Add matrices determinants, inverses, special matrices
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@ -781,3 +781,349 @@ $$
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but $B \ne C$
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but $B \ne C$
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</details>
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</details>
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## Special Matrices
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### Square Matrix
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Where $m = n$
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<details>
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<summary>
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#### Example 1
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A $3\times3$ matrix.
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</summary>
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$$\begin{pmatrix}1 & 2 & 3 \\ 4 & 5 & 6 \\ 7 & 8 & 9\end{pmatrix}$$
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</details>
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<details>
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<summary>
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#### Example 2
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A $2\times2$ matrix.
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</summary>
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$$\begin{pmatrix}1 & 2 \\ 4 & 5 \end{pmatrix}$$
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</details>
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### Identity Matrix
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The identity matrix is a square matrix whose eleements are all 0, except the leading diagonal which
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is 1s.
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The leading diagonal is the top left to bottom right corner.
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It is usually denoted by $I$ or $I_n$.
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The identity matrix has the properties that
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$$AI = IA = A$$
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for any square matrix $A$ of the same order as I, and
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$$Ix = x$$
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for any vector $x$.
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<details>
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<summary>
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#### Example 1
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The $3\times3$ identity matrix.
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</summary>
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$$\begin{pmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix}$$
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</details>
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<details>
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<summary>
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#### Example 2
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The $2\times2$ identity matrix.
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</summary>
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$$\begin{pmatrix}1 & 0 \\ 0 & 1 \end{pmatrix}$$
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</details>
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### Transposed Matrix
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The transpose of matrix $A$ of order $m\times n$ is matrix $A^T$ which has the order $n\times m$.
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It is found by reflecting it along the leading diagonal, or interchanging the rows and columns of
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$A$.
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![by [Lucas Vieira](https://commons.wikimedia.org/wiki/File:Matrix_transpose.gif)](./images/Matrix_transpose.gif)
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Let matrix $D = EF$, then $D^T = (EF)^T = E^TF^T$
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#### Example 1
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$$
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A = \begin{pmatrix}3 & 2 & 1 \\ 4 & 5 & 6 \end{pmatrix},\,
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A^T = \begin{pmatrix}3 & 4 \\ 2 & 5 \\ 1 & 6\end{pmatrix}
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$$
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#### Example 2
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$$
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B = \begin{pmatrix}1 \\ 4\end{pmatrix},\,
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B^T = \begin{pmatrix}1 & 4\end{pmatrix}
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$$
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#### Example 3
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$$
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C = \begin{pmatrix}1 & 2 & 3 \\ 0 & 5 & 1 \\ 2 & 3 & 7\end{pmatrix},\,
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C^T = \begin{pmatrix}1 & 0 & 2 \\ 2 & 5 & 4 \\ 3 & 1 & 7\end{pmatrix}
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$$
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### Orthogonal Matrices
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A matrix, $A$, such that
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$$A^{-1} = A^T$$
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is said to be orthogonal.
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Another way to say this is
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$$AA^T = A^TA = I$$
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### Symmetric Matrices
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A square matrix which is symmetric about its leading diagonal:
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$$A = A^T$$
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You can also express this as the matrix $A$, where
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$$a_{ij} = a_{ji}$$
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is satisfied to all elements.
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<details>
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<summary>
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#### Example 1
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</summary>
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$$\begin{pmatrix}
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1 & 0 & -1 & 3 \\
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0 & 3 & 4 & -1 \\
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-2 & 4 & -1 & 6 \\
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3 & -7 & 6 & 2
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\end{pmatrix}$$
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</details>
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### Anti-Symmetric
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A square matrix is anti-symmetric if
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$$A = -A^T$$
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This can also be expressed as
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$$a_{ij} = -a_{ji}$$
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This means that all elements on the leading diagonal must be 0.
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<details>
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<summary>
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#### Example 1
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</summary>
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$$\begin{pmatrix}
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0 & -1 & 5 \\
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1 & 0 & 1 \\
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-5 & -1 & 0
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\end{pmatrix}$$
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</details>
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## The Determinant
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### Determinant of a 2x2 System
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The determinant of a $2x2$ system is
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$$D = a_{11}a_{22} - a_{12}a_{21}$$
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It is denoted by
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$$
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\begin{vmatrix}
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a_{11} & a_{12} \\
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a_{21} & a_{22}
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\end{vmatrix}
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\text{ or }
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\det
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\begin{pmatrix}
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a_{11} & a_{12} \\
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a_{21} & a_{22}
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\end{pmatrix}
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$$
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- A system of equations has a unique solution if $D \ne 0$
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- If $D = 0$, then there are either
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- no solutions (the equations are inconsistent)
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- intinitely many solutions
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### Determinant of a 3x3 System
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Let
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$$
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A = \begin{pmatrix}
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a_{11} & a_{12} & a_{13} \\
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a_{21} & a_{22} & a_{23} \\
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a_{31} & a_{32} & a_{33}
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\end{pmatrix}
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$$
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\begin{align*}
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\det A = &a_{11} \times \det \begin{pmatrix}a_{22} & a_{23} \\ a_{32} & a_{33} \end{pmatrix} \\
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&-a_{12} \times \det \begin{pmatrix}a_{21} & a_{23} \\ a_{31} & a_{33} \end{pmatrix} \\
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&+a_{13} \times \det \begin{pmatrix}a_{21} & a_{22} \\ a_{31} & a_{32} \end{pmatrix}
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\end{align*}
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The $2x2$ matrices above are created by removing any elements on the same row or column as its corresponding
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coefficient:
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![](./images/vimscrot-2021-11-02T16:19:40,013146580+00:00.png)
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### Chessboard Determinant
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$\det A$ may be obtained by expanding out any row or column.
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To figure out which coefficients should be subtracted and which ones added use the chessboard
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pattern of signs:
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$$\begin{pmatrix} + & - & + \\ - & + & - \\ + & - & + \end{pmatrix}$$
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### Properties of Determinants
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- $$\det A = \det A^T$$
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- If all elements of one row of a matrix are multiplied by a constant $z$, the determinant of the
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new matrix is $z$ times the determinant of the original matrix:
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\begin{align*}
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\begin{vmatrix} za & zb \\ c & d \end{vmatrix} &= zad - zbc \\
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&= z(ad-bc) \\
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&= z\begin{vmatrix} a & b \\ c & d \end{vmatrix}
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\end{align*}
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This is also true if a column of a matrix is mutiplied by a constant.
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**Application** if the fator $z$ appears in each elements of a row or column of a determinant it
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can be factored out
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$$\begin{vmatrix}2 & 12 \\ 1 & 3 \end{vmatrix} = 2\begin{vmatrix}1 & 6 \\ 1 & 3 \end{vmatrix} = 2 \times 3
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\begin{vmatrix} 1 & 2 \\ 1 & 1 \end{vmatrix}$$
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**Application** if all elements in one row or column of a matrix are zero, the value of the
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determinant is 0.
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$$\begin{vmatrix} 0 & 0 \\ c & d \end{vmatrix} = 0\times d - 0\times c = 0$$
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**Application** if $A$ is an $n\times n$ matrix,
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$$\det(zA) = z^n\det A$$
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- Swapping any two rows or columns of a matrix changes the sign of the determinant
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\begin{align*}
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\begin{vmatrix} c & d \\ a & b \end{vmatrix} &= cb - ad \\
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&= -(ad - bc) \\
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&= -\begin{vmatrix} a & b \\ c & d \end{vmatrix}
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\end{align*}
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**Application** If any two rows or two columns are identical, the determinant is zero.
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**Application** If any row is a mutiple of another, or a column a multiple of another column, the
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determinant is zero.
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- The value of a determinant is unchanged by adding to any row a constant multiple of another row,
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or adding to any column a constant multiple of another column
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- If $A$ and $B$ are square matrices of the same order then
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$$\det(AB) = \det A \times \det B $$
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## Inverse of a Matrix
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If $A$ is a square matrix, then its inverse matrix is $A^{-1}$ and is defined by the property that:
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$$A^{-1}A = AA^{-1} = I$$
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- Not every matrix has an inverse
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- If the inverse exists, then it is very useful for solving systems of equations:
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\begin{align*}
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A\pmb{x} = \pmb b \rightarrow A^{-1}A\pmb x &= A^{-1}\pmb b \\
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I\pmb x &= A^{-1}\pmb b \\
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\pmb x &= A^{-1}\pmb b
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\end{align*}
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Therefore there must be a unique solution to $A\pmb x = \pmb b$: $\pmb x = A^{-1}\pmb b$.
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- If $D = EF$ then
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$$D^-1 = (EF)^{-1} = F^{-1}E^{-1}$$
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### Inverse of a 2x2 Matrix
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If $A$ is the $2x2$ matrix
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$$
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A = \begin{pmatrix}
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a_{11} & a_{12} \\
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a_{21} & a_{22}
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\end{pmatrix}
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$$
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and its determinant, $D$, satisfies $D \ne 0$, $A$ has the inverse $A^{-1}$ given by
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$$
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A^{-1} = \frac 1 D \begin{pmatrix}
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a_{22} & -a_{12} \\
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-a_{21} & a_{11}
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\end{pmatrix}
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$$
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If $D = 0$, then matrix $A$ has no inverse.
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<details>
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<summary>
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#### Example 1
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Find the inverse of matrix $A = \begin{pmatrix} -1 & 5 \\ 2 & 3 \end{pmatrix}$.
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</summary>
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1. Calculate the determinant
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$$\det A = -1 \times 3 - 5 \times 2 = -13$$
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Since $\det A \ne 0$, the inverse exists.
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2. Calculate $A^{-1}$
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$$ A^{-1} = \frac 1 {-13} \begin{pmatrix} 3 & -5 \\ -2 & -1\end{pmatrix}$$
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