Objectives: Topics
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Inverse of a matrix, its algebraic properties, and its relation to solving systems of linear equations.
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Elementary matrices and their role in calculating the matrix inverse.
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Section 2.2 Matrix Inverses
Handout 2.2 Matrix Inverses
Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.―Spielberg and Crichton, Jurassic Park, 1993 film
The algorithm we introduce in this section could be used to compute the inverse of an \(n\times n\) matrix. At the end of the lecture we’ll discuss some of the problems with our algorithm and why it can be difficult to compute a matrix inverse.
Objectives: Goals
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Apply the formal definition of an inverse, and its algebraic properties, to solve and analyze linear systems.
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Compute the inverse of an \(n\times n\) matrix, and use it to solve linear systems.
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Construct elementary matrices.
Motivating Question: Is there a matrix \(A\) such that
\begin{equation*}
\begin{bmatrix}
2 \amp -1 \amp 0\\
-1 \amp 2 \amp -1\\
0 \amp -1 \amp 2
\end{bmatrix}A = I_3\; ?
\end{equation*}
Definition 2.16. Invertible (Non-singular) Matrix.
An \(n \times n\) matrix \(A\) is invertible (or non-singular) if there exists a matrix \(C\in\mathbb{R}^{n\times n}\) such that
\begin{equation*}
AC = CA = I_n.
\end{equation*}
If such a matrix exists, we write \(C=A^{-1}\text{.}\)
Theorem 2.17. Inverse Formula for a \(2\times 2\) Matrix.
The \(2\times 2\) matrix \(\begin{bmatrix} a \amp b\\ c \amp d \end{bmatrix}\) is invertible if and only if \(ad-bc\neq 0\text{.}\) In that case,
\begin{equation*}
\begin{bmatrix}
a \amp b\\
c \amp d
\end{bmatrix}^{-1}
=
\frac{1}{ad-bc}
\begin{bmatrix}
d \amp -b\\
-c \amp a
\end{bmatrix}.
\end{equation*}
Hint.
In the solution, I give a proof of this theorem. You may find it interesting to try it for yourself before looking at the full proof. The idea is to start with a matrix \(A = \begin{bmatrix} a \amp b \\ c \amp d \end{bmatrix}\) for fixed \(a,b,c,d \in \mathbb{R}\) with \(ad - bc \neq 0\text{.}\) Now, let \(A^{-1} = \begin{bmatrix} x_1 \amp x_2 \\ x_3 \amp x_4 \end{bmatrix}\) for unknown \(x_1,x_2,x_3,x_4\in \mathbb{R}\text{.}\) Set \(AA^{-1} = I_2\) and determine what the unknowns \(x_1,x_2,x_3,x_4\) are in terms of the known \(a,b,c,d\text{.}\) You should end up solving a system of linear equations involving 4 equations in the 4 unknowns.
Example 2.18.
Find the inverse of the matrix below. Check that it truly is the inverse.
\begin{equation*}
A = \begin{bmatrix}
2 \amp 5\\
-3 \amp -7
\end{bmatrix}.
\end{equation*}
Theorem 2.19. Inverse and Unique Solutions.
A matrix \(A\in\mathbb{R}^{n\times n}\) has an inverse if and only if for every \(\vec b\in\mathbb{R}^n\text{,}\) the system \(A\vec x=\vec b\) has a unique solution. In this case,
\begin{equation*}
\vec x = A^{-1}\vec b.
\end{equation*}
Example 2.20.
Solve the linear system.
\begin{equation*}
\begin{array}{ccc}
3x_1 + 4x_2 \amp= \amp 7 \\
5x_1 + 6x_2 \amp = \amp 7
\end{array}
\end{equation*}
Let \(A\) and \(B\) be invertible \(n\times n\) matrices. Then
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\(\displaystyle (A^{-1})^{-1} = A\)
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\((AB)^{-1} = B^{-1}A^{-1}\) (non-commutative!)
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\(\displaystyle (A^T)^{-1} = (A^{-1})^T\)
Example 2.21.
True or false: \((ABC)^{-1} = C^{-1}B^{-1}A^{-1}\text{.}\)
If \(A\in\mathbb{R}^{n\times n}\) and \(n>2\text{,}\) we can compute \(A^{-1}\) with the following algorithm:
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Row reduce the augmented matrix \(\begin{bmatrix} A \ \rvert \ I_n\end{bmatrix}\text{.}\)
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If the reduction has the form \(\begin{bmatrix}I_n \ \rvert \ B\end{bmatrix}\text{,}\) then \(A\) is invertible and \(B=A^{-1}\text{.}\) Otherwise, \(A\) is not invertible.
Example 2.22.
Compute the inverse of
\begin{equation*}
A=
\begin{bmatrix}
0 \amp 1 \amp 2\\
1 \amp 0 \amp 3\\
0 \amp 0 \amp 1
\end{bmatrix}.
\end{equation*}
Why does the previous algorithm work? We can think of our algorithm as simultaneously solving \(n\) linear systems:
\begin{equation*}
\begin{array}{ccc}
A\vec x_1 \amp = \amp \vec e_1\\
A\vec x_2 \amp = \amp \vec e_2\\
\amp\vdots\\
A\vec x_n \amp = \amp \vec e_n
\end{array}
\end{equation*}
We can also justify the algorithm by using elementary matrices.
Definition 2.23. Elementary Matrix.
An elementary matrix \(E\) is one that differs from \(I_n\) by exactly one elementary row operation.
Recall the elementary row operations:
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Swap rows
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Multiply a row by a non-zero scalar
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Add a multiple of one row to another
Each operation can be represented by matrix multiplication using an elementary matrix.
Example 2.24.
Suppose
\begin{equation*}
E
\begin{bmatrix}
1 \amp 1 \amp 1\\
-2 \amp 1 \amp 0\\
0 \amp 0 \amp 1
\end{bmatrix}
=
\begin{bmatrix}
1 \amp 1 \amp 1\\
0 \amp 3 \amp 2\\
0 \amp 0 \amp 1
\end{bmatrix}.
\end{equation*}
Returning to why the algorithm works, applying a sequence of row operations to \(A\) to obtain \(I_n\) can be written as:
\begin{equation*}
(E_k \cdots E_3E_2E_1)A = I_n.
\end{equation*}
Thus, \(E_k \cdots E_3E_2E_1\) is the inverse matrix we seek.
Theorem 2.25. Invertibility and Row Equivalence.
A matrix \(A\) is invertible if and only if it is row equivalent to the identity matrix. In this case, any sequence of elementary row operations that transforms \(A\) into \(I\text{,}\) when applied to \(I\text{,}\) generates \(A^{-1}\text{.}\)
Some final thoughts:
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We could use \(A^{-1}\) to solve a linear system \(A\vec x=\vec b\text{.}\) We would compute \(A^{-1}\) and then solve by multiplying appropriately.
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As many textbooks point out, \(A^{-1}\) is seldom used in practice: computing it can take a long time and may be prone to numerical error.
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So why did we learn how to compute \(A^{-1}\text{?}\) Later in the course, we use elementary matrices and properties of \(A^{-1}\) to derive results.
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A recurring theme: just because we can do something a certain way, doesn’t mean we should.
