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STEP 2 Matrices Topic Notes
Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Manipulating Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Some proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2×2Transformation Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3×3Transformation Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Invariant Points and Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
An alternative approach to invariants (for interest only) . . . . . . . . . . . . . . . . . . . 12
Examples revisited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Thesenotesaredesignedtohelpsupportthestudyofthematricestopic, which has been introduced
into the new STEP syllabus (first examination in 2019) in light of the changes to the A-level
specifications.
They begin with a brief summary of the relevant definitions and related results, which should
mostly be familiar. There is an extensive section at the end of the notes on invariant points and
lines.
STEP 2 Matrices Topic Notes 1
maths.org/step
Definitions
Elements: The individual items in a matrix (usually numbers, which might be unknown and
represented by letters). Elements of a matrix are also known as entries.
Dimensions: The “size” of a matrix. A matrix with m rows and n columns has dimensions m×n
(“mbyn”). Mostofthetimeyouwillbedealing with square matrices, which are ones which
have the same number of rows and columns.
Conformable: For addition (or subtraction), two matrices are called conformable if they can be
added (or subtracted), which is if they have the same dimensions. For multiplication, two
matrices are called conformable if they can be multiplied in a specified order, which is if the
number of columns in the first matrix is equal to the number of rows in the second matrix.
Zero Matrix: A zero or null matrix is one that has 0 for every element. It is often written as
O. Adding the (conformable) zero matrix to matrix A gives A+O = O+A = A, whereas
multiplying A by a conformable zero matrix results in a zero matrix: AO = OA = O (where
the different zero matrices in this equation may have different dimensions).
Transpose: The transpose of a matrix A is what you get if you swap the rows and columns
round (so that the first row becomes the first column and so on). It is written AT.
Identity: An identity matrix (usually written as I) is one which has 1’s on the leading diagonal
(i.e., the diagonal from top left to bottom right) and 0’s everywhere else. An identity matrix
must be a square matrix. If you multiply a matrix A by the (conformable) identity I then
you get AI = IA = A (where the different identity matrices in this equation will have
different dimensions if A is not square).
Determinant: Every square matrix has a number associated with it called its determinant. In
the case of a 2 × 2 matrix A = a b, the determinant is denoted by detA or |A| and
c d
a b
is given by the formula detA = |A| = = ad − bc. Note that when writing out
c d
the determinant explicitly, we generally only write vertical lines; we wouldn’t usually write
a b
c d . −1 −1 −1
Inverse: The inverse of a square matrix A is the matrix A such that A A=AA =I. In
the case of a 2 × 2 matrix A = a b, A has an inverse if detA 6= 0, and in this case, the
c d
inverse is given by A−1 = 1 d −b= 1 d −b.
ad−bc −c a detA −c a
Singular matrix: A square matrix A which has detA = 0 is called singular. If a matrix A
is singular then the inverse A−1 does not exist. A matrix which is not singular is called
non-singular.
STEP 2 Matrices Topic Notes 2
maths.org/step
Manipulating Matrices
To add or subtract two conformable matrices you add/subtract the corresponding elements of
the two matrices, ending up with a matrix of the same dimensions as the original matrices.
To multiply a matrix by a scalar, multiply every element by the scalar.
Multiplying two matrices together: if matrix A has dimensions p×q and matrix B has dimensions
q ×r then matrix AB will have dimensions p×r. Matrix BA will only exist if p = r. See here for
an explanation of how to multiply two matrices together.
Note that matrix multiplication is not commutative, that is in general AB 6= BA, even if both
sides are defined. Matrix multiplication is associative, that is we have A(BC) = (AB)C whenever
either side is defined.
We can also expand brackets as with normal algebra, though we must now be careful about the
order of multiplication. For example, assuming that all matrices are conformable:
A(B+C)=AB+AC
(A+B)C=AC+BC
2 2 2
(A+B) =A +AB+BA+B
The first two of these say that multiplication distributes over addition. Note that we cannot, in
general, simplify the right hand side of the third line, as AB and BA may be different.
If a matrix A has an inverse, the the inverse is unique. We show this by assuming that A has two
different inverses, B and C. If we can show that B = C, this will be a contradiction, so A can
have at most one inverse. So to show that B = C, consider BAC. By associativity, we have
BAC=(BA)C
=IC as B is an inverse of A
=C
and BAC=B(AC)
=BI as C is an inverse of A
=B
so B = C, and A has at most one inverse.
−1 −1 −1
If the matrices A and B are both non-singular then (AB) =B A . Note that the order
of the matrices is reversed. We can prove this as follows: the inverse of AB is a matrix X which
satisfies (AB)X = X(AB) = I. Letting X = AB gives:
−1 −1
(AB)X=(AB)(B A )
−1 −1
=A(BB )A
=AIA−1
=AA−1
=I,
−1 −1
and X(AB)=I similarly. Therefore the inverse of AB is B A .
STEP 2 Matrices Topic Notes 3
maths.org/step
T T T
Likewise, for any two conformable matrices A and B, we have the identity (AB) = B A . This
is relatively easy to show, just by thinking about how an element of AB is calculated, and what
happens when the matrix is then transposed.
Another useful result is that for any square matrix A, we have detAT = detA. A quick calculation
shows that it is true for 2 × 2 matrices. (To prove this for n × n matrices requires a more general
definition of determinant.)
Thepair of simultaneous equations ax+by = e and cx+dy = f can be written in the form Ax = q
where A = a b , x= x and q= e . If detA6=0 then the simultaneous equations have a
c d y f
unique solution given by x = A−1q. If detA = 0, then A does not have an inverse. In this case,
either the two lines represented by the equations are parallel and so do not meet, meaning that
there are no solutions, or the two lines are the same line and there are infinitely many solutions.
We can see this algebraically: detA = ad − bc, so if ad − bc = 0, we have (assuming a and b are
non-zero) d = c, so c = ak and d = bk for some k. Therefore the second equation is akx+bky = f,
b a
so (assuming k 6= 0) ax+by = f. If f = e, then the two equations are just multiples of each other
k k
and there are infinitely many solutions, while if f 6= e, the two lines are parallel and there are no
k
solutions. The cases where k = 0 or a = 0 or b = 0 can be dealt with similarly.
STEP 2 Matrices Topic Notes 4
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