Inverses
Contents
18. Inverses#
18.1. Inverse Functions#
The inverse of a function
If

Fig. 18.1 The inverse of a function
Exercise 18.1
For each of the functions below, state whether it has an inverse and if so write it down.
, . , , where is the set of positive real numbers. , .
18.2. Matrix Inverses#
Since a matrix represents a linear transformation, which is a function, we can consider if a matrix has an inverse. For example, consider the
Its inverse is a
In general, if
for all
In other words,
Definition
Let
then
We write

Fig. 18.2 If the matrix
Exercise 18.2
Show that
is the inverse of .What is the inverse of the matrix
representing an anticlockwise rotation about the origin by ? Calculate and show that it equals the identity matrix.Given that
, write down the solution for explicitly in terms of inverse matrices and .
18.3. Solving Matrix Equations#
Suppose that we are given the definitions below and asked to compute the result for
If this was ordinary scalar algebra, then
The difficulty could be addressed by introducing separate concepts of “left-division” and “right-division”, and some authors have done exactly this. However, a more fundamental approach is to abandon the idea of division for matrices altogether, and consider what it means for matrix multiplication to be invertible.
To illustrate the use of the inverse matrix, we multiply each side of the equation for
It is very important to recognise that we must do exactly the same thing to both sides of the equation. Since we pre-multiply (left multiply) the left-hand side by
Due to the non-commutative nature of matrix multiplication, the result
Now, since matrix multiplication is associative, the left hand side of (18.2) can be rewritten as
Thus, the result for
Solving
Let
18.4. Calculating the (2x2) inverse#
The (2x2) matrix that satisfies the definition
The inverse of a (2x2) matrix
The inverse of a (2x2) matrix
where
and
Note that
Notice the special notation
Exercise 18.3
1. Calculate the determinant of the matrix
2. Write the equations below in the form
Calculate the coefficient matrix
3. Solve the problem given in (18.1) to find B.
18.5. What it means if #
The value of the determinant can be used to infer whether a given linear system has a unique solution. If the determinant is zero then the matrix
To illustrate, we will consider two examples of a system of two equations in two unknowns:
Both sets of equations can be written in the form
The two equations in (18.3) are inconsistent, and so there is no solution, whilst the two equations in (18.4) have an infinite number of solutions satisfying
You can also think about this problem graphically. In general, the determinant of a (2x2) matrix
This brings us to an important theorem which ties together a lot of the ideas we have studied so far.
Invertible Matrix Theorem
Let
is invertible. . has pivots.The null space of
is .The column space of
is . has a unique solution for every .The columns of
are linearly independent.
Exercise 18.4
Let
What is the null space of
?Is
is Invertible?What is
?
18.5.1. Derivation of the (2x2) inverse from first principles#
Consider the problem
The algorithm proceeds as follows:
in which
For the (2x2) problem we start with the augmented matrix:
the following row operations can be used:
,
We obtain
from which the following result for the inverse matrix
The steps that were carried out here were purely algebraic manipulations, and so we can see that the result for
18.6. The (3x3) inverse#
We can extend the method used in the previous section to calculate the inverse of higher order matrices. For example, you could have a go at calculating the inverse of a general (3x3) matrix by Gaussian elimination. The algebra would get very tedious.
However, given the systematic nature of Gaussian elimination, you may not be surprised that there is a pattern that can be spotted, which allows the inverse to be calculated by a recursive method. The result is given in the box below.
The (3x3) inverse formula
The inverse of a (3x3) matrix
where
The minors
The determinant satisfies both of the following results, so you can choose either:
Let’s unpack this complicated definition by considering an example for
Then, we have the following cofactors:
We can find the determinant by expansion of any row or column.
For instance, if we choose to expand by the first row (i=1), we obtain
You may pick any other row or column to expand and you will obtain the same result. For instance, expanding by the third column gives
The inverse matrix is given by
Exercise 18.5
Calculate the inverse of the matrices
1.
2.
18.7. Inverse of the Matrix Product#
By associativity of matrix multiplication,
Therefore,
This result satisfies the “common sense” idea (seen in function composition) that inversion comes in reverse order. If transform B follows transform A then we have to reverse transform B before reversing A. We remove the outer operation first.
We can liken the result to the operation of getting dressed/undressed: If you put your socks on before your shoes, you have to take your shoes off before you can remove your socks!
18.7.1. Linking matrix multiplication and Gaussian elimination#
The row-reduction operations that were introduced in the section on Gaussian elimination can be implemented by matrix multiplication. The illustration here matches the example that was given in A systematic technique for solving systems of equations for the augmented coefficient matrix:
In the first step we used the row operations
The composition of these two matrices puts the coefficient matrix into the upper triangular form previously obtained:
The additional operations that were carried out to fully row-reduce the matrix can be captured in the following row-reduction matrices:
You may verify the result:
It is also possible to combine the row reduction operations into a single multiplication matrix:
Later, we will come to think of this as the inverse of the coefficient matrix.
18.8. Transpose Property#
The following result is true in general (provided that
Proof
18.9. Solutions#
Solution to Exercise 18.1
does not have an inverse because for any negative number there is no such that . has an inverse and . does not have an inverse. To prove this, suppose is the inverse of . Then so we must have . But also so we must also have . In general, any function which is not one-to-one (injective) does not have an inverse.
Solution to Exercise 18.2
1.
2.
Similarly,
3.
We left multiply by
Solution to Exercise 18.3
1.
2. We have
That is,
3.
Solution to Exercise 18.4
Reduce
The null space
is . is not invertible. By the invertible matrix theorem a matrix is invertible if and only if its null space is not zero.By the invertible matrix theorem,
.