Difference between revisions of "Matrix"

Latest revision as of 14:29, 30 March 2013

A matrix over a field $F$ is a function from $A\times B$ to $F$ , where $A$ and $B$ are the sets $A=\{1,2,\ldots,m\}$ and $B=\{1,2,\ldots,n\}$ . A matrix is usually represented as a rectangular array of scalars from the field, such that each column belongs to the vector space $F^m$ , where $m$ is the number of rows. If a matrix $A$ has $m$ rows and $n$ columns, its order is said to be $m \times n$ , and it is written as $A_{m \times n}$ .

The element in the $i^{th}$ row and $j^{th}$ column of $A$ is written as $(A)_{ij}$ . It is more often written as $a_{ij}$ , in which case $A$ can be written as $[a_{ij}]$ .

Determinant

If $A_{m\times n}$ is a matrix over $F$ with $m=n$ , a Determinant assigns $A_{m\times n}$ to a member of $F$ and is denoted by $|A|$ or $\begin{vmatrix} a_{11} & a_{12} & \ldots & a_{1n} \\ a_{21} & a_{22} & \ldots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{n1} & a_{n2} & \ldots & a_{nn}\end{vmatrix}$

It is defined recursively.

$\begin{vmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \end{vmatrix}\dot{=}a_{11} a_{22} - a_{21} a_{12}$ $\begin{vmatrix} a_{11} & a_{12} & \ldots & a_{1n} \\ a_{21} & a_{22} & \ldots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{n1} & a_{n2} & \ldots & a_{nn}\end{vmatrix}\dot{=}\sum_{k=1}^n (-1)^{k+1} a_{1k} |A'_{1k}|$

where $A'_{cd}$ is the matrix $A$ with the $c^{th}$ row and $d^{th}$ column removed.

Transposes

Let $A$ be $[a_{ij}]$ . Then $[a_{ji}]$ is said to be the transpose of $A$ , written as $A^T$ or simply $A'$ . If A is over the complex field, replacing each element of $A^T$ by its complex conjugate gives us the conjugate transpose $A^*$ of $A$ . In other words, $A^*=[\bar {a_{ji}}]$

$A$ is said to be symmetric if and only if $A=A^T$ . $A$ is said to be hermitian if and only if $A=A^*$ . $A$ is said to be skew symmetric if and only if $A=-A^T$ . $A$ is said to be skew hermitian if and only if $A=-A^*$ .

Matrix Product

Let $A$ be a matrix of order $m_1 \times m_2$ and $B$ a matrix of order $n_1 \times n_2$ . Then the product $AB$ exists if and only if $m_2=n_1$ and in that case we define the product $C=AB$ as the matrix of order $m_1 \times n_2$ for which $(C)_{ij}=\sum ^{n_1} _{k=1} (A)_{ik} (B)_{kj}$ for all $i$ and $j$ such that $1\le i\le m_1$ and $1\le j\le n_2$ .

Vector spaces associated with a matrix

As already stated before, the columns of $A$ form a subset of $F^m$ . The subspace of $F^m$ generated by these columns is said to be the column space of $A$ , written as $C(A)$ . Similarly, the transposes of the rows form a subset of the vector space $F^n$ . The subspace of $F^n$ generated by these is known as the row space of $A$ , written as $R(A)$ .

$y \in C(A)$ implies $\exists x$ such that $y_{m \times 1} = A_{m \times n} x_{n \times 1}$

Similarly, $y \in C(A)$ implies $\exists x$ such that $y_{n \times 1} = A^T_{n \times m} x_{m \times 1}$

The set $\{x:A_{m \times n}x_{n \times 1} = \phi\}$ forms a subspace of $F^n$ , known as the null space $N(A)$ of $A$ .

Rank and nullity

The dimension of $C(A)$ is known as the column rank of $A$ . The dimension of $R(A)$ is known as the row rank of $A$ . These two ranks are found to be equal, and the common value is known as the rank $r(A)$ of $A$ .

The dimension of $N(A)$ is known as the nullity $\eta (A)$ of A.

If $A$ is a square matrix of order $n \times n$ , then $r(A) + \eta (A) = n$ .

@@ Line 1: / Line 1: @@
-A matrix is a rectangular array of scalars from any [[field]], such that each column belongs to the [[vector space]] <math>F^m</math>, where <math>m</math> is the number of rows. If a matrix <math>A</math> has <math>m</math> rows and <math>n</math> columns, its order is said to be <math>m \times n</math>, and it is written as <math>A_{m \times n}</math>.
+A '''matrix''' over a field <math>F</math> is a [[function]] from <math>A\times B</math> to <math>F</math>, where <math>A</math> and <math>B</math> are the sets <math>A=\{1,2,\ldots,m\}</math> and <math>B=\{1,2,\ldots,n\}</math>.
+A matrix is usually represented as a rectangular array of scalars from the [[field]], such that each column belongs to the [[vector space]] <math>F^m</math>, where <math>m</math> is the number of rows. If a matrix <math>A</math> has <math>m</math> rows and <math>n</math> columns, its order is said to be <math>m \times n</math>, and it is written as <math>A_{m \times n}</math>.
+The element in the <math>i^{th}</math> row and <math>j^{th}</math> column of <math>A</math> is written as <math>(A)_{ij}</math>. It is more often written as <math>a_{ij}</math>, in which case <math>A</math> can be written as <math>[a_{ij}]</math>.
+==Determinant==
+If <math>A_{m\times n}</math> is a matrix over <math>F</math> with <math>m=n</math>, a '''Determinant''' assigns <math>A_{m\times n}</math> to a member of <math>F</math> and is denoted by <math>|A|</math> or <math>\begin{vmatrix} a_{11} & a_{12} & \ldots & a_{1n} \\ a_{21} & a_{22} & \ldots & a_{2n} \\ \vdots & \vdots
+& \ddots & \vdots \\ a_{n1} & a_{n2} & \ldots & a_{nn}\end{vmatrix}</math>
+It is defined recursively.
+<center><math>\begin{vmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \end{vmatrix}\dot{=}a_{11} a_{22} - a_{21} a_{12}</math>
+<math>\begin{vmatrix} a_{11} & a_{12} & \ldots & a_{1n} \\ a_{21} & a_{22} & \ldots & a_{2n} \\ \vdots & \vdots
+& \ddots & \vdots \\ a_{n1} & a_{n2} & \ldots & a_{nn}\end{vmatrix}\dot{=}\sum_{k=1}^n (-1)^{k+1} a_{1k} |A'_{1k}|</math></center> where <math>A'_{cd}</math> is the matrix <math>A</math> with the <math>c^{th}</math> row and <math>d^{th}</math> column removed.
+== Transposes ==
+Let <math>A</math> be <math>[a_{ij}]</math>. Then <math>[a_{ji}]</math> is said to be the transpose of <math>A</math>, written as <math>A^T</math> or simply <math>A'</math>. If A is over the complex field, replacing each element of <math>A^T</math> by its complex conjugate gives us the conjugate transpose <math>A^*</math> of <math>A</math>. In other words, <math>A^*=[\bar {a_{ji}}]</math>
+<math>A</math> is said to be symmetric if and only if <math>A=A^T</math>. <math>A</math> is said to be hermitian if and only if <math>A=A^*</math>. <math>A</math> is said to be skew symmetric if and only if <math>A=-A^T</math>. <math>A</math> is said to be skew hermitian if and only if <math>A=-A^*</math>.
+== Matrix Product ==
+Let <math>A</math> be a matrix of order <math>m_1 \times m_2</math> and <math>B</math> a matrix of order <math>n_1 \times n_2</math>. Then the product <math>AB</math> exists if and only if <math>m_2=n_1</math> and in that case we define the product <math>C=AB</math> as the matrix of order <math>m_1 \times n_2</math> for which
+<cmath>(C)_{ij}=\sum ^{n_1} _{k=1} (A)_{ik} (B)_{kj}</cmath>
+for all <math>i</math> and <math>j</math> such that <math>1\le i\le m_1</math> and <math>1\le j\le n_2</math>.
+== Vector spaces associated with a matrix ==
+As already stated before, the columns of <math>A</math> form a subset of <math>F^m</math>. The subspace of <math>F^m</math> generated by these columns is said to be the column space of <math>A</math>, written as <math>C(A)</math>. Similarly, the transposes of the rows form a subset of the vector space <math>F^n</math>. The subspace of <math>F^n</math> generated by these is known as the row space of <math>A</math>, written as <math>R(A)</math>.
+<math>y \in C(A) </math>implies <math>\exists x </math> such that <math> y_{m \times 1} = A_{m \times n} x_{n \times 1}</math>
+Similarly, <math>y \in C(A) </math>implies <math>\exists x </math> such that <math> y_{n \times 1} = A^T_{n \times m} x_{m \times 1}</math>
+The set <math>\{x:A_{m \times n}x_{n \times 1} = \phi\}</math> forms a subspace of <math>F^n</math>, known as the null space <math>N(A)</math> of <math>A</math>.
+== Rank and nullity ==
+The dimension of <math>C(A)</math> is known as the column rank of <math>A</math>. The dimension of <math>R(A)</math> is known as the row rank of <math>A</math>. These two ranks are found to be equal, and the common value is known as the rank <math>r(A)</math> of <math>A</math>.
+The dimension of <math>N(A)</math> is known as the nullity <math>\eta (A)</math> of A.
+If <math>A</math> is a square matrix of order <math>n \times n</math>, then <math>r(A) + \eta (A) = n</math>.
+[[Category:Linear algebra]]

Page

Toolbox

Search