Matrix Inverse Algorithm/Examples/Arbitrary Matrix 1
Examples of use of Matrix Inverse Algorithm
Let $\mathbf A$ be the (square) matrix defined as:
- $\mathbf A = \begin {pmatrix} 1 & 1 & 0 & 0 \\ 0 & 1 & 1 & 0 \\ 0 & 0 & 1 & 1 \\ 0 & 0 & 0 & 1 \\ \end {pmatrix}$
Then its inverse $\mathbf A^{-1}$ is:
- $\mathbf A^{-1} = \begin {pmatrix} 1 & -1 & 1 & -1 \\ 0 & 1 & -1 & 1 \\ 0 & 0 & 1 & -1 \\ 0 & 0 & 0 & 1 \\ \end {pmatrix}$
Proof
We construct $\begin {pmatrix} \mathbf A & \mathbf I \end {pmatrix}$:
- $\begin {pmatrix} \mathbf A & \mathbf I \end {pmatrix} = \paren {\begin {array} {cccc|cccc} 1 & 1 & 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 1 & 1 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 1 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ \end {array} }$
In the following, $\sequence {e_n}_{n \mathop \ge 1}$ denotes the sequence of elementary row operations that are to be applied to $\begin {pmatrix} \mathbf A & \mathbf I \end {pmatrix}$.
The matrix that results from having applied $e_1$ to $e_k$ in order is denoted $\begin {pmatrix} \mathbf A_k & \mathbf B_k \end {pmatrix}$.
$e_1 := r_3 \to r_3 - r_4$
Hence:
- $\begin {pmatrix} \mathbf A_1 & \mathbf B_1 \end {pmatrix} = \paren {\begin {array} {cccc|cccc} 1 & 1 & 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 1 & 1 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 & 0 & 1 & -1 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ \end {array} }$
$e_2 := r_2 \to r_2 - r_3$
- $\begin {pmatrix} \mathbf A_2 & \mathbf B_2 \end {pmatrix} = \paren {\begin {array} {cccc|cccc} 1 & 1 & 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 & 1 & -1 & 1 \\ 0 & 0 & 1 & 0 & 0 & 0 & 1 & -1 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ \end {array} }$
$e_3 := r_1 \to r_1 - r_2$
- $\begin {pmatrix} \mathbf A_3 & \mathbf B_3 \end {pmatrix} = \paren {\begin {array} {cccc|cccc} 1 & 0 & 0 & 0 & 1 & -1 & 1 & -1 \\ 0 & 1 & 0 & 0 & 0 & 1 & -1 & 1 \\ 0 & 0 & 1 & 0 & 0 & 0 & 1 & -1 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ \end {array} }$
and it is seen that $\begin {pmatrix} \mathbf A_3 & \mathbf B_3 \end {pmatrix}$ is the required reduced echelon form:
- $\mathbf A_3 = \mathbf I$
and so by the Matrix Inverse Algorithm:
- $\mathbf B_3 = \mathbf A^{-1}$
$\blacksquare$
Sources
- 1998: Richard Kaye and Robert Wilson: Linear Algebra ... (previous) ... (next): Part $\text I$: Matrices and vector spaces: $1$ Matrices: $1.5$ Row and column operations: Exercise $1.7 \ \text {(a)}$