Add matrices determinants, inverses, special matrices

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