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## https:/.../video3-additionetcf61mb.mp4

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In this video we look at the
subjective addition and
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subtraction of matrices, and we
also look at scalar
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multiplication of matrices. To
do that, we're going to need
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some matrices, so here are some
matrices that I've already
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prepared, and you'll see we've
got four matrices here, and
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they've all got different sizes.
So the first thing we need to do
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is just remind ourselves as to
how we look at the size of a
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matrix, so we count up the
number of rows and the number of
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columns. So this matrix A.
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We say is a two by two matrix
because it's got two rows and
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two columns. Matrix B's got
three rows and two columns, so
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that's a three by two matrix.
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And we can clearly see that
matrix C is a two by three
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matrix, two rows and three
columns, and matrix D is a three
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by two matrix with three rows
and two columns.
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Now, when it comes to adding and
subtracting matrices, we can
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only do it when the two matrices
have the same size. That is,
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when they got this both got the
same number of rows and the same
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number of columns and two
matrices that have the same size
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are said to be compatible, and
when they're compatible we can
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add them and subtract them.
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So if we return to our four
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matrices. We see that of these
four matrices, the only two that
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are compatible or matrix B and
matrix D. They have the same
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size 3 rows and two columns. So
what that means is that we can
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find B + D and we can find
B -- D and we can find D
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-- B. So we can add and subtract
matrices, B&D because they have
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the same size.
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Because they're compatible, we
can't add A&B because they have
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different sizes. We can't add
C&A because they have different
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sizes. Might be worth noting
that where we define the matrix
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C transpose. C transpose. That's
where the rows become columns
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would have. Two columns, because
each row would turn into a
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column, it would have three
rows, so C transpose would be a
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three by two matrix. So C
transpose is also compatible
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with B&D. So we can add C
transpose to be or today, but we
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can't add C to be or today.
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Once we found two matrices that
are compatible that these two
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matrices that have got the same
size, then we need to know how
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to actually add them up. So we
look at our matrices B&D and see
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how we go through this process.
So here's our Matrix B and
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here's our Matrix D and I've
written them with a plus sign
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between them and underneath I've
written them out again with a
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minus sign. So this is B + D and
this is B -- D. So how do we do
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the addition? Well, it's quite
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straightforward. All we do
is we were adding we add the
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elements that are in the
same position. We call that
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the corresponding position.
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So because the five is in the
first row on the 1st column and
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the two is in the first row and
the first column, they get added
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together. So we do 5 + 2 which
is 7 and that gives us the entry
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in the first row and the first
column of our answer.
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We do the same with all the
elements, so the minus one is in
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the 2nd row and the first
column. So we add that to the
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zero in the 2nd row and the
first column. So we do minus 1 +
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0, which gives us minus one and
we can continue to do that for
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all six elements of the matrix.
So 1 + 4 because the one and
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four are in corresponding
positions gives us 5 -- 2 + --
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2. Gives us minus four and that
goes up here because it's in the
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first row and the second column
first row on the second column
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for three and the one get added
to give us four and the nought
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and the minus one get added to
give us minus one.
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And so that's how we do matrix
addition. So just to recap, we
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have to have two matrices that
have the same size and then when
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we have two matrices at the same
size we add them by adding the
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elements that are in
corresponding positions. And so
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the answer we get is the same
size as the two matrices that
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we've added together.
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Now the principles of
subtraction are exactly the
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same. We deal with elements that
are in the corresponding
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positions, but obviously this
time we subtract rather than add
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them. So we do 5 -- 2 to get
three, we do minus 1 -- 0 to get
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minus one. We do 1 -- 4 to
get minus three. That's done the
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elements in the first column
with the elements in the second
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column minus 2 -- -- 2 becomes
minus 2 + 2, which is 0.
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3 -- 1 gives us 2
and 0 -- -- 1 is 0
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+ 1 which is 1.
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And there's our answer. So
when we do B -- D, This is the
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answer. Again, a matrix of the
same size as B&D, so that
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illustrates how we do matrix
addition and subtraction. We
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have to have two matrices
which have the same size in
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order to be compatible. And
then what we do is we add or
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subtract the elements that are
in the same positions. We call
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corresponding elements.
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Now Matrix obviously has the
same size itself, so we can
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always add a matrix to itself,
and we're going to do that now
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with the Matrix A. So we're
going to add matrix A to
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itself, so into a plus a. So
here's Matrix A and what adding
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matrix a onto it. And because
it's the same matrix, clearly
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it's not the same size that
both 2 by two matrices, so we
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go through the standard
procedure.
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When we add elements that are
in corresponding positions, so
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the four gets added to the
four, which gives us 8, the
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three gets added to the three
to give us 6 not getting to
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nought, which gives us nought
and minus one gets added to
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minus one. To give this minus
2.
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So matrix a + A is this matrix
here with entries 860 and minus
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two and we used to writing A
plus a in a shorthand form as a
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+ A = 2 A.
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One lot of a there's another lot
of a gives us two lots of a.
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So this matrix that we found
here, we can refer to as 2A.
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And if we look at the entries in
this matrix and compare them
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with the entries of a, we see
that each of the entries is just
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twice the entries of a 2 * 4 is
eight 2 * 3 or 6 two times
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naughties nought 2 * -- 1 is
minus two, and so this process
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illustrates how we do we call
scalar multiplication. We take a
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matrix and we multiply it by a
number. All that happens is that
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every element inside the matrix.
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Gets multiplied by the number,
so in this case the number was
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two and we'll do some examples
now, but we use a different
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number. So we've seen how we can
do scalar multiplication by
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simply multiplying every element
inside our matrix by the number.
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The scalar that we're trying to
multiply by. So we'll do a
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couple more examples now, so
we're going to workout is going
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to five times the matrix B and
I'm going to do 1/2 times the
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matrix D. So all I've done is
I've written down what matrix B
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is. I'm going to do five times
this matrix. So remember the
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rule for scalar multiplication
is the scalar, the number that
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we're trying to multiply by
multiplies every entry inside
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the matrix. So we get 5
* 5 is 20 five 5 * --
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1 is minus five. 5 * 1 is
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5. 5 * -- 2 is minus
ten. 5 * 3 is 15 and 5
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* 0 is 0.
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So this is our answer. This is
the matrix 5B or scalar five
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times matrix B. Notice that the
matrix we get to that answer has
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the same size, the same order as
the matrix we started from, and
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that's fairly obvious. That must
be the case because all we do is
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we multiply every element inside
the matrix by the scalar
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outside. So we are not creating
any new entries in the Matrix
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and we're not losing any. So the
matrix that we get must have the
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same size. So when we
started with.
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Here's another example. We don't
have to just multiply by whole
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numbers or two previous
examples. We did 2 * A and 5 *
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B, but we can multiply by any
number, and in this case I'm
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choosing to multiply the
fraction or half. So I'm going
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to do half times matrix D.
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So here it is written out.
Here's matrix D. We do 1/2 times
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that number. All we have to do
is do 1/2 times every element
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inside the matrix.
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So we do 1/2 * 2, which gives us
one 1/2 * 0, which gives us
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nought 1/2. Times 4, which gives
us two. That's not the first
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column and 1/2 * -- 2, giving us
minus 1/2 * 1, giving us a half
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and 1/2 * -- 1, giving us minus
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1/2. And so here's our product
matrix and not surprisingly, it
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ended up with some fractions.
Then, because we were
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multiplying by a fraction to
start off with.
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That concludes the video on
addition and subtraction of
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matrices and on scalar
multiplication.
Title:
https:/.../video3-additionetcf61mb.mp4
Video Language:
English
Duration:
10:39

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