For numbers, dividing by a a a is the same as multiplying by 1 a \displaystyle \frac{1}{a} a 1 , the number with a × 1 a = 1 \displaystyle a \times \frac{1}{a} = 1 a × a 1 = 1 . Matrices have no division, but many square matrices have an inverse : a matrix A − 1 A^{-1} A − 1 with A A − 1 = A − 1 A = I AA^{-1} = A^{-1}A = I A A − 1 = A − 1 A = I . The inverse is what lets you "divide" a matrix equation, and it is the key to solving systems of equations in the next lesson. This lesson shows how to find the inverse using the adjoint, and when an inverse exists.
The adjoint#
The adjoint of a square matrix A A A , written adj A \text{adj}\,A adj A , is the transpose of the matrix of cofactors of A A A .
So to find adj A \text{adj}\,A adj A : replace each entry of A A A by its cofactor, then transpose.
Shortcut for 2 × 2 2 \times 2 2 × 2 . For A = [ a b c d ] A = \begin{bmatrix} a & b \\ c & d \end{bmatrix} A = [ a c b d ] , the cofactors are C 11 = d C_{11} = d C 11 = d , C 12 = − c C_{12} = -c C 12 = − c , C 21 = − b C_{21} = -b C 21 = − b , C 22 = a C_{22} = a C 22 = a . Transposing gives
adj A = [ d − b − c a ] \text{adj}\,A = \begin{bmatrix} d & -b \\ -c & a \end{bmatrix} adj A = [ d − c − b a ]
In words: swap the two diagonal entries and change the signs of the other two.
The key fact#
Result. For any square matrix A A A ,
A ( adj A ) = ( adj A ) A = ∣ A ∣ I A(\text{adj}\,A) = (\text{adj}\,A)A = |A|\,I A ( adj A ) = ( adj A ) A = ∣ A ∣ I
The reason: the diagonal entries of A ( adj A ) A(\text{adj}\,A) A ( adj A ) are rows of A A A times their own cofactors, which is the expansion of ∣ A ∣ |A| ∣ A ∣ . The off-diagonal entries are rows times the cofactors of a different row, which is the expansion of a determinant with two equal rows, and so is 0 0 0 .
The inverse#
If ∣ A ∣ ≠ 0 |A| \neq 0 ∣ A ∣ = 0 , divide the key fact by ∣ A ∣ |A| ∣ A ∣ :
A ( 1 ∣ A ∣ adj A ) = I \displaystyle A\left(\frac{1}{|A|}\text{adj}\,A\right) = I A ( ∣ A ∣ 1 adj A ) = I
So
A − 1 = 1 ∣ A ∣ adj A \displaystyle A^{-1} = \frac{1}{|A|}\,\text{adj}\,A A − 1 = ∣ A ∣ 1 adj A
A square matrix has an inverse exactly when it is non-singular , that is, ∣ A ∣ ≠ 0 |A| \neq 0 ∣ A ∣ = 0 . A singular matrix (∣ A ∣ = 0 |A| = 0 ∣ A ∣ = 0 ) has no inverse.
For a 2 × 2 2 \times 2 2 × 2 matrix with a d − b c ≠ 0 ad - bc \neq 0 a d − b c = 0 :
[ a b c d ] − 1 = 1 a d − b c [ d − b − c a ] \displaystyle \begin{bmatrix} a & b \\ c & d \end{bmatrix}^{-1} = \frac{1}{ad - bc}\begin{bmatrix} d & -b \\ -c & a \end{bmatrix} [ a c b d ] − 1 = a d − b c 1 [ d − c − b a ]
Example 1#
Find the inverse of A = [ 3 5 1 2 ] A = \begin{bmatrix} 3 & 5 \\ 1 & 2 \end{bmatrix} A = [ 3 1 5 2 ] and check it.
∣ A ∣ = 6 − 5 = 1 |A| = 6 - 5 = 1 ∣ A ∣ = 6 − 5 = 1 , so A − 1 = [ 2 − 5 − 1 3 ] A^{-1} = \begin{bmatrix} 2 & -5 \\ -1 & 3 \end{bmatrix} A − 1 = [ 2 − 1 − 5 3 ] .
Check: A A − 1 = [ 6 − 5 − 15 + 15 2 − 2 − 5 + 6 ] = [ 1 0 0 1 ] = I AA^{-1} = \begin{bmatrix} 6 - 5 & -15 + 15 \\ 2 - 2 & -5 + 6 \end{bmatrix} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = I A A − 1 = [ 6 − 5 2 − 2 − 15 + 15 − 5 + 6 ] = [ 1 0 0 1 ] = I .
Example 2#
Find the inverse of B = [ 4 7 2 6 ] B = \begin{bmatrix} 4 & 7 \\ 2 & 6 \end{bmatrix} B = [ 4 2 7 6 ] .
∣ B ∣ = 24 − 14 = 10 |B| = 24 - 14 = 10 ∣ B ∣ = 24 − 14 = 10 , so
B − 1 = 1 10 [ 6 − 7 − 2 4 ] = [ 3 5 − 7 10 − 1 5 2 5 ] \displaystyle B^{-1} = \frac{1}{10}\begin{bmatrix} 6 & -7 \\ -2 & 4 \end{bmatrix} = \begin{bmatrix} \frac{3}{5} & -\frac{7}{10} \\ -\frac{1}{5} & \frac{2}{5} \end{bmatrix} B − 1 = 10 1 [ 6 − 2 − 7 4 ] = [ 5 3 − 5 1 − 10 7 5 2 ]
Example 3#
Find the inverse of A = [ 2 1 1 1 3 2 1 0 0 ] A = \begin{bmatrix} 2 & 1 & 1 \\ 1 & 3 & 2 \\ 1 & 0 & 0 \end{bmatrix} A = 2 1 1 1 3 0 1 2 0 .
Step 1: the determinant. Expand along the third row, which has two zeros:
∣ A ∣ = 1 × ∣ 1 1 3 2 ∣ = 2 − 3 = − 1 |A| = 1 \times \begin{vmatrix} 1 & 1 \\ 3 & 2 \end{vmatrix} = 2 - 3 = -1 ∣ A ∣ = 1 × 1 3 1 2 = 2 − 3 = − 1
∣ A ∣ ≠ 0 |A| \neq 0 ∣ A ∣ = 0 , so the inverse exists.
Step 2: the cofactors.
C 11 = ∣ 3 2 0 0 ∣ = 0 , C 12 = − ∣ 1 2 1 0 ∣ = 2 , C 13 = ∣ 1 3 1 0 ∣ = − 3 C_{11} = \begin{vmatrix} 3 & 2 \\ 0 & 0 \end{vmatrix} = 0, \quad C_{12} = -\begin{vmatrix} 1 & 2 \\ 1 & 0 \end{vmatrix} = 2, \quad C_{13} = \begin{vmatrix} 1 & 3 \\ 1 & 0 \end{vmatrix} = -3 C 11 = 3 0 2 0 = 0 , C 12 = − 1 1 2 0 = 2 , C 13 = 1 1 3 0 = − 3
C 21 = − ∣ 1 1 0 0 ∣ = 0 , C 22 = ∣ 2 1 1 0 ∣ = − 1 , C 23 = − ∣ 2 1 1 0 ∣ = 1 C_{21} = -\begin{vmatrix} 1 & 1 \\ 0 & 0 \end{vmatrix} = 0, \quad C_{22} = \begin{vmatrix} 2 & 1 \\ 1 & 0 \end{vmatrix} = -1, \quad C_{23} = -\begin{vmatrix} 2 & 1 \\ 1 & 0 \end{vmatrix} = 1 C 21 = − 1 0 1 0 = 0 , C 22 = 2 1 1 0 = − 1 , C 23 = − 2 1 1 0 = 1
C 31 = ∣ 1 1 3 2 ∣ = − 1 , C 32 = − ∣ 2 1 1 2 ∣ = − 3 , C 33 = ∣ 2 1 1 3 ∣ = 5 C_{31} = \begin{vmatrix} 1 & 1 \\ 3 & 2 \end{vmatrix} = -1, \quad C_{32} = -\begin{vmatrix} 2 & 1 \\ 1 & 2 \end{vmatrix} = -3, \quad C_{33} = \begin{vmatrix} 2 & 1 \\ 1 & 3 \end{vmatrix} = 5 C 31 = 1 3 1 2 = − 1 , C 32 = − 2 1 1 2 = − 3 , C 33 = 2 1 1 3 = 5
Step 3: the adjoint. The cofactor matrix is [ 0 2 − 3 0 − 1 1 − 1 − 3 5 ] \begin{bmatrix} 0 & 2 & -3 \\ 0 & -1 & 1 \\ -1 & -3 & 5 \end{bmatrix} 0 0 − 1 2 − 1 − 3 − 3 1 5 ; transposing,
adj A = [ 0 0 − 1 2 − 1 − 3 − 3 1 5 ] \text{adj}\,A = \begin{bmatrix} 0 & 0 & -1 \\ 2 & -1 & -3 \\ -3 & 1 & 5 \end{bmatrix} adj A = 0 2 − 3 0 − 1 1 − 1 − 3 5
Step 4: the inverse.
A − 1 = 1 − 1 adj A = [ 0 0 1 − 2 1 3 3 − 1 − 5 ] \displaystyle A^{-1} = \frac{1}{-1}\,\text{adj}\,A = \begin{bmatrix} 0 & 0 & 1 \\ -2 & 1 & 3 \\ 3 & -1 & -5 \end{bmatrix} A − 1 = − 1 1 adj A = 0 − 2 3 0 1 − 1 1 3 − 5
Check the first row of A A A against each column of A − 1 A^{-1} A − 1 : ( 2 ) ( 0 ) + ( 1 ) ( − 2 ) + ( 1 ) ( 3 ) = 1 (2)(0) + (1)(-2) + (1)(3) = 1 ( 2 ) ( 0 ) + ( 1 ) ( − 2 ) + ( 1 ) ( 3 ) = 1 , ( 2 ) ( 0 ) + ( 1 ) ( 1 ) + ( 1 ) ( − 1 ) = 0 (2)(0) + (1)(1) + (1)(-1) = 0 ( 2 ) ( 0 ) + ( 1 ) ( 1 ) + ( 1 ) ( − 1 ) = 0 , ( 2 ) ( 1 ) + ( 1 ) ( 3 ) + ( 1 ) ( − 5 ) = 0 (2)(1) + (1)(3) + (1)(-5) = 0 ( 2 ) ( 1 ) + ( 1 ) ( 3 ) + ( 1 ) ( − 5 ) = 0 . That is the first row of I I I , as it should be.
Properties of the inverse#
For non-singular matrices of the same order:
( A − 1 ) − 1 = A (A^{-1})^{-1} = A ( A − 1 ) − 1 = A
( A B ) − 1 = B − 1 A − 1 (AB)^{-1} = B^{-1}A^{-1} ( A B ) − 1 = B − 1 A − 1 (the reversal law again)
( A ′ ) − 1 = ( A − 1 ) ′ (A')^{-1} = (A^{-1})' ( A ′ ) − 1 = ( A − 1 ) ′
∣ A − 1 ∣ = 1 ∣ A ∣ \displaystyle |A^{-1}| = \frac{1}{|A|} ∣ A − 1 ∣ = ∣ A ∣ 1
Example 4#
If A = [ 2 3 1 2 ] A = \begin{bmatrix} 2 & 3 \\ 1 & 2 \end{bmatrix} A = [ 2 1 3 2 ] , show that A 2 − 4 A + I = O A^2 - 4A + I = O A 2 − 4 A + I = O , and use this to find A − 1 A^{-1} A − 1 .
A 2 = [ 4 + 3 6 + 6 2 + 2 3 + 4 ] = [ 7 12 4 7 ] , A 2 − 4 A + I = [ 7 − 8 + 1 12 − 12 4 − 4 7 − 8 + 1 ] = O A^2 = \begin{bmatrix} 4 + 3 & 6 + 6 \\ 2 + 2 & 3 + 4 \end{bmatrix} = \begin{bmatrix} 7 & 12 \\ 4 & 7 \end{bmatrix}, \qquad A^2 - 4A + I = \begin{bmatrix} 7 - 8 + 1 & 12 - 12 \\ 4 - 4 & 7 - 8 + 1 \end{bmatrix} = O A 2 = [ 4 + 3 2 + 2 6 + 6 3 + 4 ] = [ 7 4 12 7 ] , A 2 − 4 A + I = [ 7 − 8 + 1 4 − 4 12 − 12 7 − 8 + 1 ] = O
Multiply A 2 − 4 A + I = O A^2 - 4A + I = O A 2 − 4 A + I = O by A − 1 A^{-1} A − 1 : A − 4 I + A − 1 = O A - 4I + A^{-1} = O A − 4 I + A − 1 = O , so
A − 1 = 4 I − A = [ 2 − 3 − 1 2 ] A^{-1} = 4I - A = \begin{bmatrix} 2 & -3 \\ -1 & 2 \end{bmatrix} A − 1 = 4 I − A = [ 2 − 1 − 3 2 ]
which agrees with the 2 × 2 2 \times 2 2 × 2 shortcut, since ∣ A ∣ = 1 |A| = 1 ∣ A ∣ = 1 .
Practice#
Write the adjoint of [ 5 2 7 3 ] \begin{bmatrix} 5 & 2 \\ 7 & 3 \end{bmatrix} [ 5 7 2 3 ] and find its inverse.
Find the inverse of [ 2 − 1 3 4 ] \begin{bmatrix} 2 & -1 \\ 3 & 4 \end{bmatrix} [ 2 3 − 1 4 ] .
Does [ 6 4 3 2 ] \begin{bmatrix} 6 & 4 \\ 3 & 2 \end{bmatrix} [ 6 3 4 2 ] have an inverse? Give a reason.
For which value of k k k does [ k 3 4 6 ] \begin{bmatrix} k & 3 \\ 4 & 6 \end{bmatrix} [ k 4 3 6 ] have no inverse?
Find the adjoint and the inverse of [ 1 0 1 2 1 0 0 1 1 ] \begin{bmatrix} 1 & 0 & 1 \\ 2 & 1 & 0 \\ 0 & 1 & 1 \end{bmatrix} 1 2 0 0 1 1 1 0 1 .
Find the inverse of [ 1 2 0 0 1 3 0 0 1 ] \begin{bmatrix} 1 & 2 & 0 \\ 0 & 1 & 3 \\ 0 & 0 & 1 \end{bmatrix} 1 0 0 2 1 0 0 3 1 .
If ∣ A ∣ = 5 |A| = 5 ∣ A ∣ = 5 for a 3 × 3 3 \times 3 3 × 3 matrix A A A , find ∣ A − 1 ∣ |A^{-1}| ∣ A − 1 ∣ .
Verify ( A B ) − 1 = B − 1 A − 1 (AB)^{-1} = B^{-1}A^{-1} ( A B ) − 1 = B − 1 A − 1 for A = [ 1 2 3 4 ] A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} A = [ 1 3 2 4 ] and B = [ 2 0 1 1 ] B = \begin{bmatrix} 2 & 0 \\ 1 & 1 \end{bmatrix} B = [ 2 1 0 1 ] .
If A 2 − 5 A + 2 I = O A^2 - 5A + 2I = O A 2 − 5 A + 2 I = O for some square matrix A A A , express A − 1 A^{-1} A − 1 in terms of A A A and I I I .
Answers#
Show answers
adj = [ 3 − 2 − 7 5 ] \text{adj} = \begin{bmatrix} 3 & -2 \\ -7 & 5 \end{bmatrix} adj = [ 3 − 7 − 2 5 ] ; the determinant is 15 − 14 = 1 15 - 14 = 1 15 − 14 = 1 , so the inverse is the same matrix.
The determinant is 8 + 3 = 11 8 + 3 = 11 8 + 3 = 11 , so the inverse is 1 11 [ 4 1 − 3 2 ] \displaystyle \frac{1}{11}\begin{bmatrix} 4 & 1 \\ -3 & 2 \end{bmatrix} 11 1 [ 4 − 3 1 2 ] .
No: the determinant is 12 − 12 = 0 12 - 12 = 0 12 − 12 = 0 , so the matrix is singular.
6 k − 12 = 0 6k - 12 = 0 6 k − 12 = 0 , so k = 2 k = 2 k = 2 .
The determinant is 1 ( 1 − 0 ) − 0 + 1 ( 2 − 0 ) = 3 1(1 - 0) - 0 + 1(2 - 0) = 3 1 ( 1 − 0 ) − 0 + 1 ( 2 − 0 ) = 3 . adj = [ 1 1 − 1 − 2 1 2 2 − 1 1 ] \text{adj} = \begin{bmatrix} 1 & 1 & -1 \\ -2 & 1 & 2 \\ 2 & -1 & 1 \end{bmatrix} adj = 1 − 2 2 1 1 − 1 − 1 2 1 , and the inverse is 1 3 [ 1 1 − 1 − 2 1 2 2 − 1 1 ] \displaystyle \frac{1}{3}\begin{bmatrix} 1 & 1 & -1 \\ -2 & 1 & 2 \\ 2 & -1 & 1 \end{bmatrix} 3 1 1 − 2 2 1 1 − 1 − 1 2 1 .
The determinant is 1 1 1 (upper triangular), and the inverse is [ 1 − 2 6 0 1 − 3 0 0 1 ] \begin{bmatrix} 1 & -2 & 6 \\ 0 & 1 & -3 \\ 0 & 0 & 1 \end{bmatrix} 1 0 0 − 2 1 0 6 − 3 1 .
1 5 \displaystyle \frac{1}{5} 5 1 .
A B = [ 4 2 10 4 ] AB = \begin{bmatrix} 4 & 2 \\ 10 & 4 \end{bmatrix} A B = [ 4 10 2 4 ] with determinant 16 − 20 = − 4 16 - 20 = -4 16 − 20 = − 4 , so ( A B ) − 1 = − 1 4 [ 4 − 2 − 10 4 ] = [ − 1 1 2 5 2 − 1 ] \displaystyle (AB)^{-1} = -\frac{1}{4}\begin{bmatrix} 4 & -2 \\ -10 & 4 \end{bmatrix} = \begin{bmatrix} -1 & \frac{1}{2} \\ \frac{5}{2} & -1 \end{bmatrix} ( A B ) − 1 = − 4 1 [ 4 − 10 − 2 4 ] = [ − 1 2 5 2 1 − 1 ] . Also B − 1 = 1 2 [ 1 0 − 1 2 ] \displaystyle B^{-1} = \frac{1}{2}\begin{bmatrix} 1 & 0 \\ -1 & 2 \end{bmatrix} B − 1 = 2 1 [ 1 − 1 0 2 ] and A − 1 = − 1 2 [ 4 − 2 − 3 1 ] \displaystyle A^{-1} = -\frac{1}{2}\begin{bmatrix} 4 & -2 \\ -3 & 1 \end{bmatrix} A − 1 = − 2 1 [ 4 − 3 − 2 1 ] , and multiplying gives B − 1 A − 1 = [ − 1 1 2 5 2 − 1 ] \displaystyle B^{-1}A^{-1} = \begin{bmatrix} -1 & \frac{1}{2} \\ \frac{5}{2} & -1 \end{bmatrix} B − 1 A − 1 = [ − 1 2 5 2 1 − 1 ] .
Multiply by A − 1 A^{-1} A − 1 : A − 5 I + 2 A − 1 = O A - 5I + 2A^{-1} = O A − 5 I + 2 A − 1 = O , so A − 1 = 1 2 ( 5 I − A ) \displaystyle A^{-1} = \frac{1}{2}(5I - A) A − 1 = 2 1 ( 5 I − A ) .