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PROFESSOR STRANG: I'm open for
questions as always on every

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aspect, or comments on every
aspect of the course. yeah?.

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The MATLAB problem, yes.
 

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I better remember what
that MATLAB problem is.

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So it's solving the equation
-u''+Vu' equal some delta

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function halfway along?
 

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And I chose, boundary
conditions like those so that

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this gets replaced by a
matrix K/(delta x squared).

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And this term is V times
some center difference.

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I don't know whether
I should use C.

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We called C earlier in the
book, C was a circulant.

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This is a centered first
difference over delta x

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equal right-hand side.
 

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All that multiplies the
discrete solution u.

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I didn't get that very
well for the camera.

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Maybe I'll say it again.
 

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We have the second difference
that corresponds to -u''.

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And V times the first
difference that

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corresponds to Vu'.
 

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00:02:12 --> 00:02:18
 
 

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So the physical problem is,
what happens is V gets larger.

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V grows.
 

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How does the solution
change as V increases?

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So it's a very
practical problem.

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The V measures the importance
of this convection, the

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velocity of the fluid.
 

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Then n, which is, well maybe
delta x is 1/(n+1), maybe.

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So that we would have n
interior points and we

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know the boundary values.
 

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So the question is how do we
interpret this increasing n?

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I guess we expect more and
more accuracy, right?

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As n increases, delta
x getting smaller.

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Our idea is that this should
be a better and better

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approximation to the
differential equation.

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The question about the
eigenvalues of this matrix, I

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don't know exactly the answer.
 

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So the eigenvalues, did you
discover that the eigenvalues

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suddenly, they change
their real, for small

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values of V and n.
 

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But then there's some point at
which they become complex.

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And a point that involves
both V and delta x

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actually, V and n.
 

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It's kind of interesting.
 

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I guess at this moment,
interesting is the key word.

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So what is the physics
going on here?

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So I have a 1-D flow and
it's blocked by these

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boundary conditions.
 

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Are sort of blocking
it at both ends.

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And I'm looking for
a steady state.

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And I'm feeding in
this source here.

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So I'm feeding in a
source halfway along.

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So I think that would be right.
 

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Now what's going to
happen roughly?

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This term is, I think,
flowing to the right.

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So it's going to carry,
suppose this was smoke or

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particles or whatever.
 

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This is like an environmental
differential equation or a

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chemical engineering equation.
 

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Just oh, and more and
more applications.

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So I think of it as this term
is carrying the flow with it.

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Like, that way.
 

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So I'm expecting your solutions
to be larger on this side.

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And how do we get any
action at all on the left?

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That comes from the diffusion.
 

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The particles, when they enter,
get carried away, and faster

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and faster as V increases.
 

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But at the same time they
diffuse, they bounce

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back and forth.
 

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And some bit of it
goes this way.

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But not a lot.
 

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I'm guessing that the solution
would have a profile that

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would be kind of small.
 

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And then there's whatever
jump there has to be here,

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and then larger there.
 

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Oh, but then I have
to get it to zero.

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It's a strange situation.
 

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And maybe I'll take this
chance to mention it.

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What happens as V gets
really, really large?

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You could say ok, as V
gets extremely large,

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forget that term.
 

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This is the important term.
 

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Vu' equals that.
 

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Which we could easily solve.
 

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But this equation with
just this term is only

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first order, right?
 

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It only has a first derivative.
 

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00:07:01 --> 00:07:04
And how many boundary
conditions would we expect to

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have if I gave you, if this
wasn't here and I gave you

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just a first order equation?
 

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One.
 

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And we've got two.
 

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Which we're imposing.
 

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So this, sort of, limit as
V increases, it produces

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something called a
boundary layer.

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The solution is forced
to satisfy these

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boundary conditions.
 

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One of them, it's
quite happy about.

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The other one, it didn't
really want to satisfy.

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It only satisfied them
because, I had two

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originally with this part.
 

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But as this takes over, the
struggle to satisfy that second

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boundary condition that it
really doesn't want to satisfy

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is resolved by something, a
layer that just makes a, sort

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of, exponential correction
at the boundary to get to

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where it's supposed to go.
 

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So, anyway.
 

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This is a model problem
that has boundary layers.

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Those are terrifically
important in aerodynamics,

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you have layers around
the actual aircraft.

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So lots of physics and
computational science is hiding

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in this type of example.
 

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So those are a few words.
 

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But not an answer
to your question.

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I guess I'm happy if you, for
example, let me know where

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the eigenvalues change
from real to complex.

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I mean, it happens, but you can
probably, if you look at the

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matrix, you can see what
happens at the point where

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that change takes place.
 

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That's some comments.
 

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And with the MATLAB, pure
MATLAB part, I guess I'm

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hoping, and the graders are
hoping, that by providing a

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code we'll get a pretty
systematic set of answers for

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the requested V equal, what
was it, three and 12

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maybe, or something.
 

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And the requested n(delta x)'s.
 

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00:09:25 --> 00:09:29
 
 

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So I'm hoping that everybody's
graph is going to look pretty

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similar for those requests.
 

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And then if you could do a
little exploration yourself and

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make that a fifth page, or
really less than a page,

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about take V larger.
 

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Take n larger.
 

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What happens?
 

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I'm very happy.
 

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Or any direction.
 

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I mean, just think of it as
a mini-project to do the

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requested ones and then to do
a little experimentation.

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I'll just be interested
about anything you observe.

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So there's no right
answer to that part.

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Is that any help
with the MATLAB?

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00:10:33 --> 00:10:38
Let me just say, since you
showed up today, if you have

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trouble with the MATLAB and you
need Peter's help at noon

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Friday it would be ok to turn
in the MATLAB later on Friday.

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I shouldn't say that.
 

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But I just did.
 

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So there you are.
 

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Right.
 

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00:11:00 --> 00:11:07
I mean, this course, as you've
begun to see, I know that you

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have lots of demands
on your time.

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And sometimes you
have job interviews.

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All sorts of stuff.
 

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Conferences that
you have to go to.

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We deal with that.
 

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So just give me the homeworks
as soon as you can.

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If you're ready Friday at
class time, that's perfect.

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If are stuck on MATLAB and you
want to go to Peter's

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discussion, which follows the
Friday class in here,

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that's fine too.
 

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So that's some thoughts
about the MATLAB and

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it's partly open-ended.
 

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What about lots of other
things in this course?

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Homework came in.
 

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How was the homework?
 

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Maybe I just take
this chance to ask.

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Was it a reasonable length?
 

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The homework three, the pset?
 

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00:12:07 --> 00:12:09
 
 

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Not too bad?
 

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I will be able to post
solutions because several

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people are contributing typed
solutions that could go on on

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the math.mit.edu/cse website.
 

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Or they could also go
on our 18.085 website

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for this semester.
 

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Good.
 

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Ok, help me out with
some questions.

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Thanks.
 

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So the question is about
element matrices.

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In lecture 8, I guess it was,
where we were assembling this,

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I used the word that's used by
finite element people,

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we were assembling K.
 

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And one way to do it was to
multiply the three matrices.

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But that's not how
it's actually done.

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It's actually assembled
out of small matrices.

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Maybe small k would
be the right.

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So this would be a
k_element out of smaller

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element matrices.
 

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So for example, what's the
element matrix for, one for

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each spring in this particular
problem of springs and masses.

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So let me draw some springs,
some masses, some springs,

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another mass, more springs,
fixed, not fixed, whatever,

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maybe fixed-free the way
I've drawn it there.

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So here's a spring with
spring constant c_2.

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And we could look at the
contribution to the whole

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matrix coming from this
piece of the problem.

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This was actually, the finite
element method has a wonderful

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history of people, and it had
different names way back, of

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people seeing the structure as
broken in pieces and then

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connected together.
 

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And then what did a
typical piece look like?

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So a typical piece there, well,
you let me just write down what

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this matrix is going to be.
 

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The little element matrix
is coming from spring two.

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So this would be, like
element two will be,

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it'll have a c_2 outside.
 

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I'll put the c_2 outside.
 

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00:15:12 --> 00:15:18
And then inside will
be a little this guy.

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And we can talk more
about why it's that one.

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00:15:23 --> 00:15:30
But just to have the element
matrix there on the board.

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00:15:30 --> 00:15:38
So my claim is that this is
a small piece of the big K.

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So the big K matrix, what's the
size of the big K, of K itself?

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Three by three in this
case, yeah, three masses.

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So it'll be three by three.
 

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00:15:50 --> 00:15:57
So I'm thinking that this
spring which connects mass one

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to mass two, so it's only going
to be like, a two by two piece,

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00:16:05 --> 00:16:11
a little local piece, you could
say, that that little k fits in

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00:16:11 --> 00:16:20
this, is assembled into the, I
better call it k_2, right?

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00:16:20 --> 00:16:26
So it's a little k that sits up
in this two by two block and

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doesn't contribute to the rest.
 

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Then let's just draw the
rest of the picture.

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So this would produce an
element matrix that looks

230
00:16:35 --> 00:16:39
just the same, that's
the beauty of this.

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That all the elements, apart
from change in the spring

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constant, look the same.
 

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So there'd be a little k_3.
 

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And where will it go?
 

235
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This is the whole
core to the point.

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00:16:55 --> 00:16:57
It'll go to the lower right.
 

237
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Down here?
 

238
00:17:00 --> 00:17:02
Overlapping, overlapping.
 

239
00:17:02 --> 00:17:09
Because this mass is attached
to that spring and to that one.

240
00:17:09 --> 00:17:12
So these little element
matrices, they overlap.

241
00:17:12 --> 00:17:18
And you just need, if you can
imagine the code that's going

242
00:17:18 --> 00:17:23
to do this, you need a list
of springs and a list of

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00:17:23 --> 00:17:26
masses and a list of the
connections between them.

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00:17:26 --> 00:17:29
And it'll sit in here
because it's two by two.

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So that's two by two, that's
two by two and in this

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00:17:33 --> 00:17:38
overlap entry will be a
c_2 from the upper box.

247
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It'll be a c_2+c_3 as we
discovered by direct

248
00:17:43 --> 00:17:45
multiplication.
 

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So that's that spring.
 

250
00:17:47 --> 00:17:51
Now what about this
first spring?

251
00:17:51 --> 00:17:52
So there's a little k_1.
 

252
00:17:54 --> 00:17:57
Now k_1 should look
the same as this.

253
00:17:57 --> 00:17:59
Except what?
 

254
00:17:59 --> 00:18:01
So it's going to be a
difference with this

255
00:18:01 --> 00:18:04
spring because?
 

256
00:18:04 --> 00:18:06
Because of this fixed.
 

257
00:18:06 --> 00:18:09
End.
 

258
00:18:09 --> 00:18:10
There's no mass zero.
 

259
00:18:12 --> 00:18:18
k_1 would normally sit up
here, but actually it's only

260
00:18:18 --> 00:18:21
going to be one by one.
 

261
00:18:21 --> 00:18:27
So the k_1 little element
matrix would look like

262
00:18:27 --> 00:18:31
c_1[1, -1; -1, 1].
 

263
00:18:31 --> 00:18:34
And then the boundary
conditions, knock those out.

264
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And an interesting point if
you're writing big code

265
00:18:39 --> 00:18:40
you have to decide.
 

266
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Do I, as I create k_1, this
little element matrix, do I

267
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pay attention to the
boundary conditions?

268
00:18:49 --> 00:18:54
And then k_1 would just be
that single number c_1

269
00:18:54 --> 00:18:56
which would sit there.
 

270
00:18:56 --> 00:19:01
So up there will be c_1 from
the k_1 matrix and c_2

271
00:19:01 --> 00:19:05
from the k_2 matrix.
 

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00:19:05 --> 00:19:08
That's the entry there.
 

273
00:19:08 --> 00:19:12
And then, as I say, in coding
finite elements, which you

274
00:19:12 --> 00:19:17
guys may do at some point,
there's a question.

275
00:19:17 --> 00:19:18
What's sufficient?
 

276
00:19:18 --> 00:19:24
Shall I pay attention to
the boundary in creating

277
00:19:24 --> 00:19:27
these element matrices
or shall I do it later?

278
00:19:27 --> 00:19:31
And the rule seems
to be, do it later.

279
00:19:31 --> 00:19:40
So what gets created in finite
elements is a, in our language,

280
00:19:40 --> 00:19:43
would be a free-free matrix.
 

281
00:19:43 --> 00:19:55
It's a matrix where even this
spring has a two by two piece.

282
00:19:55 --> 00:20:01
So what's the problem with
the free-free matrix?

283
00:20:01 --> 00:20:05
The free-free matrices, those
with no supports, those

284
00:20:05 --> 00:20:08
matrices will be singular.
 

285
00:20:08 --> 00:20:10
Right, singular.
 

286
00:20:10 --> 00:20:16
Because the vector of all ones,
if you multiply the K, the

287
00:20:16 --> 00:20:18
free-free matrix times the
vector of all ones,

288
00:20:18 --> 00:20:20
you get zero.
 

289
00:20:20 --> 00:20:23
The whole thing could shift.
 

290
00:20:23 --> 00:20:28
And will have other rigid
motions in two dimensions.

291
00:20:28 --> 00:20:31
Let's just think ahead here.
 

292
00:20:31 --> 00:20:38
What are the rigid motions, the
null space of K, you could say,

293
00:20:38 --> 00:20:41
for a two-dimensional truss.
 

294
00:20:41 --> 00:20:44
So I've got a bunch
of bars and springs.

295
00:20:44 --> 00:20:46
Think of a mattress.
 

296
00:20:46 --> 00:20:49
I'm in two dimensions, a
mattress is a bunch of springs

297
00:20:49 --> 00:20:54
connected in a 2-D grid.
 

298
00:20:54 --> 00:20:58
And say it's free at both ends.
 

299
00:20:58 --> 00:21:00
So what can I do to a mattress?
 

300
00:21:00 --> 00:21:01
Oh, my gosh!
 

301
00:21:01 --> 00:21:05
I didn't expect that
to be on videotape.

302
00:21:05 --> 00:21:06
So it's in the plane.
 

303
00:21:06 --> 00:21:09
We're in 2-D here.
 

304
00:21:09 --> 00:21:13
What can I do if there are
no boundary conditions?

305
00:21:13 --> 00:21:15
Well I can shift
it to the right.

306
00:21:15 --> 00:21:16
Right?
 

307
00:21:16 --> 00:21:18
That's our .
 

308
00:21:18 --> 00:21:20
What else can I do?
 

309
00:21:20 --> 00:21:21
I can rotate.
 

310
00:21:21 --> 00:21:23
And I can shift it
the other way.

311
00:21:23 --> 00:21:29
So there would be three rigid
motions for the 2-D problem.

312
00:21:29 --> 00:21:33
Two translations
and a rotation.

313
00:21:33 --> 00:21:35
And when you get up to three
dimensions, do you want to

314
00:21:35 --> 00:21:40
guess what's the number in 3-D?
 

315
00:21:40 --> 00:21:41
Six.
 

316
00:21:41 --> 00:21:42
Number six.
 

317
00:21:42 --> 00:21:46
Three translations and
rotations around three axes.

318
00:21:46 --> 00:21:54
So those, either one rigid
motion or three rigid motions

319
00:21:54 --> 00:21:59
or six rigid motions have to
get, the boundary conditions

320
00:21:59 --> 00:22:04
eventually have to remove
those, have to knock out rows

321
00:22:04 --> 00:22:05
and columns and remove them.
 

322
00:22:05 --> 00:22:13
But my point was just that
quite efficient to do it later.

323
00:22:13 --> 00:22:15
The picture is so clear here.
 

324
00:22:15 --> 00:22:19
So the actual matrix would
be four by four, the

325
00:22:19 --> 00:22:20
unreduced matrix.
 

326
00:22:20 --> 00:22:28
And then when we fix node zero
there, that would knock out

327
00:22:28 --> 00:22:31
that part and leave the three
by three that we want.

328
00:22:31 --> 00:22:38
So this is just discussion of
how to think of this matrix K.

329
00:22:38 --> 00:22:45
So the direct way to think
of it was as a product

330
00:22:45 --> 00:22:46
of big matrices.
 

331
00:22:46 --> 00:22:53
But in reality it's assembled
from these element pieces.

332
00:22:53 --> 00:22:56
And of course, our goal in
talking about finite elements

333
00:22:56 --> 00:23:00
will be to see that.
 

334
00:23:00 --> 00:23:04
It'll come up again, of course.
 

335
00:23:04 --> 00:23:06
Your good question led
me there, but did I

336
00:23:06 --> 00:23:11
answer the question?
 

337
00:23:11 --> 00:23:16
And maybe the way I mentioned
it in class was to notice

338
00:23:16 --> 00:23:20
that these guys, these
element matrices can be

339
00:23:20 --> 00:23:22
thought of this way.
 

340
00:23:22 --> 00:23:26
It is matrix multiplication,
but done differently.

341
00:23:26 --> 00:23:31
It's a column of this matrix
times the number C here

342
00:23:31 --> 00:23:33
times a row of this.
 

343
00:23:33 --> 00:23:38
So it's matrix multiplication,
columns times rows.

344
00:23:38 --> 00:23:49
So you can multiply AB, columns
of A times rows of B and

345
00:23:49 --> 00:23:55
then add over from one to n.
 

346
00:23:55 --> 00:23:59
So column of A times row of B.
 

347
00:23:59 --> 00:24:03
So a column, then, is
a vector like this.

348
00:24:03 --> 00:24:10
A row is a vector like that.
 

349
00:24:10 --> 00:24:17
And the result is a full size
matrix, but if the column is

350
00:24:17 --> 00:24:23
concentrated at a couple of
nodes and the row is, then it

351
00:24:23 --> 00:24:28
will have zeroes everywhere
except at that.

352
00:24:28 --> 00:24:34
This is the element matrix
with the C included.

353
00:24:34 --> 00:24:39
That would be the element
matrix already blown up to

354
00:24:39 --> 00:24:43
full size by adding
zeroes elsewhere.

355
00:24:43 --> 00:24:49
So, I mean the heart of the
element matrix is where the

356
00:24:49 --> 00:24:51
action is on that spring.
 

357
00:24:51 --> 00:24:55
And then, when we assemble
it, of course, it doesn't

358
00:24:55 --> 00:24:59
contribute down there.
 

359
00:24:59 --> 00:25:03
So the technology of
finite elements is quite

360
00:25:03 --> 00:25:05
interesting and it fits.
 

361
00:25:05 --> 00:25:11
It's a beautiful way to
see the discrete problem.

362
00:25:11 --> 00:25:15
Ready for another
question of any sort.

363
00:25:15 --> 00:25:22
Thank you, good.
 

364
00:25:22 --> 00:25:25
Yeah, sorry, it got
onto the problem set.

365
00:25:25 --> 00:25:30
And then I thought-- let me
write those words down first,

366
00:25:30 --> 00:25:34
singular value decomposition.
 

367
00:25:34 --> 00:25:40
Well I won't write
all those words.

368
00:25:40 --> 00:25:42
That's a wonderful thing.
 

369
00:25:42 --> 00:25:45
It's a highlight of matrix
theory except for the

370
00:25:45 --> 00:25:47
length of its name.
 

371
00:25:47 --> 00:25:53
So SVD is what
everybody calls it.

372
00:25:53 --> 00:25:57
It's only like, every year now
people appreciate more and more

373
00:25:57 --> 00:26:01
the importance of this SVD,
this singular value

374
00:26:01 --> 00:26:02
decomposition.
 

375
00:26:02 --> 00:26:09
So shall I say a few
words about it now?

376
00:26:09 --> 00:26:14
Just a few.
 

377
00:26:14 --> 00:26:18
So it's Section
1.7 of the book.

378
00:26:18 --> 00:26:26
And my thought was, hey we've
done so much matrix theory

379
00:26:26 --> 00:26:29
including eigenvalues and
positive definiteness,

380
00:26:29 --> 00:26:32
let's get on and use it.
 

381
00:26:32 --> 00:26:37
And then I can come back
to the SVD in a sort

382
00:26:37 --> 00:26:40
of lighter moment.
 

383
00:26:40 --> 00:26:43
Because I'm not thinking
you will be responsible

384
00:26:43 --> 00:26:43
for the SVD.
 

385
00:26:44 --> 00:26:46
Eigenvalues I hope
you're understanding.

386
00:26:46 --> 00:26:49
Positive definite I hope
you're understanding.

387
00:26:49 --> 00:26:52
That's heart of
the course stuff.

388
00:26:52 --> 00:26:59
But SVD is a key idea in
linear algebra, but we

389
00:26:59 --> 00:27:01
can't do everything.
 

390
00:27:01 --> 00:27:06
But we can say what it is.
 

391
00:27:06 --> 00:27:08
What's the first point?
 

392
00:27:08 --> 00:27:12
The first point is that every
matrix, even a rectangular

393
00:27:12 --> 00:27:15
matrix, has got a singular
value decomposition.

394
00:27:15 --> 00:27:23
So the matrix A can be m by n.
 

395
00:27:23 --> 00:27:27
I wouldn't speak about
the eigenvalues of a

396
00:27:27 --> 00:27:29
rectangular matrix.
 

397
00:27:29 --> 00:27:32
Because if I multiply, you
remember, the eigenvalue

398
00:27:32 --> 00:27:34
equation wouldn't make sense.
 

399
00:27:34 --> 00:27:39
Ax=lambda*x is no good
if A is rectangular.

400
00:27:39 --> 00:27:40
Right?
 

401
00:27:40 --> 00:27:45
The input would be of length n
and the output would be of

402
00:27:45 --> 00:27:50
length m and this
wouldn't work.

403
00:27:50 --> 00:27:54
So eigenvectors are
not what I'm after.

404
00:27:54 --> 00:27:58
But somehow the goal
of eigenvectors was

405
00:27:58 --> 00:28:00
to diagonalize.
 

406
00:28:00 --> 00:28:03
The goal of eigenvectors
was to find these special

407
00:28:03 --> 00:28:09
directions in which matrix
A acted like a number.

408
00:28:09 --> 00:28:15
And then, as we saw today in
the part that partly still up

409
00:28:15 --> 00:28:20
here, we could solve equations
by eigenvectors by looking

410
00:28:20 --> 00:28:26
for these, following
these special guys.

411
00:28:26 --> 00:28:29
What do we do for a
rectangular matrix?

412
00:28:29 --> 00:28:31
What replaces this?
 

413
00:28:31 --> 00:28:36
So this is now not good.
 

414
00:28:36 --> 00:28:40
The idea is simply we need
two sets of vectors.

415
00:28:40 --> 00:28:48
We need some v's and some u's.
 

416
00:28:48 --> 00:28:51
So that's the central
equation of the SVD.

417
00:28:52 --> 00:28:56
Now what can we get?
 

418
00:28:56 --> 00:29:01
So now we have more freedom
because we're getting a bunch

419
00:29:01 --> 00:29:05
of v's that have, these
guys are in our n.

420
00:29:05 --> 00:29:09
They have length n, right
to multiply A times v.

421
00:29:09 --> 00:29:17
And the output is, so the
n of these, n v's, we're

422
00:29:17 --> 00:29:19
in n dimensional space.
 

423
00:29:19 --> 00:29:22
So those are called
singular vectors.

424
00:29:22 --> 00:29:24
They're called right singular
vectors because they're

425
00:29:24 --> 00:29:26
sitting to the right of A.
 

426
00:29:26 --> 00:29:32
And these guys, these outputs
will be in-- these are v's

427
00:29:32 --> 00:29:35
in our n, n dimensions.
 

428
00:29:35 --> 00:29:41
I have m of these, m u's
in m dimensional space.

429
00:29:41 --> 00:29:45
And these things are
numbers, of course.

430
00:29:45 --> 00:29:50
And actually, they're all
greater or equal to zero.

431
00:29:50 --> 00:29:59
So we can get, by allowing
ourselves the freedom of two

432
00:29:59 --> 00:30:02
right singular vectors and left
singular vectors, we can get a

433
00:30:02 --> 00:30:06
lot more, and we can get,
here's the punch line.

434
00:30:06 --> 00:30:13
We can get the v's to be
orthogonal, orthonormal.

435
00:30:13 --> 00:30:19
Just like the eigenvectors of a
symmetric matrix, we can get

436
00:30:19 --> 00:30:23
these v's, these singular
vectors to be perpendicular to

437
00:30:23 --> 00:30:27
each other and we can get the
u's to be perpendicular

438
00:30:27 --> 00:30:28
to each other.
 

439
00:30:28 --> 00:30:34
What we can't, what we're not
shooting for is the v's to

440
00:30:34 --> 00:30:36
be the same as the u's.
 

441
00:30:36 --> 00:30:40
They're not even in the same
space now, if the matrix

442
00:30:40 --> 00:30:41
A is rectangular.
 

443
00:30:41 --> 00:30:47
And even if the matrix A is
square but not symmetric, we

444
00:30:47 --> 00:30:49
wouldn't get this
perpendicularity.

445
00:30:49 --> 00:30:57
But we get it in the SVD by
having different v's and u's.

446
00:30:57 --> 00:31:02
Here's my picture
of linear algebra.

447
00:31:02 --> 00:31:10
This is the big picture
of linear algebra.

448
00:31:10 --> 00:31:12
This over here is n
dimensional space.

449
00:31:12 --> 00:31:17
We have v's.
 

450
00:31:17 --> 00:31:19
Think of that as n
dimensional space.

451
00:31:19 --> 00:31:21
That's kind of a puny picture.
 

452
00:31:21 --> 00:31:29
But when I multiply by A,
I take a vector here,

453
00:31:29 --> 00:31:30
I multiply by A.
 

454
00:31:30 --> 00:31:33
So let me just do that.
 

455
00:31:33 --> 00:31:37
I multiply by A, I take a
vector v, well, already put

456
00:31:37 --> 00:31:43
v, I take Av and I get
something over here.

457
00:31:43 --> 00:31:51
And this will be
the space of u's.

458
00:31:51 --> 00:31:54
Now I have to ask you one thing
about linear algebra that

459
00:31:54 --> 00:31:56
I keep hammering away.
 

460
00:31:56 --> 00:32:04
If I take any vector and
multiple by A, What do I get?

461
00:32:04 --> 00:32:11
If I take any vector
v, like .

462
00:32:11 --> 00:32:13
Here's A say,
[3, 6; 4, 7; 5, 8].

463
00:32:13 --> 00:32:16
 
 

464
00:32:16 --> 00:32:19
So that's A times v.
 

465
00:32:19 --> 00:32:23
What can you tell me about A
times v that goes a little

466
00:32:23 --> 00:32:26
deeper than just telling
me the numbers?

467
00:32:26 --> 00:32:33
It's a linear combination
of the columns.

468
00:32:33 --> 00:32:36
These are the
outputs, the Av's.

469
00:32:36 --> 00:32:40
So this space is called
the column space.

470
00:32:40 --> 00:32:43
It's all combinations
of the columns.

471
00:32:43 --> 00:32:50
These are all combinations
of the columns.

472
00:32:50 --> 00:32:52
That's the column space.
 

473
00:32:52 --> 00:32:56
It's a bunch of vectors.
 

474
00:32:56 --> 00:33:02
So the point is that these
u's, that I have like, a

475
00:33:02 --> 00:33:06
fantastic choice of axes.
 

476
00:33:06 --> 00:33:10
A linear algebra person
would use the word, bases.

477
00:33:10 --> 00:33:14
But geometrically I'm
saying they're fantastic

478
00:33:14 --> 00:33:17
axes in these spaces.
 

479
00:33:17 --> 00:33:25
So that if I choose the right
axes in the two spaces, then

480
00:33:25 --> 00:33:28
one axis will go to that
one when I multiply by A.

481
00:33:28 --> 00:33:30
The next one will
go to that one.

482
00:33:30 --> 00:33:32
The third will go to that one.
 

483
00:33:32 --> 00:33:37
It just could not be better.
 

484
00:33:37 --> 00:33:43
And that's the statement.
 

485
00:33:43 --> 00:33:46
Now maybe I'll just say
a word about where it's

486
00:33:46 --> 00:33:51
used or why it's useful.
 

487
00:33:51 --> 00:33:58
Oh, in so many applications.
 

488
00:33:58 --> 00:34:02
Well most of you are
not biologists.

489
00:34:02 --> 00:34:04
And I'm not certainly.
 

490
00:34:04 --> 00:34:09
But we know that there's a lot
of interesting math these days

491
00:34:09 --> 00:34:15
in a lot of interesting
experiments with genes.

492
00:34:15 --> 00:34:20
So what happens?
 

493
00:34:20 --> 00:34:24
So we've got about
40 people here.

494
00:34:24 --> 00:34:34
And we measure the,
well, we get data.

495
00:34:34 --> 00:34:36
That's what I'm
really going to say.

496
00:34:36 --> 00:34:40
Somehow we got a giant
amount of data. right?.

497
00:34:40 --> 00:34:41
Probably 40 columns.
 

498
00:34:41 --> 00:34:46
Everybody here is entitled to
be a column of the matrix.

499
00:34:46 --> 00:34:51
And the entries will be
like, have you got TB, what

500
00:34:51 --> 00:34:55
height, all this stuff.
 

501
00:34:55 --> 00:34:57
So you got enormous
amount of data.

502
00:34:57 --> 00:35:02
And the question is, what's
important in that data.

503
00:35:02 --> 00:35:06
You've got a million entries in
a giant matrix and you want to

504
00:35:06 --> 00:35:08
extract the important thing.
 

505
00:35:08 --> 00:35:11
Well, it's the SVD
that does it.

506
00:35:11 --> 00:35:17
You take that giant matrix of
data, you find the v's and

507
00:35:17 --> 00:35:19
the sigmas and to u's.
 

508
00:35:19 --> 00:35:28
And then the largest sigmas are
the most important information

509
00:35:28 --> 00:35:32
if things are scaled and
statistics is coming

510
00:35:32 --> 00:35:34
into this also.
 

511
00:35:34 --> 00:35:43
I could a give sensible, much
more mathematical discussion

512
00:35:43 --> 00:35:49
of one word, one name it
goes under is principle

513
00:35:49 --> 00:35:52
component analysis.
 

514
00:35:52 --> 00:35:56
That's a standard tool for
statisticians looking at

515
00:35:56 --> 00:35:58
giant amounts of data.
 

516
00:35:58 --> 00:36:02
Is to find the principal
components and those will come

517
00:36:02 --> 00:36:04
from these eigenvectors.
 

518
00:36:04 --> 00:36:12
Well your question about the
SVD set off that discussion.

519
00:36:12 --> 00:36:15
I'll only add a
couple of words.

520
00:36:15 --> 00:36:22
These v's and these u's are
actually eigenvectors.

521
00:36:22 --> 00:36:28
But they're not eigenvectors
of A. v's happen to be the

522
00:36:28 --> 00:36:31
eigenvectors of A transpose A.
 

523
00:36:31 --> 00:36:35
And the u's happen to be
eigenvectors of A, A transpose.

524
00:36:35 --> 00:36:41
And the linear algebra
comes together to give

525
00:36:41 --> 00:36:45
you this key equation.
 

526
00:36:45 --> 00:36:48
And of course, you and I know
that if I'm looking at the

527
00:36:48 --> 00:36:53
eigenvectors v of A
transpose*A, A transpose A is

528
00:36:53 --> 00:36:56
a symmetric matrix.
 

529
00:36:56 --> 00:36:58
In fact, positive definite.
 

530
00:36:58 --> 00:37:02
So that the eigenvectors will
be orthogonal, the eigenvalues

531
00:37:02 --> 00:37:03
will be positive.
 

532
00:37:03 --> 00:37:07
And then this one is coming
from the eigenvectors of A A

533
00:37:07 --> 00:37:12
transpose, which is different.
 

534
00:37:12 --> 00:37:16
So if the matrix A happened to
be square, happened to be

535
00:37:16 --> 00:37:19
symmetric, happened to be
positive definite, this

536
00:37:19 --> 00:37:20
would just be Ax=lambda*x.
 

537
00:37:21 --> 00:37:24
I didn't have to cross it out.
 

538
00:37:24 --> 00:37:26
I'll use K for that.
 

539
00:37:26 --> 00:37:32
So if the matrix A was one of
our favorite matrices, was a K,

540
00:37:32 --> 00:37:35
that would be the case in which
the SVD is no different

541
00:37:35 --> 00:37:37
from eigenvalues.
 

542
00:37:37 --> 00:37:41
The v's are the same as the
u's, the sigmas are the same

543
00:37:41 --> 00:37:46
as the lambdas, all fine.
 

544
00:37:46 --> 00:37:50
This is sort of the way to get
the beauty of positive definite

545
00:37:50 --> 00:37:54
symmetric matrices when the
matrix itself that you start

546
00:37:54 --> 00:37:56
with isn't even square.
 

547
00:37:56 --> 00:38:00
Like this one.
 

548
00:38:00 --> 00:38:02
More than I wanted to say,
more than you wanted

549
00:38:02 --> 00:38:04
to hear about the SVD.
 

550
00:38:04 --> 00:38:06
 
 

551
00:38:06 --> 00:38:08
What else?
 

552
00:38:08 --> 00:38:17
Yes, thank you.
 

553
00:38:17 --> 00:38:19
More about, sure.
 

554
00:38:19 --> 00:38:20
Let me repeat the question.
 

555
00:38:20 --> 00:38:24
So this refers to this
morning's lecture, lecture 9

556
00:38:24 --> 00:38:32
about time-dependent problems.
 

557
00:38:32 --> 00:38:35
And the point was that when I
have a differential equation

558
00:38:35 --> 00:38:39
in time there're lots of
ways to replace it by

559
00:38:39 --> 00:38:45
difference equations.
 

560
00:38:45 --> 00:38:50
So let's take the equation
du/dt, let's make it first

561
00:38:50 --> 00:38:57
order is some function
of u often and t.

562
00:38:57 --> 00:39:02
That would be a first order.
 

563
00:39:02 --> 00:39:07
Yeah, it's going to look at
when I write that much down.

564
00:39:07 --> 00:39:09
What have I got?
 

565
00:39:09 --> 00:39:14
I've got a first order
differential equation.

566
00:39:14 --> 00:39:17
Is it linear?
 

567
00:39:17 --> 00:39:18
No.
 

568
00:39:18 --> 00:39:21
I'm allowing some, this
function of u could be sin(u).

569
00:39:23 --> 00:39:25
It could be u to
the tenth power.

570
00:39:25 --> 00:39:28
It could be e to the u.
 

571
00:39:28 --> 00:39:32
So this is how
equations really come.

572
00:39:32 --> 00:39:35
The linear ones are the model
problems that we can solve.

573
00:39:35 --> 00:39:40
This is how linear
equations really come.

574
00:39:40 --> 00:39:44
Euler thought of, let's give
Euler credit here, so forward

575
00:39:44 --> 00:39:51
Euler and backward Euler.
 

576
00:39:51 --> 00:39:58
And the point will be that
this guy is explicit.

577
00:39:58 --> 00:40:01
So can I write that word so
I don't forget to write it?

578
00:40:01 --> 00:40:02
Explicit.
 

579
00:40:02 --> 00:40:04
And that this guy
will be implicit.

580
00:40:04 --> 00:40:10
And this is a big distinction.
 

581
00:40:10 --> 00:40:12
And we'll see it.
 

582
00:40:12 --> 00:40:20
So this says u_(n+1)-u_n over
delta t is the value of the

583
00:40:20 --> 00:40:33
slope at the start of the step.
 

584
00:40:33 --> 00:40:37
So that's the most important,
most basic, first thing

585
00:40:37 --> 00:40:38
you would think of.
 

586
00:40:38 --> 00:40:40
It replaces this by this.
 

587
00:40:40 --> 00:40:44
You start with u_0, and
from this you get u_1.

588
00:40:45 --> 00:40:48
And then you know u_1 and
from this you get u_2.

589
00:40:49 --> 00:40:55
And the point is at every step
this equation is telling

590
00:40:55 --> 00:40:57
you what u_1 is from u_0.
 

591
00:40:58 --> 00:41:00
You just move u_0
over to that side.

592
00:41:00 --> 00:41:03
You've only got stuff you
know and then you know u_1.

593
00:41:04 --> 00:41:07
Contrast that with
backward Euler.

594
00:41:07 --> 00:41:14
So that's u_(n+1)-u_n over
delta t is f at-- Now what

595
00:41:14 --> 00:41:18
am I going to put there?
 

596
00:41:18 --> 00:41:24
I'm going to put the end, the
point we don't know yet.

597
00:41:24 --> 00:41:31
So is the slope at u_(n
+ 1) and the time that

598
00:41:31 --> 00:41:32
goes with it, t_(n+1).
 

599
00:41:34 --> 00:41:41
t_n is just a shorthand for
n*delta t. n steps forward

600
00:41:41 --> 00:41:42
in time got us to t_n.
 

601
00:41:43 --> 00:41:45
This is one more step.
 

602
00:41:45 --> 00:41:52
Now why is this
called implicit?

603
00:41:52 --> 00:41:57
How do I find u_(n+1)
out of this?

604
00:41:57 --> 00:41:59
I've got to solve for it.
 

605
00:41:59 --> 00:42:01
It's much more expensive.
 

606
00:42:01 --> 00:42:04
Because it'll be probably
a non-linear system of

607
00:42:04 --> 00:42:07
equations to solve.
 

608
00:42:07 --> 00:42:09
We'll take a little
time on that.

609
00:42:09 --> 00:42:13
But of course, there's one
outstanding method to solve a

610
00:42:13 --> 00:42:16
system of non-linear equations
and that's called

611
00:42:16 --> 00:42:18
Newton's method.
 

612
00:42:18 --> 00:42:20
So Newton is coming in.
 

613
00:42:20 --> 00:42:26
Newton's method is sort of
the first, the good way, if

614
00:42:26 --> 00:42:31
you can do it, to solve.
 

615
00:42:31 --> 00:42:37
Well when I say solve, I mean
set up an iteration which,

616
00:42:37 --> 00:42:43
after maybe three loops or five
loops will be accurate to

617
00:42:43 --> 00:42:46
enough digits that you can
say, ok that's u_(n+1).

618
00:42:47 --> 00:42:49
On to the next step.
 

619
00:42:49 --> 00:42:53
Then the next step will
have the same equation

620
00:42:53 --> 00:42:57
but with n up one.
 

621
00:42:57 --> 00:42:57
So it'd be u_( n+2)-u_(n+1).
 

622
00:43:00 --> 00:43:05
So you see the extra work
here, but it's more stable.

623
00:43:05 --> 00:43:08
The way this one spiraled
out, this one spiraled

624
00:43:08 --> 00:43:12
in in the model problem.
 

625
00:43:12 --> 00:43:17
I hope you look at that
Section 2.2 which was about

626
00:43:17 --> 00:43:21
the model problem that
we discussed today.

627
00:43:21 --> 00:43:22
There's more to say.
 

628
00:43:22 --> 00:43:29
I mean, this is the central
problem of time-dependent,

629
00:43:29 --> 00:43:34
evolving initial
value problems.

630
00:43:34 --> 00:43:37
You're sort of
marching forward.

631
00:43:37 --> 00:43:42
But here, each step of the
march takes an inner loop which

632
00:43:42 --> 00:43:49
has to work hard to solve,
to find where you march to.

633
00:43:49 --> 00:43:52
Is that a help, to indicate?
 

634
00:43:52 --> 00:43:59
Because this is a very
fundamental difference.

635
00:43:59 --> 00:44:05
And Chapter 6 of the book
develops higher order methods.

636
00:44:05 --> 00:44:08
These are both first order,
first order accurate.

637
00:44:08 --> 00:44:14
The error that you're making
is of the order of delta t.

638
00:44:14 --> 00:44:16
That's not great, right?
 

639
00:44:16 --> 00:44:19
Because you would have to take
many, many small steps to

640
00:44:19 --> 00:44:24
have a reasonable error.
 

641
00:44:24 --> 00:44:28
So these higher order methods
allow you to get to a great

642
00:44:28 --> 00:44:32
answer with bigger steps.
 

643
00:44:32 --> 00:44:37
A lot of thinking has gone
into that and somehow it's

644
00:44:37 --> 00:44:43
a pretty basic problem.
 

645
00:44:43 --> 00:44:51
Just to mention for MATLAB,
ode45 is maybe the workhorse

646
00:44:51 --> 00:44:56
code to solve this
type of a problem.

647
00:44:56 --> 00:45:01
And the method is not Euler.
 

648
00:45:01 --> 00:45:04
That would not be good enough.
 

649
00:45:04 --> 00:45:07
It's called Runge-Kutta.
 

650
00:45:07 --> 00:45:11
Two guys, Runge and Kutta,
figured out a formula that got

651
00:45:11 --> 00:45:14
up to fourth order accurate.
 

652
00:45:14 --> 00:45:17
So that's the thing.
 

653
00:45:17 --> 00:45:23
And then if we looked further
about this subject we would

654
00:45:23 --> 00:45:27
distinguish some equations
that are called stiff.

655
00:45:27 --> 00:45:29
So I'll just write
that word down.

656
00:45:29 --> 00:45:38
Some equations, the iteration
is particularly difficult.

657
00:45:38 --> 00:45:40
You have two time scales.
 

658
00:45:40 --> 00:45:42
Maybe you've got things
happening on a slow

659
00:45:42 --> 00:45:44
scale and a high scale.
 

660
00:45:44 --> 00:45:47
Multi-scale computations,
that's where the

661
00:45:47 --> 00:45:49
subject is now.
 

662
00:45:49 --> 00:45:55
And so there would be separate
codes with an S in their names

663
00:45:55 --> 00:46:02
suitable for these tougher
problems, stiff equation.

664
00:46:02 --> 00:46:09
I guess one thing you sometimes
get in the review session is a

665
00:46:09 --> 00:46:17
look outside the scope of
what I can cover and ask

666
00:46:17 --> 00:46:20
homework problems about.
 

667
00:46:20 --> 00:46:29
I'm sure hoping that you're
assembling the elements of

668
00:46:29 --> 00:46:32
computational science here.
 

669
00:46:32 --> 00:46:34
First, what are the questions?
 

670
00:46:34 --> 00:46:35
What are some answers?
 

671
00:46:35 --> 00:46:38
What are the issues?
 

672
00:46:38 --> 00:46:44
What do you have to balance
to make a good decision?

673
00:46:44 --> 00:46:47
Time for another
question if we like.

674
00:46:47 --> 00:46:50
Yeah, thank you.
 

675
00:46:50 --> 00:47:05
Stability, yeah, right.
 

676
00:47:05 --> 00:47:10
This here?
 

677
00:47:10 --> 00:47:13
First, if we want a matrix
then this has to be a

678
00:47:13 --> 00:47:15
vector of unknowns.
 

679
00:47:15 --> 00:47:19
I'm thinking now of a system
of, this is n equations.

680
00:47:19 --> 00:47:23
I've got u is a vector
with n components.

681
00:47:23 --> 00:47:25
I've got n equations here.
 

682
00:47:25 --> 00:47:29
The notation, I can put
a little arrow over it

683
00:47:29 --> 00:47:32
just to remind myself.
 

684
00:47:32 --> 00:47:36
And if I want to get
to a matrix I better

685
00:47:36 --> 00:47:38
do the linear case.
 

686
00:47:38 --> 00:47:48
So I'll do the linear case.
 

687
00:47:48 --> 00:47:51
The big picture is that
the new values come

688
00:47:51 --> 00:47:54
from the old values.
 

689
00:47:54 --> 00:47:56
There has to be a
matrix multiplier.

690
00:47:56 --> 00:48:01
Anytime we have a
linear process.

691
00:48:01 --> 00:48:06
So the new values come from old
values by some linear map.

692
00:48:06 --> 00:48:12
A matrix is doing it.
 

693
00:48:12 --> 00:48:14
So there's a sort
of growth matrix.

694
00:48:14 --> 00:48:17
Can I just put down
some letters here?

695
00:48:17 --> 00:48:22
I won't be able to give
you a complete answer.

696
00:48:22 --> 00:48:25
But this'll do it.
 

697
00:48:25 --> 00:48:34
So u_(n+1) is some
matrix times u_n.

698
00:48:34 --> 00:48:38
That's what I wrote down this
morning for a special case.

699
00:48:38 --> 00:48:44
It's gotta look like that
for a linear problem.

700
00:48:44 --> 00:48:48
And let's suppose that we have
this nice situation as today

701
00:48:48 --> 00:48:51
where it's the same
G at every step.

702
00:48:51 --> 00:48:58
So the problem is not changing
in time, it's linear,

703
00:48:58 --> 00:48:59
it's all good.
 

704
00:48:59 --> 00:49:03
What is the solution after
n times steps compared

705
00:49:03 --> 00:49:06
to the start?
 

706
00:49:06 --> 00:49:12
So give me a simple formula for
the solution to this step,

707
00:49:12 --> 00:49:15
step, step equation.
 

708
00:49:15 --> 00:49:20
If I started at initial value
u_0 and I looked to see, well

709
00:49:20 --> 00:49:25
what matrix gets me over n
steps, what do I write there?

710
00:49:25 --> 00:49:30
G to the n, right, G to the n.
 

711
00:49:30 --> 00:49:36
So now comes the question.
 

712
00:49:36 --> 00:49:38
The stability question is
whether do the powers

713
00:49:38 --> 00:49:41
of G to the nth grow?
 

714
00:49:41 --> 00:49:43
And here's the point.
 

715
00:49:43 --> 00:49:48
That the continuous problem
that this came from, it's

716
00:49:48 --> 00:49:52
got its own growth or
decay or oscillation.

717
00:49:52 --> 00:49:56
This guy, the discrete
one, has got its.

718
00:49:56 --> 00:50:01
They're going to be close
for a step or two.

719
00:50:01 --> 00:50:06
For one or two steps I'm
expecting that this is a

720
00:50:06 --> 00:50:10
reasonable consistent
approximation to my problem.

721
00:50:10 --> 00:50:16
But after I take a thousand
steps, this one could still

722
00:50:16 --> 00:50:22
be close or it could
have exploded.

723
00:50:22 --> 00:50:26
And that's the stability thing.
 

724
00:50:26 --> 00:50:32
The choice of the difference
method can be be stable or not.

725
00:50:32 --> 00:50:36
And it's going to be the
eigenvalues of G that

726
00:50:36 --> 00:50:38
are the best guide.
 

727
00:50:38 --> 00:50:45
So in the end people compute
the eigenvalues of the growth

728
00:50:45 --> 00:51:10
matrix and look to see are
they bigger than one or not.

729
00:51:10 --> 00:51:13
Let's close with that example
because that's a good example.

730
00:51:13 --> 00:51:18
So it fits this, but not quite,
because it wasn't completely

731
00:51:18 --> 00:51:20
forward or completely backward.
 

732
00:51:20 --> 00:51:23
And let's just write down what
it was in that model problem

733
00:51:23 --> 00:51:26
and find the matrix.
 

734
00:51:26 --> 00:51:29
So can you remind me
what I wrote today?

735
00:51:29 --> 00:51:33
So u, was it u first?
 

736
00:51:33 --> 00:51:35
U_(n+1) was U_n+delta t*V_n.
 

737
00:51:35 --> 00:51:41
 
 

738
00:51:41 --> 00:51:47
And then the new velocity was
the old velocity and it should

739
00:51:47 --> 00:51:50
be minus delta t times the u.
 

740
00:51:50 --> 00:51:55
Because our equation is, these
are representing the equation.

741
00:51:55 --> 00:52:01
u'=v is the first one. v'=-u
is my second equation.

742
00:52:01 --> 00:52:04
So and then the point was
I could take that because

743
00:52:04 --> 00:52:10
I know it already, I
could take it there.

744
00:52:10 --> 00:52:14
Where is the matrix here?
 

745
00:52:14 --> 00:52:20
I want to find the growth
matrix that tells me now-- My

746
00:52:20 --> 00:52:28
growth matrix, of course, this
is  and this is .

747
00:52:28 --> 00:52:31
 
 

748
00:52:31 --> 00:52:34
And this is a two
by two matrix.

749
00:52:34 --> 00:52:38
And I hope we can read it off.
 

750
00:52:38 --> 00:52:40
Or find it anyway.
 

751
00:52:40 --> 00:52:45
Because that's the key.
 

752
00:52:45 --> 00:52:49
How could we get a matrix
out of these two equations?

753
00:52:49 --> 00:52:52
Let me just ask you to look
at them and think what

754
00:52:52 --> 00:52:58
are we going to do.
 

755
00:52:58 --> 00:53:00
That's a good question.
 

756
00:53:00 --> 00:53:02
What shall I do?
 

757
00:53:02 --> 00:53:07
I would like to know the new
U's, U, V from the old.

758
00:53:07 --> 00:53:10
What'll I do?
 

759
00:53:10 --> 00:53:22
Bring stuff onto, a U_(n+1) on
this side and n on this side.

760
00:53:22 --> 00:53:25
This guy I want to
get over here.

761
00:53:25 --> 00:53:29
Can I do that with erasing?
 

762
00:53:29 --> 00:53:40
So it's going to come
over with a plus sign.

763
00:53:40 --> 00:53:45
So I guess I see here an
implicit matrix acting on the

764
00:53:45 --> 00:53:48
left and an explicit matrix
acting on the right.

765
00:53:48 --> 00:53:50
So I see a .
 

766
00:53:52 --> 00:53:54
And I see my explicit matrix.
 

767
00:53:54 --> 00:53:56
Shall I call it E?
 

768
00:53:56 --> 00:53:58
That's the right sides.
 

769
00:53:58 --> 00:54:02
I see a one and a
one and a delta t.

770
00:54:02 --> 00:54:09
And now my left side of my
equation, the n+1 stuff.

771
00:54:09 --> 00:54:14
What's my implicit matrix on
the left sides of the equation?

772
00:54:14 --> 00:54:23
Well, a one and a one and a
plus delta t is here, right?

773
00:54:23 --> 00:54:31
And let's call that
I for implicit.

774
00:54:31 --> 00:54:34
Are we close?
 

775
00:54:34 --> 00:54:36
I think that looks pretty good.
 

776
00:54:36 --> 00:54:38
What's G?
 

777
00:54:38 --> 00:54:40
What's the matrix G now?
 

778
00:54:40 --> 00:54:44
You just have to begin to
develop a little faith that

779
00:54:44 --> 00:54:48
yeah, I can deal with matrices,
I can move them around,

780
00:54:48 --> 00:54:51
they're under my control.
 

781
00:54:51 --> 00:54:55
I wanted this picture.
 

782
00:54:55 --> 00:54:58
I want to move everything
to the right.

783
00:54:58 --> 00:55:03
What do I do to move everything
to the right-hand side?

784
00:55:03 --> 00:55:06
I want that to be over there.
 

785
00:55:06 --> 00:55:07
It's an inverse.
 

786
00:55:07 --> 00:55:08
It's the inverse.
 

787
00:55:08 --> 00:55:14
So G, the matrix G there
is, I bring I over here.

788
00:55:14 --> 00:55:22
It's the inverse
of I times the E.

789
00:55:22 --> 00:55:25
And I can figure out
what that matrix is.

790
00:55:25 --> 00:55:28
And I can find its eigenvalues.
 

791
00:55:28 --> 00:55:31
And it'll be interesting.
 

792
00:55:31 --> 00:55:33
So actually that's
the perfect problem.

793
00:55:33 --> 00:55:37
I mean, if you want to see
what's going on, well the book

794
00:55:37 --> 00:55:43
will be a good help I think,
but that's the growth

795
00:55:43 --> 00:55:45
matrix for leapfrog.
 

796
00:55:45 --> 00:55:49
And I'll tell you the result.
 

797
00:55:49 --> 00:55:56
The eigenvalues are right of
magnitude one, as you hope, up

798
00:55:56 --> 00:55:58
to a certain value of delta t.
 

799
00:55:58 --> 00:56:01
And then when delta t passes
that stability limit the

800
00:56:01 --> 00:56:04
eigenvalues take off.
 

801
00:56:04 --> 00:56:09
So that this method is
great provided you're not

802
00:56:09 --> 00:56:15
too ambitious and take
too large a delta t.

803
00:56:15 --> 00:56:18
Thanks for that last
question, thanks for coming.