 --> 00:32:46
starve a fever means.
And, remember this for the rest

490
00:32:44 --> 00:32:50
of your life.
If you feed a cold,

491
00:32:46 --> 00:32:52
if you eat too much when you
have a cold, you will get a

492
00:32:50 --> 00:32:56
fever and end up still having to
starve yourself because,

493
00:32:54 --> 00:33:00
of course, nobody,
when you have a fever,

494
00:32:57 --> 00:33:03
nobody feels like eating,
so they don't eat anything.

495
00:33:02 --> 00:33:08
All right, you got that?
Good.

496
00:33:06 --> 00:33:12
I want all of you to go home
and tell that to your mothers.

497
00:33:14 --> 00:33:20
You know, that's the way we
always used to speak.

498
00:33:21 --> 00:33:27
Grimmer ones:
spare the rod,

499
00:33:25 --> 00:33:31
spoil the child does not mean
that you should not hit your

500
00:33:33 --> 00:33:39
kid.
It means that if you fail to

501
00:33:39 --> 00:33:45
hit your kid,
he or she will be spoiled,

502
00:33:44 --> 00:33:50
whatever that means.
So, you don't want to do that.

503
00:33:50 --> 00:33:56
I guess the mantra today would
be, I don't know.

504
00:33:56 --> 00:34:02
Okay, so the first line of
defense is simply to keep having

505
00:34:03 --> 00:34:09
the step size in Euler.
And, what people do is,

506
00:34:08 --> 00:34:14
if they don't want to use
anything better than Euler's

507
00:34:11 --> 00:34:17
method, is you keep having the
step size until the curve

508
00:34:15 --> 00:34:21
doesn't seem to change anymore.
And then you say,

509
00:34:18 --> 00:34:24
well, that must be the
solution.

510
00:34:20 --> 00:34:26
And, I asked you on the
problems set,

511
00:34:22 --> 00:34:28
how much would you continue to
have to have the step size in

512
00:34:26 --> 00:34:32
order for that good thing to
happen?

513
00:34:30 --> 00:34:36
However, there are more
efficient methods which get the

514
00:34:35 --> 00:34:41
results faster.
So if that's our good method,

515
00:34:39 --> 00:34:45
let's call this our still
better method.

516
00:34:43 --> 00:34:49
The better methods aim at being
better.

517
00:34:47 --> 00:34:53
They keep the same idea as
Euler's method,

518
00:34:51 --> 00:34:57
but they say,
look, let's try to improve that

519
00:34:55 --> 00:35:01
slope, An.
In other words,

520
00:34:59 --> 00:35:05
since the slope,
An, that we start with is

521
00:35:01 --> 00:35:07
guaranteed to be wrong if the
curve is convex or concave,

522
00:35:05 --> 00:35:11
can we somehow correct it?
So, for example,

523
00:35:08 --> 00:35:14
instead of immediately aiming
there, can't we somehow aim it

524
00:35:12 --> 00:35:18
so that by luck,
we just, at the next step just

525
00:35:15 --> 00:35:21
lands us back on the curve
again?

526
00:35:17 --> 00:35:23
In other words,
with sort of looking for the

527
00:35:20 --> 00:35:26
short path, a shortcut path,
which by good luck will end us

528
00:35:24 --> 00:35:30
up back on the curve again.
And, all the simple

529
00:35:27 --> 00:35:33
improvements on the Euler
method, and they are the most

530
00:35:31 --> 00:35:37
stable in ways to solve
differential equations

531
00:35:34 --> 00:35:40
numerically, aim at finding a
better slope.

532
00:35:39 --> 00:35:45
So, they find a better value
for a better slope,

533
00:35:43 --> 00:35:49
find a better value than An.
Try to improve that slope that

534
00:35:48 --> 00:35:54
you found.
Now, once you have the idea

535
00:35:52 --> 00:35:58
that you should look for a
better slope,

536
00:35:55 --> 00:36:01
it's not very difficult to see
what, in fact,

537
00:35:59 --> 00:36:05
you should try.
Again, I think most of you

538
00:36:03 --> 00:36:09
would say, hey,
I would have thought of that.

539
00:36:07 --> 00:36:13
And, you would be closer in
time, since these methods were

540
00:36:11 --> 00:36:17
only found about around the turn
of the last century is when I

541
00:36:15 --> 00:36:21
place them, mostly by some
German mathematicians interested

542
00:36:20 --> 00:36:26
in solving equations
numerically.

543
00:36:22 --> 00:36:28
All right, so what is the
better method?

544
00:36:25 --> 00:36:31
Our better slope,
what should we look for in our

545
00:36:28 --> 00:36:34
better slope?
Well, the simplest procedure

546
00:36:33 --> 00:36:39
is, once again,
we are starting from there,

547
00:36:36 --> 00:36:42
and the Euler slope would be
the same as a line element.

548
00:36:41 --> 00:36:47
So, the line element looks like
this.

549
00:36:44 --> 00:36:50
And, our yellow slope,
A, and I'll still continue to

550
00:36:48 --> 00:36:54
call it An, goes like that,
gets to here.

551
00:36:51 --> 00:36:57
Okay, now if it were convex,
if the curve were convex,

552
00:36:55 --> 00:37:01
this would be too low.
And therefore,

553
00:36:58 --> 00:37:04
the next step would be,
I'm going to draw this next

554
00:37:02 --> 00:37:08
step in pink.
Well, let's continue in here,

555
00:37:07 --> 00:37:13
would be going up like that.
I'll call this Bn,

556
00:37:10 --> 00:37:16
just because it's the next step
of Euler's method.

557
00:37:14 --> 00:37:20
It could be called An prime
or something like that.

558
00:37:19 --> 00:37:25
But this will do.
And now what you do is,

559
00:37:22 --> 00:37:28
let me put an arrow on it to
indicate parallelness,

560
00:37:26 --> 00:37:32
go back to the beginning,
draw this parallel to Bn.

561
00:37:31 --> 00:37:37
So, here is Bn.
Again, just a line of that same

562
00:37:35 --> 00:37:41
slope.
And now, what you should use as

563
00:37:38 --> 00:37:44
the simplest improvement on
Euler's method,

564
00:37:42 --> 00:37:48
is take the average of these
two because that's more likely

565
00:37:48 --> 00:37:54
to hit the curve than An will,
which is sure to be too low if

566
00:37:54 --> 00:38:00
the curve is convex.
In other words,

567
00:37:57 --> 00:38:03
use this instead.
Use that.

568
00:37:59 --> 00:38:05
So, this is our better slope.
Okay, what will we call that

569
00:38:06 --> 00:38:12
slope?
We don't have to call it

570
00:38:08 --> 00:38:14
anything.
What were the equations for the

571
00:38:11 --> 00:38:17
method be?
Well, x n plus one

572
00:38:14 --> 00:38:20
is gotten by adding the step
size.

573
00:38:17 --> 00:38:23
So, here's my step size just as
it was before.

574
00:38:20 --> 00:38:26
Just as it was before,
the new thing is how to get the

575
00:38:25 --> 00:38:31
new value of y.
So, y n plus one

576
00:38:28 --> 00:38:34
should be the old yn,
plus h times not this crummy

577
00:38:32 --> 00:38:38
slope, An, but the better,
the pink slope.

578
00:38:37 --> 00:38:43
What's the formula for the pink
slope?

579
00:38:39 --> 00:38:45
Well, let's do it in two steps.
It's the average of An and Bn.

580
00:38:44 --> 00:38:50
Hey, but you didn't tell me,
or I didn't tell you what Bn

581
00:38:48 --> 00:38:54
was.
So, you now must tell the

582
00:38:50 --> 00:38:56
computer, oh yes,
by the way, you remember that

583
00:38:54 --> 00:39:00
An was what it always was.
The interesting thing is,

584
00:38:58 --> 00:39:04
what is Bn?
Well, to get Bn,

585
00:39:01 --> 00:39:07
Bn is the slope of the line
element at this new point.

586
00:39:05 --> 00:39:11
Now, what am I going to call
that new point?

587
00:39:08 --> 00:39:14
I don't want to call this y
value, y n plus one,

588
00:39:12 --> 00:39:18
because that's,
it's this up here that's going

589
00:39:16 --> 00:39:22
to be the y n plus one.
All this is,

590
00:39:18 --> 00:39:24
is a temporary value used to
make another calculation,

591
00:39:22 --> 00:39:28
which will then be combined
with the previous calculations

592
00:39:27 --> 00:39:33
to get the right value.
Therefore, give it a temporary

593
00:39:31 --> 00:39:37
name.
That point, we'll call it,

594
00:39:34 --> 00:39:40
it's not going to be the final,
the real y n plus one.

595
00:39:38 --> 00:39:44
We'll call it y n plus one

596
00:39:40 --> 00:39:46
twiddle, y n plus one temporary.
And, what's the formula for it?

597
00:39:44 --> 00:39:50
Well, it's just going to be
what the original Euler formula;

598
00:39:48 --> 00:39:54
it's going to be y n plus what
you would have gotten if you had

599
00:39:52 --> 00:39:58
calculated, in other words,
it's the point that the Euler

600
00:39:56 --> 00:40:02
method produced,
but it's not,

601
00:39:57 --> 00:40:03
finally, the point that we
want.

602
00:40:01 --> 00:40:07
Now, do I have to say anything
else?

603
00:40:04 --> 00:40:10
Yeah, I didn't tell the
computer what Bn was.

604
00:40:08 --> 00:40:14
Okay, Bn is the slope of the
direction field at the point n

605
00:40:14 --> 00:40:20
plus one.
And the computer knows what

606
00:40:19 --> 00:40:25
that is.
And, this point,

607
00:40:21 --> 00:40:27
y n plus one temporary.

608
00:40:24 --> 00:40:30
So, you make a temporary choice
of this, calculate that number,

609
00:40:31 --> 00:40:37
and then go back,
and as it were,

610
00:40:34 --> 00:40:40
correct that value to this
value by using this better

611
00:40:39 --> 00:40:45
slope.
Now, that's all there is to the

612
00:40:44 --> 00:40:50
method, except I didn't give you
its name.

613
00:40:48 --> 00:40:54
Well, it has three names,
four names in fact.

614
00:40:52 --> 00:40:58
Which one shall I give you?
I don't care.

615
00:40:56 --> 00:41:02
Okay, the shortest name is
Heun's method.

616
00:41:00 --> 00:41:06
But nobody pronounces that
correctly.

617
00:41:05 --> 00:41:11
So, it's Heun's method.
It's called,

618
00:41:09 --> 00:41:15
also, the Improved Euler
method.

619
00:41:13 --> 00:41:19
It's called Modified Euler,
very expressive word,

620
00:41:19 --> 00:41:25
Modified Euler's method.
And, it's also called RK2.

621
00:41:25 --> 00:41:31
I'm sure you'll like that name
best.

622
00:41:31 --> 00:41:37
It has a Star Wars sort of
sound.

623
00:41:33 --> 00:41:39
RK stands for Runge-Kutta,
and the reason for the two is

624
00:41:38 --> 00:41:44
not that it uses,
well, it is that it uses two

625
00:41:42 --> 00:41:48
slopes, but the real reason for
the two is that it is a

626
00:41:46 --> 00:41:52
second-order method.
So, that's the most important

627
00:41:50 --> 00:41:56
thing to put down about it.
It's a second-order method,

628
00:41:55 --> 00:42:01
whereas Euler's was only a
first-order method.

629
00:42:00 --> 00:42:06
So, Heun's method,
or RK2, let's write it,

630
00:42:03 --> 00:42:09
is the shortest thing to write,
is a second-order method,

631
00:42:08 --> 00:42:14
meaning that the error varies
with the step size like some

632
00:42:13 --> 00:42:19
constant, it will not be the
same as the constant for Euler's

633
00:42:19 --> 00:42:25
method, times h squared.

634
00:42:22 --> 00:42:28
That's a big saving because it
now means that if you have the

635
00:42:28 --> 00:42:34
step size, you're going to
decrease the error by a factor

636
00:42:33 --> 00:42:39
of one quarter.
You will quarter the error.

637
00:42:38 --> 00:42:44
Now, you say,
hey, why should anyone use

638
00:42:40 --> 00:42:46
anything else?
Well, think a little second.

639
00:42:44 --> 00:42:50
The real thing which determines
how slowly one of these methods

640
00:42:48 --> 00:42:54
run is you look at the hardest
step of the method and ask how

641
00:42:53 --> 00:42:59
long does the computer take,
how many of those hardest steps

642
00:42:57 --> 00:43:03
are there?
Now, the answer is,

643
00:42:59 --> 00:43:05
the hardest step is always the
evaluation of the function

644
00:43:04 --> 00:43:10
because the functions that are
common use are not x squared

645
00:43:08 --> 00:43:14
minus y squared.
They take half a page and have,

646
00:43:14 --> 00:43:20
as coefficients,
you know, ten decimal place

647
00:43:17 --> 00:43:23
numbers, whatever the engineers
doing it, whatever their

648
00:43:22 --> 00:43:28
accuracy was.
So, the thing that controls how

649
00:43:25 --> 00:43:31
long a method runs is how many
times the slope,

650
00:43:29 --> 00:43:35
the function,
must be evaluated.

651
00:43:33 --> 00:43:39
For Euler, I only have to
evaluate it once.

652
00:43:37 --> 00:43:43
Here, I have to evaluate it
twice.

653
00:43:40 --> 00:43:46
Now, roughly speaking,
the number of function

654
00:43:44 --> 00:43:50
evaluations will each give you
the exponent.

655
00:43:48 --> 00:43:54
The method that's called
Runge-Kutta fourth order will

656
00:43:53 --> 00:43:59
require four evaluations of
slope, but the accuracy will be

657
00:43:59 --> 00:44:05
like h to the fourth:
very accurate.

658
00:44:05 --> 00:44:11
You have the step size,
and it goes down by a factor of

659
00:44:10 --> 00:44:16
16.
Great.

660
00:44:10 --> 00:44:16
But you had to evaluate the
slope four times.

661
00:44:14 --> 00:44:20
Suppose, instead,
you have four times this thing.

662
00:44:19 --> 00:44:25
And, what would you have done?
You would have decreased it to

663
00:44:24 --> 00:44:30
1/16th to what it was.
You still would increase the

664
00:44:29 --> 00:44:35
number of function evaluations
you needed by four,

665
00:44:34 --> 00:44:40
and you would have decreased
the error by a 16th.

666
00:44:40 --> 00:44:46
So, in some sense,
it really doesn't matter

667
00:44:43 --> 00:44:49
whether you use a very fancy
method, which requires more

668
00:44:47 --> 00:44:53
function evaluations.
That's true.

669
00:44:50 --> 00:44:56
The error goes down faster,
but you are having to more work

670
00:44:54 --> 00:45:00
to get it.
So, anyway, nothing is free.

671
00:44:57 --> 00:45:03
Now, there is an RK4.
I think I'll skip that,

672
00:45:01 --> 00:45:07
since I wouldn't dare to ask
you any questions about it.

673
00:45:06 --> 00:45:12
But, let me just mention it,
at least, because it's the

674
00:45:10 --> 00:45:16
standard.
It uses four evaluations.

675
00:45:13 --> 00:45:19
It's the standard method,
if you don't want to do

676
00:45:17 --> 00:45:23
anything fancier.
It's rather inefficient,

677
00:45:20 --> 00:45:26
but it's very accurate,
standard method,

678
00:45:23 --> 00:45:29
accurate, and you'll see when
you use the programs,

679
00:45:27 --> 00:45:33
it's, in fact,
a program which is drawing

680
00:45:30 --> 00:45:36
those curves,
the numerical method which

681
00:45:33 --> 00:45:39
draws all those curves that you
believe in on the computer

682
00:45:38 --> 00:45:44
screen is the RK4 method.
The Runge-Kutta,

683
00:45:43 --> 00:45:49
I should give them their names.
Runge-Kutta,

684
00:45:47 --> 00:45:53
fourth-order method.
Two mathematicians,

685
00:45:51 --> 00:45:57
I believe both German
mathematicians around the turn

686
00:45:55 --> 00:46:01
of the last century,
Runge-Kutta fourth-order method

687
00:46:00 --> 00:46:06
requires four slopes,
requires you to calculate four

688
00:46:05 --> 00:46:11
slopes.
I won't bother telling you what

689
00:46:09 --> 00:46:15
you do, but it's a procedure
like that.

690
00:46:11 --> 00:46:17
It's just a little bit more
elaborate.

691
00:46:14 --> 00:46:20
And you take two of these,
you make up a weighted average

692
00:46:17 --> 00:46:23
for the super slope.
You use weighted average.

693
00:46:20 --> 00:46:26
What should I divide that by to
get the right...?

694
00:46:23 --> 00:46:29
Six.

695
00:46:24 --> 00:46:30
Why six?
Well, because if all these

696
00:46:26 --> 00:46:32
numbers were the same,
I'd want it to come out to be

697
00:46:29 --> 00:46:35
whatever that common value was.
Therefore, in a weighted

698
00:46:35 --> 00:46:41
average, you must always divide
by the sum of the coefficients.

699
00:46:40 --> 00:46:46
So, this is the super-slope.
And, if you plug that

700
00:46:44 --> 00:46:50
super-slope into here,
you will be using the

701
00:46:47 --> 00:46:53
Runge-Kutta method,
and get the best possible

702
00:46:51 --> 00:46:57
results.
Now, I wanted to spend the last

703
00:46:54 --> 00:47:00
three minutes talking about
pitfalls of numerical

704
00:46:58 --> 00:47:04
computation in general.
One pitfall I am leaving you on

705
00:47:04 --> 00:47:10
the homework to discover for
yourself.

706
00:47:08 --> 00:47:14
Don't worry,
it won't cause you any grief.

707
00:47:12 --> 00:47:18
It'll just destroy your faith
in these things for the rest of

708
00:47:18 --> 00:47:24
your life, which is probably a
good thing.

709
00:47:22 --> 00:47:28
So, pitfalls,
number one, you find,

710
00:47:25 --> 00:47:31
you'll find.
Let me talk,

711
00:47:27 --> 00:47:33
instead, briefly about number
two, which I am not giving you

712
00:47:33 --> 00:47:39
an exercise in.
Number two is illustrated by

713
00:47:37 --> 00:47:43
the following equation.
What could be simpler?

714
00:47:40 --> 00:47:46
This is a very bad equation to
try to solve numerically.

715
00:47:44 --> 00:47:50
Now, why?
Well, because if I separate

716
00:47:47 --> 00:47:53
variables, why don't I save a
little time?

717
00:47:49 --> 00:47:55
I'll just tell you what the
solution is, okay?

718
00:47:53 --> 00:47:59
You obviously separate
variables.

719
00:47:55 --> 00:48:01
Maybe you can do it in your
head.

720
00:47:57 --> 00:48:03
The solution will be,
the solutions will have an

721
00:48:00 --> 00:48:06
arbitrary constant in them,
and they won't be very

722
00:48:03 --> 00:48:09
complicated. They will be 
one over c minus x.

723
00:48:08 --> 00:48:14
C is an arbitrary constant,

724
00:48:10 --> 00:48:16
and as you give different
values, you get,

725
00:48:13 --> 00:48:19
now, what do those guys look
like?

726
00:48:15 --> 00:48:21
Okay, so here I am.
I start out at the point,

727
00:48:18 --> 00:48:24
one.
And, I start out,

728
00:48:19 --> 00:48:25
I tell the computer,
compute for me the value of the

729
00:48:23 --> 00:48:29
solution at one starting out at
one.

730
00:48:25 --> 00:48:31
And, it computes and computes a
little while.

731
00:48:28 --> 00:48:34
But the solution,
how does this curve actually

732
00:48:31 --> 00:48:37
look?
So, in other words,

733
00:48:34 --> 00:48:40
suppose I say that y of zero
equals one.

734
00:48:38 --> 00:48:44
Find me y of two.
In other words,

735
00:48:41 --> 00:48:47
take a nice small step size.
Use the Runge-Kutta

736
00:48:44 --> 00:48:50
fourth-order method.
Calculate a little bit,

737
00:48:48 --> 00:48:54
and tell me,
I just want to know what y of

738
00:48:51 --> 00:48:57
two is.
Well, what is y of two?

739
00:48:53 --> 00:48:59
Well, unfortunately,
how does that curve look?

740
00:48:56 --> 00:49:02
The curve looks like this.
At that point,

741
00:49:01 --> 00:49:07
it drops to infinity in a
manner of speaking,

742
00:49:04 --> 00:49:10
and then sort of comes back up
again like that.

743
00:49:07 --> 00:49:13
What is the value of y?
This is the point,

744
00:49:11 --> 00:49:17
one.
What is the value of y of two?

745
00:49:13 --> 00:49:19
Is it here?

746
00:49:15 --> 00:49:21
Is it this?
Well, I don't know,

747
00:49:17 --> 00:49:23
but I do know that the computer
will not find it.

748
00:49:20 --> 00:49:26
The computer will follow this
along, and get lost in eternity,

749
00:49:25 --> 00:49:31
in infinity,
and see no reason whatever why

750
00:49:28 --> 00:49:34
it should start again on this
branch of the curve.

751
00:49:34 --> 00:49:40
Okay, well, can't we predict
that that's going to happen

752
00:49:38 --> 00:49:44
somehow, avoid what I should
have.

753
00:49:40 --> 00:49:46
The whole difficulty is,
this is called a singular

754
00:49:44 --> 00:49:50
point.
The solution has a singularity,

755
00:49:47 --> 00:49:53
in other words,
a single place where it goes to

756
00:49:51 --> 00:49:57
infinity or becomes
discontinuous,

757
00:49:53 --> 00:49:59
maybe as a jump discontinuity.
It has a singularity at x

758
00:49:57 --> 00:50:03
equals c.
This, in particular,

759
00:50:00 --> 00:50:06
at x equals one here,
but from the differential

760
00:50:04 --> 00:50:10
equation, where is that c?
There is no way of predicting

761
00:50:10 --> 00:50:16
it.
Each solution,

762
00:50:11 --> 00:50:17
in other words,
to this differential equation,

763
00:50:15 --> 00:50:21
has its own,
private singularity,

764
00:50:17 --> 00:50:23
which only it knows about,
and where it's going to blow

765
00:50:21 --> 00:50:27
up, and there's no way of
telling from the differential

766
00:50:25 --> 00:50:31
equation where that's going to
be.

767
00:50:28 --> 00:50:34
That's one of the things that
makes numerical calculation

768
00:50:32 --> 00:50:38
difficult, when you cannot
predict where things are going

769
00:50:37 --> 00:50:43
to go bad in advance.