The last time I spent solving a
system of equations dealing with
the chilling of this hardboiled
egg being put in an ice bath.
We called T1 the temperature of
the yoke and T2 the temperature
of the white.
What I am going to do is
revisit that same system of
equations, but basically the
topic for today is to learn to
solve that system of equations
by a completely different
method.
It is the method that is
normally used in practice.
Elimination is used mostly by
people who have forgotten how to
do it any other way.
Now, in order to make it a
little more general,
I am not going to use the
dependent variables T1 and T2
because they suggest temperature
a little too closely.
Let's change them to neutral
variables.
I will use x equals T1,
and for T2 I will just use
y.
I am not going to re-derive
anything.
I am not going to resolve
anything.
I am not going to repeat
anything of what I did last
time, except to write down to
remind you what the system was
in terms of these variables,
the system we derived using the
particular conductivity
constants, two and three,
respectively.
The system was this one,
minus 2x plus 2y.
And the y prime was 
2x minus 5y.
And so we solved this by
elimination.
We got a single second-order
equation with constant
coefficients,
which we solved in the usual
way.
From that I derived what the x
was, from that we derived what
the y was, and then I put them
all together.
I will just remind you what the
final solution was when written
out in terms of arbitrary
constants.
It was c1 times e to the
negative t plus c2 e to the
negative 6t, 
and y was c1 over 2 e
to the negative t minus 2c2 e to
the negative 6t.
That was the solution we got.
And then I went on to put in
initial conditions,
but we are not going to explore
that aspect of it today.
We will in a week or so.
This was the general solution
because it had two arbitrary
constants in it.
What I want to do now is
revisit this and do it by a
different method,
which makes heavy use of
matrices.
That is a prerequisite for this
course, so I am assuming that
you reviewed a little bit about
matrices.
And it is in your book.
Your book puts in a nice little
review section.
Two-by-two and three-by-three
will be good enough for 18.03
mostly because I don't want you
to calculate all night on bigger
matrices, bigger systems.
So nothing serious,
matrix multiplication,
solving systems of linear
equations, end-by-end systems.
I will remind you at the
appropriate places today of what
it is you need to remember.
The very first thing we are
going to do is,
let's see.
I haven't figured out the color
coding for this lecture yet,
but let's make this system in
green and the solution can be in
purple.
Invisible purple,
but I have a lot of it.
Let's abbreviate,
first of all,
the system using matrices.
I am going to make a column
vector out of (x,
y).
Then you differentiate a column
vector by differentiating each
component.
I can write the left-hand side
of the system as (x,
y) prime.
How about the right-hand side?
Well, I say I can just write
the matrix of coefficients to
negative 2, 2,
2, negative 5 times x,y.
And I say that this matrix
equation says exactly the same
thing as that green equation
and, therefore,
it is legitimate to put it up
in green, too.
The top here is x prime.
What is the top here?
After I multiply these two I
get a column vector.
And what is its top entry?
It is negative 2x plus 2y.
There it is.
And the bottom entry the same
way is 2x minus 5y,
just as it is down there.
Now, what I want to do is,
well, maybe I should translate
the solution.
What does the solution look
like?
We got that,
too.
How am I going to write this as
a matrix equation?
Actually, if I told you to use
matrices, use vectors,
the point at which you might be
most hesitant is this one right
here, the very next step.
Because how you should write it
is extremely well-concealed in
this notation.
But the point is,
this is a column vector and I
am adding together two column
vectors.
And what is in each one of the
column vectors?
Think of these two things as a
column vector.
Pull out all the scalars from
them that you can.
Well, you see that c1 is a
common factor of both entries
and so is e to the negative t,
that function.
Now, if I pull both of those
out of the vector,
what is left of the vector?
Well, you cannot even see it.
What is left is a 1 up here and
a one-half there.
So I am going to write that in
the following form.
I will put out the c1,
it's the common factor in both,
and put that out front.
Then I will put in the guts of
the vector, even though you
cannot see it,
the column vector 1,
one-half.
And then I will put the other
scalar function in back.
The only reason for putting one
of these in front and one in
back is visual so to make it
easy to read.
There is no other reason.
You could put the c1 here,
you could put it here,
you could put the e negative t
in front if you want
to, but people will fire you.
Don't do that.
Write it the standard way
because that is the way that it
is easiest to read.
The constants out front,
the functions behind,
and the column vector of
numbers in the middle.
And so the other one will be
written how?
Well, here, that one is a
little more transparent.
c2, 1, 2 and the other thing is
e to the negative 6t.
There is our solution.
That is going to need a lot of
purple, but I have it.
And now I want to talk about
how the new method of solving
the equation.
It is based just on the same
idea as the way we solve
second-order equations.
Yes, question.
Oh, here.
Sorry.
This should be negative two.
Thanks very much.
What I am going to use is a
trial solution.
Remember when we had a
second-order equation with
constant coefficients the very
first thing I did was I said we
are going to try a solution of
the form e to the rt.
Why that?
Well, because Oiler thought of
it and it has been known for 200
or 300 years that that is the
thing you should do.
Well, this has not been known
nearly as long because matrices
were only invented around 1880
or so, and people did not really
use them to solve systems of
differential equations until the
middle of the last century,
1950-1960.
If you look at books written in
1950, they won't even talk about
systems of differential
equations, or talk very little
anyway and they won't solve them
using matrices.
This is only 50 years old.
I mean, my God,
in mathematics that is very up
to date, particularly elementary
mathematics.
Anyway, the method of solving
is going to use as a trial
solution.
Now, if you were left to your
own devices you might say,
well, let's try x equals some
constant times e to the lambda1
t and y
equals some other constant times
e to the lambda2 t.
Now, if you try that,
it is a sensible thing to try,
but it will turn out not to
work.
And that is the reason I have
written out this particular
solution, so we can see what
solutions look like.
The essential point is here is
the basic solution I am trying
to find.
Here is another one.
Their form is a column vector
of constants.
But they both use the same
exponential factor,
which is the point.
In other words,
I should not use here,
in my trial solution,
two different lambdas,
I should use the same lambda.
And so the way to write the
trial solution is (x,
y) equals two unknown numbers,
that or that or whatever,
times e to a single unknown
exponent factor.
Let's call it lambda t.
It is called lambda.
It is called r.
It is called m.
I have never seen it called
anything but one of those three
things.
I am using lambda.
Your book uses lambda.
It is a common choice.
Let's stick with it.
Now what is the next step?
Well, we plug into the system.
Substitute into the system.
What are we going to get?
Well, let's do it.
First of all,
I have to differentiate.
The left-hand side asks me to
differentiate this.
How do I differentiate this?
Column vector times a function.
Well, the column vector acts as
a constant.
And I differentiate that.
That is lambda e to the lambda
t.
So the (x, y) prime is (a1,
a2) times e to the lambda t
times lambda.
Now, it is ugly to put the
lambda afterwards because it is
a number so you should put it in
front, again,
to make things easier to read.
But this lambda comes from
differentiating e to the lambda
t and using the chain rule.
This much is the left-hand
side.
That is the derivative (x,
y) prime.
I differentiate the x and I
differentiated the y.
How about the right-hand side.
Well, the right-hand side is
negative 2, 2,
2, negative 5 times what?
Well, times (x,
y), which is (a1,
a2) e to the lambda t.
Now, the same thing that
happened a month or a month and
a half ago happens now.
The whole point of making that
substitution is that the e to
the lambda t,
the function part of it drops
out completely.
And one is left with what?
An algebraic equation to be
solved for lambda a1 and a2.
In other words,
by means of that substitution,
and it basically uses the fact
that the coefficients are
constant, what you have done is
reduced the problem of calculus,
of solving differential
equations, to solving algebraic
equations.
In some sense that is the only
method there is,
unless you do numerical stuff.
You reduce the calculus to
algebra.
The Laplace transform is
exactly the same thing.
All the work is algebra.
You turn the original
differential equation into an
algebraic equation for Y of s,
you solve it,
and then you use more algebra
to find out what the original
little y of t was.
It is not different here.
So let's solve this system of
equations.
Now, the whole problem with
solving this system,
first of all,
what is the system?
Let's write it out explicitly.
Well, it is really two
equations, isn't it?
The first one says lambda a1 is
equal to negative 2 a1 
plus 2 a2.
That is the first one.
The other one says lambda a2 is
equal to 2 a1 minus 5 a2.
Now, purely,
if you want to classify that,
that is two equations and three
variables, three unknowns.
The a1, a2, and lambda are all
unknown.
And, unfortunately,
if you want to classify them
correctly, they are nonlinear
equations because they are made
nonlinear by the fact that you
have multiplied two of the
variables.
Well, if you sit down and try
to hack away at solving those
without a plan,
you are not going to get
anywhere.
It is going to be a mess.
Also, two equations and three
unknowns is indeterminate.
You can solve three equations
and three unknowns and get a
definite answer,
but two equations and three
unknowns usually have an
infinity of solutions.
Well, at this point it is the
only idea that is required.
Well, this was a little idea,
but I assume one would think of
that.
And the idea that is required
here is, I think,
not so unnatural,
it is not to view these a1,
a2, and lambda as equal.
Not all variables are created
equal.
Some are more equal than
others.
a1 and a2 are definitely equal
to each other,
and let's relegate lambda to
the background.
In other words,
I am going to think of lambda
as just a parameter.
I am going to demote it from
the status of variable to
parameter.
If I demoted it further it
would just be an unknown
constant.
That is as bad as you can be.
I am going to focus my
attention on the a1,
a2 and sort of view the lambda
as a nuisance.
Now, as soon as I do that,
I see that these equations are
linear if I just look at them as
equations in a1 and a2.
And moreover,
they are not just linear,
they are homogenous.
Because if I think of lambda
just as a parameter,
I should rewrite the equations
this way.
I am going to subtract this and
move the left-hand side to the
right side, and it is going to
look like (minus 2 minus lambda)
times a1 plus 2 a2 is equal to
zero.
And the same way for the other
one.
It is going to be 2a1 plus,
what is the coefficient,
(minus 5 minus lambda) a2
equals zero.
That is a pair of simultaneous
linear equations for determining
a1 and a2, and the coefficients
involved are parameter lambda.
Now, what is the point of doing
that?
Well, now the point is whatever
you learned about linear
equations, you should have
learned the most fundamental
theorem of linear equations.
The main theorem is that you
have a square system of
homogeneous equations,
this is a two-by-two system so
it is square,
it always has the trivial
solution, of course,
a1, a2 equals zero.
Now, we don't want that trivial
solution because if a1 and a2
are zero, then so are x and y
zero.
Now that is a solution.
Unfortunately,
it is of no interest.
If the solution were x,
y zero, it corresponds to the
fact that this is an ice bath.
The yoke is at zero,
the white is at zero and it
stays that way for all time
until the ice melts.
So that is the solution we
don't want.
We don't want the trivial
solution.
Well, when does it have a
nontrivial solution?
Nontrivial means non-zero,
in other words.
If and only if this determinant
is zero.
In other words,
by using that theorem on linear
equations, what we find is there
is a condition that lambda must
satisfy, an equation in lambda
in order that we would be able
to find non-zero values for a1
and a2.
Let's write it out.
I will recopy it over here.
What was it?
Negative 2 minus lambda,
two, here it was 2 and minus 5
minus lambda.
All right.
You have to expand the
determinant.
In other words,
we are trying to find out for
what values of lambda is this
determinant zero.
Those will be the good values
which lead to nontrivial
solutions for the a's.
This is the equation lambda
plus 2.
See, this is minus that and
minus that, the product of the
two minus ones is plus one.
So it is lambda plus 2 times
lambda plus 5,
which is the product of the two
diagonal elements,
minus the product of the two
anti-diagonal elements,
which is 4, is equal to zero.
And if I write that out,
what is that,
that is the equation lambda
squared plus 7 lambda,
5 lambda plus 2 lambda,
and then the constant term is
10 minus 4 which is 6.
How many of you have long
enough memories,
two-day memories that you
remember that equation?
When I did the method of
elimination, it led to exactly
the same equation except it had
r's in it instead of lambda.
And this equation,
therefore, is given the same
name and another color.
Let's make it salmon.
And it is called the
characteristic equation for this
method.
All right.
Now I am going to use now the
word from last time.
You factor this.
From the factorization we get
its root easily enough.
The roots are lambda equals
negative 1 and
lambda equals negative 6
by factoring the
equation.
Now what I am supposed to do?
You have to keep the different
parts of the method together.
Now I have found the only
values of lambda for which I
will be able to find nonzero
values for the a1 and a2.
For each of those values of
lambda, I now have to find the
corresponding a1 and a2.
Let's do them one at a time.
Let's take first lambda equals
negative one.
My problem is now to find a1
and a2.
Where am I going to find them
from?
Well, from that system of
equations over there.
I will recopy it over here.
What is the system?
The hardest part of this is
dealing with multiple minus
signs, but you had experience
with that in determinants so you
know all about that.
In other words,
there is the system of
equations over there.
Let's recopy them here.
Minus 2, minus minus 1 makes
minus 1.
What's the other coefficient?
It is just plain old 2.
Good.
There is my first equation.
And when I substitute lambda
equals negative one
for the second equation,
what do you get?
2 a1 plus negative 5 minus
negative 1 makes negative 4.
There is my system that will
find me a1 and a2.
What is the first thing you
notice about it?
You immediately notice that
this system is fake because this
second equation is twice the
first one.
Something is wrong.
No, something is right.
If that did not happen,
if the second equation were not
a constant multiple of the first
one then the only solution of
the system would be a1 equals
zero, a2 equals zero because the
determinant of the coefficients
would not be zero.
The whole function of this
exercise was to find the value
of lambda, negative 1,
for which the system would be
redundant and,
therefore, would have a
nontrivial solution.
Do you get that?
In other words,
calculate the system out,
just as I have done here,
you have an automatic check on
the method.
If one equation is not a
constant multiple of the other
you made a mistake.
You don't have the right value
of lambda or you substituted
into the system wrong,
which is frankly a more common
error.
Go back, recheck first the
substitution,
and if convinced that is right
then recheck where you got
lambda from.
But here everything is going
fine so we can now find out what
the value of a1 and a2 are.
You don't have to go through a
big song and dance for this
since most of the time you will
have two-by-two equations and
now and then three-by-three.
For two-by-two all you do is,
since we really have the same
equation twice,
to get a solution I can assign
one of the variables any value
and then simply solve for the
other.
The natural thing to do is to
make a2 equal one,
then I won't need fractions and
then a1 will be a2.
So the solution is (2,
1).
I am only trying to find one
solution.
Any constant multiple of this
would also be a solution,
as long as it wasn't zero,
zero which is the trivial one.
And, therefore,
this is a solution to this
system of algebraic equations.
And the solution to the whole
system of differential equations
is, this is only the (a1,
a2) part.
I have to add to it,
as a factor,
lambda is negative,
therefore, e to the minus t.
There is our purple thing.
See how I got it?
Starting with the trial
solution, I first found out
through this procedure what the
lambda's have to be.
Then I took the lambda and
found what the corresponding a1
and a2 that went with it and
then made up my solution out of
that.
Now, quickly I will do the same
thing for lambda 
equals negative 6.
Each one of these must be
treated separately.
They are separate problems and
you are looking for separate
solutions.
Lambda equals negative 6.
What do I do?
How do my equations look now?
Well, the first one is minus 2
minus negative 6 makes plus 4.
It is 4a1 plus 2a2 equals zero.
Then I hold my breath while I
calculate the second one to see
if it comes out to be a constant
multiple.
I get 2a1 plus negative 5 minus
negative 6, which makes plus 1.
And, indeed,
one is a constant multiple of
the other.
I really only have on equation
there.
I will just write down
immediately now what the
solution is to the system.
Well, the (a1,
a2) will be what?
Now, it is more natural to make
a1 equal 1 and then solve to get
an integer for a2.
If a1 is 1, then a2 is negative
2.
And I should multiply that by e
to the negative 6t
because negative 6 is the
corresponding value.
There is my other one.
And now there is a
superposition principle,
which if I get a chance will
prove for you at the end of the
hour.
If not, you will have to do it
yourself for homework.
Since this is a linear system
of equations,
once you have two separate
solutions, neither a constant
multiple of the other,
you can multiply each one of
these by a constant and it will
still be a solution.
You can add them together and
that will still be a solution,
and that gives the general
solution.
The general solution is the sum
of these two,
an arbitrary constant.
I am going to change the name
since I don't want to confuse it
with the c1 I used before,
times the first solution which
is (2, 1) e to the negative t
plus c2, another arbitrary
constant, times 1 negative 2 e
to the minus 6t.
Now you notice that is exactly
the same solution I got before.
The only difference is that I
have renamed the arbitrary
constants.
The relationship between them,
c1 over 2,
I am now calling c1 tilda,
and c2 I am calling c2 tilda.
If you have an arbitrary
constant, it doesn't matter
whether you divide it by two.
It is still just an arbitrary a
constant.
It covers all values,
in other words.
Well, I think you will agree
that is a different procedure,
yet it has only one
coincidence.
It is like elimination goes
this way and comes to the
answer.
And this method goes a
completely different route and
comes to the answer,
except it is not quite like
that.
They walk like this and then
they come within viewing
distance of each other to check
that both are using the same
characteristic equation,
and then they again go their
separate ways and end up with
the same answer.
There is something special of
these values.
You cannot get away from those
two values of lambda.
Somehow they are really
intrinsically connected.
Occurs the exponential
coefficient, and they are
intrinsically connected with the
problem of the egg that we
started with.
Now what I would like to do is
very quickly sketch how this
method looks when I remove all
the numbers from it.
In some sense,
it becomes a little clearer
what is going on.
And that will give me a chance
to introduce the terminology
that you need when you talk
about it.
Well, you have notes.
Let me try to write it down in
general.
I will first write it out
two-by-two.
I am just going to sketch.
The system looks like (x,
y) equals, I will still put it
up in colors.
Except now, instead of using
twos and fives,
I will use (a,
b; c, d).
The trial solution will look
how?
The trial is going to be (a1,
a2).
That I don't have to change the
name of.
I am going to substitute in,
and what the result of
substitution is going to be
lambda (a1, a2).
I am going to skip a step and
pretend that the e to the lambda
t's have already
been canceled out.
Is equal to (a,
b; c, d) times (a1,
a2).
What does that correspond to?
That corresponds to the system
as I wrote it here.
And then we wrote it out in
terms of two equations.
And what was the resulting
thing that we ended up with?
Well, you write it out,
you move the lambda to the
other side.
And then the homogeneous system
is we will look in general how?
Well, we could write it out.
It is going to look like a
minus lambda,
b, c, d minus lambda.
That is just how it looks there
and the general calculation is
the same.
Times (a1, a2) is equal to
zero.
This is solvable nontrivially.
In other words,
it has a nontrivial solution if
an only if the determinant of
coefficients is zero.
Let's now write that out,
calculate out once and for all
what that determinant is.
I will write it out here.
It is a minus lambda times d
minus lambda,
the product of the diagonal
elements, minus the
anti-diagonal minus bc is equal
to zero.
And let's calculate that out.
It is lambda squared minus a
lambda minus d lambda plus ad,
the constant term from here,
negative bc from there,
plus ad minus bc,
where have I seen that before?
This equation is the general
form using letters of what we
calculated using the specific
numbers before.
Again, I will code it the same
way with that color salmon.
Now, most of the calculations
will be for two-by-two systems.
I advise you,
in the strongest possible
terms, to remember this
equation.
You could write down this
equation immediately for the
matrix.
You don't have to go through
all this stuff.
For God's sakes,
don't say let the trial
solution be blah,
blah, blah.
You don't want to do that.
I don't want you to repeat the
derivation of this every time
you go through a particular
problem.
It is just like in solving
second order equations.
You have a second order
equation.
You immediately write down its
characteristic equation,
then you factor it,
you find its roots and you
construct the solution.
It takes a minute.
The same thing,
this takes a minute,
too.
What is the constant term?
Ad minus bc,
what is that?
Matrix is (a,
b; c, d).
Ad minus bc is its determinant.
This is the determinant of that
matrix.
I didn't give the matrix a
name, did I?
I will now give the matrix a
name A.
What is this?
Well, you are not supposed to
know that until now.
I will tell you.
This is called the trace of A.
Put that down in your little
books.
The abbreviation is trace A,
and the word is trace.
The trace of a square matrix is
the sum of the d elements down
its main diagonal.
If it were a three-by-three
there would be three terms in
whatever you are up to.
Here it is a plus b,
the sum of the diagonal
elements.
You can immediately write down
this characteristic equation.
Let's give it a name.
This is a characteristic
equation of what?
Of the matrix,
now.
Not of the system,
of the matrix.
You have a two-by-two matrix.
You could immediately write
down its characteristic
equation.
Watch out for this sign,
minus.
That is a very common error to
leave out the minus sign because
that is the way the formula
comes out.
Its roots.
If it is a quadratic equation
it will have roots;
lambda1, lambda2 for the moment
let's assume are real and
distinct.
For the enrichment of your
vocabulary, those are called the
eigenvalues.
They are something which
belonged to the matrix A.
They are two secret numbers.
You can calculate from the
coefficients a,
b, and c, and d,
but they are not in the
coefficients.
You cannot look at a matrix and
see what its eigenvalues are.
You have to calculate
something.
But they are the most important
numbers in the matrix.
They are hidden,
but they are the things that
control how this system behaves.
Those are called the
eigenvalues.
Now, there are various purists,
there are a fair number of them
in the world who do not like
this word because it begins
German and ends English.
Eigenvalues were first
introduced by a German
mathematician,
you know, around the time
matrices came into being in 1880
or so.
A little while after
eigenvalues came into being,
too.
And since all this happened in
Germany they were named
eigenvalues in German,
which begins eigen and ends
value.
But people who do not like that
call them the characteristic
values.
Unfortunately,
it is two words and takes a lot
more space to write out.
An older generation even calls
them something different,
which you are not so likely to
see nowadays,
but you will in slightly older
books.
You can also call them the
proper values.
Characteristic is not a
translation of eigen,
but proper is,
but it means it in a funny
sense which has almost
disappeared nowadays.
It means proper in the sense of
belong to.
The only example I can think of
is the word property.
Property is something that
belongs to you.
That is the use of the word
proper.
It is something that belongs to
the matrix.
The matrix has its proper
values.
It does not mean proper in the
sense of fitting and proper or I
hope you will behave properly
when we go to Aunt Agatha's or
something like that.
But, as I say,
by far the most popular thing,
slowly the word eigenvalue is
pretty much taking over the
literature.
Just because it's just one
word, that is a tremendous
advantage.
Okay.
What now is still to be done?
Well, there are those vectors
to be found.
So the very last step would be
to solve the system to find the
vectors a1 and a2.
For each (lambda)i,
find the associated vector.
The vector, we will call it
(alpha)i.
That is the a1 and a2.
Of course it's going to be
indexed.
You have to put another
subscript on it because there
are two of them.
And a1 and a2 is stretched a
little too far.
By solving the system,
and the system will be the
system which I will write this
way, (a minus lambda,
b, c, d minus lambda).
It is just that system that was
over there, but I will recopy
it, (a1, a2) equals zero,
zero.
And these are called the
eigenvectors.
Each of these is called the
eigenvector associated with or
belonging to,
again, in that sense of
property.
Eigenvector,
let's say belonging to,
I see that a little more
frequently, belonging to lambda
i.
So we have the eigenvalues,
the eigenvectors and,
of course, the people who call
them characteristic values also
call these guys characteristic
vectors.
I don't think I have ever seen
proper vectors,
but that is because I am not
old enough.
I think that is what they used
to be called a long time ago,
but not anymore.
And then, finally,
the general solution will be,
by the superposition principle,
(x, y) equals the arbitrary
constant times the first
eigenvector times the eigenvalue
times the e to the corresponding
eigenvalue.
And then the same thing for the
second one, (a1,
a2), but now the second index
will be 2 to indicate that it
goes with the eigenvalue e to
the lambda 2t.
I have done that twice.
And now in the remaining five
minutes I will do it a third
time because it is possible to
write this in still a more
condensed form.
And the advantage of the more
condensed form is A,
it takes only that much space
to write, and B,
it applies to systems,
not just the two-by-two
systems, but to end-by-end
systems.
The method is exactly the same.
Let's write it out as it would
apply to end-by-end systems.
The vector I started with is
(x, y) and so on,
but I will simply abbreviate
this, as is done in 18.02,
by x with an arrow over it.
The matrix A I will abbreviate
with A, as I did before with
capital A.
And then the system looks like
x prime is equal to --
x prime is what?
Ax.
That is all there is to it.
There is our green system.
Now notice in this form I did
not even tell you whether this a
two-by-two matrix or an
end-by-end.
And in this condensed form it
will look the same no matter how
many equations you have.
Your book deals from the
beginning with end-by-end
systems.
That is, in my view,
one of its weaknesses because I
don't think most students start
with two-by-two.
Fortunately,
the book double-talks.
The theory is end-by-end,
but all the examples are
two-by-two.
So just read the examples.
Read the notes instead,
which just do two-by-two to
start out with.
The trial solution is x equals
what?
An unknown vector alpha times e
to the lambda t.
Alpha is what we called a1 and
a2 before.
Plug this into there and cancel
the e to the lambda t's.
What do you get?
Well, this is lambda alpha e to
the lambda t equals A alpha e to
the lambda t.
These two cancel.
And the system to be solved,
A alpha equals lambda alpha.
And now the question is how do
you solve that system?
Well, you can tell if a book is
written by a scoundrel or not by
how they go --
A book, which is in my opinion
completely scoundrel,
simply says you subtract one
from the other,
and without further ado writes
A minus lambda,
and they tuck a little I in
there and write alpha equals
zero.
Why is the I put in there?
Well, this is what you would
like to write.
What is wrong with this
equation?
This is not a valid matrix
equation because that is a
square end-by-end matrix,
a square two-by-two matrix if
you like.
This is a scalar.
You cannot subtract the scalar
from a matrix.
It is not an operation.
To subtract matrices they have
to be the same size,
the same shape.
What is done is you make this a
two-by-two matrix.
This is a two-by-two matrix
with lambdas down the main
diagonal and I elsewhere.
And the justification is that
lambda alpha is the same thing
as the lambda I times alpha
because I is an identity matrix.
Now, in fact,
jumping from here to here is
not something that would occur
to anybody.
The way it should occur to you
to do this is you do this,
you write that,
you realize it doesn't work,
and then you say to yourself I
don't understand what these
matrices are all about.
I think I'd better write it all
out.
And then you would write it all
out and you would write that
equation on the left-hand board
there.
Oh, now I see what it should
look like.
I should subtract lambda from
the main diagonal.
That is the way it will come
out.
And then say,
hey, the way to save lambda
from the main diagonal is put it
in an identity matrix.
That will do it for me.
In other words,
there is a little detour that
goes from here to here.
And one of the ways I judge
books is by how well they
explain the passage from this to
that.
If they don't explain it at all
and just write it down,
they have never talked to
students.
They have just written books.
Where did we get finally here?
The characteristic equation
from that, I had forgotten what
color.
That is in salmon.
The characteristic equation,
then, is going to be the thing
which says that the determinant
of that is zero.
That is the circumstances under
which it is solvable.
In general, this is the way the
characteristic equation looks.
And its roots,
once again, are the
eigenvalues.
And from then you calculate the
corresponding eigenvectors.
Okay.
Go home and practice.
In recitation you will practice
on both two-by-two and
three-by-three cases,
and we will talk more next
time.
