Basic notation

An \(m \times n\) matrix \(A\) is a rectangular array of numbers with \(m\) rows and \(n\) columns:

\[\begin{split} A=\begin{pmatrix} a_{11} &\cdots &a_{1n} \\ \vdots & &\vdots \\ a_{m1} &\cdots &a_{mn} \end{pmatrix} \end{split}\]

The elements of a matrix \(A\) \((m\times n)\) are \(\{a_{ij}; \ \ i=1,\cdots,m \ \mbox{(rows)}, \ \ j=1,…,n \ \mbox{(columns)} \}\). For example, the elements of the matrix

\[\begin{split} A=\begin{pmatrix} 1 &2 &3 \\ 4 &5 &6 \end{pmatrix} \end{split}\]

are \(a_{11}=1, a_{12}=2, a_{13}=3, a_{21}=4, a_{22}=5,\) and \(a_{23}=6\).

A column vector with \(m\) elements, \(y=\begin{pmatrix}y_1 \\ y_2 \\ \vdots \\y_m\end{pmatrix}\), is a matrix with only one column, i.e. an \(m\times1\) matrix.

A row vector with \(n\) elements, \(x = \begin{pmatrix}x_1 & \cdots &x_n \end{pmatrix}\), is a matrix with only one row, i.e. a \(1\times m\) matrix.

Basic definitions

The order of a matrix is the number of rows by the number of columns i.e. \((m\times n)\). For example,

\[\begin{split} A=\begin{pmatrix} 1 &2 &3 \\ 4 &5 &6 \end{pmatrix} \end{split}\]

is a matrix of order \(2\times3\), and

\[\begin{split} B=\begin{pmatrix} 1 &2 \\ 3 &4 \\ 5 &6 \end{pmatrix} \end{split}\]

is of order \(3\times 2\).

A transposed matrix \(A^T\) (or \(A'\)) arises from the matrix \(A\) by interchanging the column vectors and the row vectors, i.e. \(a_{ij}^T = a_{ji}\) (so a column vector is converted into a row vector and vice versa). For example, \(x = \begin{pmatrix} 1 &2 &3 \end{pmatrix}\) is a \(1\times3\) row vector and \(x^T =\begin{pmatrix} 1 \\2 \\3 \end{pmatrix}\) is a \(3\times1\) column vector,

\(A = \begin{pmatrix} 1 &2 &3 \\ 4 &5 &6 \end{pmatrix}\) is of order \(2\times3\) and \(A^T= \begin{pmatrix} 1 &4 \\2 &5 \\3 &6 \end{pmatrix}\) is of order \(3\times2\).

A partitioned matrix is a matrix written in terms of sub-matrices. For example,

\[\begin{split} A = \begin{pmatrix} A_{11} &A_{12} \\ A_{21} &A_{22} \end{pmatrix} \end{split}\]

where \(A_{11}, A_{12}, A_{21}\) and \(A_{22}\) are sub-matrices, and

  • \(A_{11}\) and \(A_{12}\) have the same number of rows (as do \(A_{21}\) and \(A_{22}\))

  • \(A_{11}\) and \(A_{21}\) have the same number of columns (as do \(A_{12}\) and \(A_{22}\))

For example,

\[\begin{split} A = \begin{pmatrix} 1 &2 &3 &4 &5 \\ 6 &7 &8 &9 &10 \\ 11 &12 &13 &14 &15 \end{pmatrix} \end{split}\]

can be written as

\[\begin{split} A= \begin{pmatrix} A_{11} &A_{12} \\ A_{21} &A_{22}\end{pmatrix} \end{split}\]

where \(A_{11}=\begin{pmatrix} 1&2&3 \\6&7&8 \end{pmatrix}, A_{12} = \begin{pmatrix}4&5 \\ 9&10 \end{pmatrix}, A_{21}=\begin{pmatrix} 11 &12 &13 \end{pmatrix}\) and \(A_{22}=\begin{pmatrix} 14 &15 \end{pmatrix}\).

Note that partitioning is not restricted to dividing a matrix into just four sub-matrices.

Special matrices

Square matrices

A square matrix has exactly as many rows as it has columns, i.e. the order of the matrix is \(m\times m\). For example,

\[\begin{split} A= \begin{pmatrix} 1&2\\3&4 \end{pmatrix} \end{split}\]

is a square matrix of order \(2\times2\).

The main diagonal (or leading diagonal) of a square matrix \(A\) \((m\times m)\) are the elements lying on the diagonal from top left to bottom right: \(a_{11}, a_{22},\cdots, a_{mm}\). i.e. all \(a_{jj}\) for \(j=1,\cdots,m\). For example, the diagonal elements of matrix

\[\begin{split} C=\begin{pmatrix} 1&1&1 \\ 2&4&5 \\ 3&5&6 \end{pmatrix} \end{split}\]

are 1, 4 and 6.

The trace of a square matrix is the sum of all the diagonal elements, i.e.

\[ tr(A)= a_{11}+ a_{22}+ \cdots + a_{mm} = \sum_{j=1}^m a_{jj} \]

For example,

\[\begin{split} A= \begin{pmatrix} 1&2 \\ 3&4 \end{pmatrix} \end{split}\]

is a \(2\times2\) square matrix, the main diagonal of this matrix are the elements 1 and 4, so \(tr(A)=1+4=5\).

Other special matrices

A symmetric matrix is a square matrix for which \(a_{ij} = a_{ji}\) for \(i\neq j\), i.e. for all off-diagonal elements, so that the matrix is symmetric about the diagonal. For a symmetric matrix \(A\), taking the transpose does not affect the matrix, \(A^T= A\). For example,

\[\begin{split} A=\begin{pmatrix} 1&2&3\\2&4&5\\3&5&1\end{pmatrix} \end{split}\]

is a \(3\times3\) symmetric matrix, and \(A^T= A\).

A diagonal matrix is a square matrix where all the off-diagonal elements are zero, e.g.

\[\begin{split} A=\begin{pmatrix}1&0&0\\0&2&0\\0&0&3\end{pmatrix}. \end{split}\]

A zero (or null) matrix is a matrix where all the elements are zero, e.g.

\[\begin{split} A=\begin{pmatrix}0&0&0\\0&0&0\\0&0&0\end{pmatrix}. \end{split}\]

An identity (or unit) matrix is a special case of a diagonal matrix having all the diagonal elements equal to 1, e.g.

\[\begin{split} I=\begin{pmatrix}1&0&0\\0&1&0\\0&0&1\end{pmatrix}. \end{split}\]

The order of an identity matrix is sometimes indicated in the name, e.g. \(I_m\) is the \(m\times m\) identity matrix.

A summing vector is a vector where all elements are 1, e.g. \(x=\begin{pmatrix}1&1&1\end{pmatrix}\).

A J matrix is a matrix (not necessarily square) whose every element is 1, e.g.

\[\begin{split} J=\begin{pmatrix}1&1\\1&1\\1&1\end{pmatrix}. \end{split}\]

Basic operations for matrices

Addition and subtraction

Addition and subtraction can only take place when the matrices involved are of the same order, i.e. they have the same number of rows and columns:

\[\begin{split} A\pm B=\begin{pmatrix}a_{11}&\cdots&a_{1n}\\ \vdots &&\vdots \\a_{m1}&\cdots&a_{mn} \end{pmatrix} \pm \begin{pmatrix}b_{11}&\cdots&b_{1n}\\\vdots&&\vdots \\b_{m1}&\cdots&b_{mn} \end{pmatrix} = \begin{pmatrix}a_{11}\pm b_{11}&\cdots&a_{1n}\pm b_{1n}\\ \vdots&&\vdots \\a_{m1}\pm b_{m1}&\cdots&a_{mn}\pm b_{mn} \end{pmatrix}. \end{split}\]

For example, if \(A=\begin{pmatrix} 1&3\\4&5\end{pmatrix}\) and \(B=\begin{pmatrix} 0&1\\2&3\end{pmatrix}\) where \(A\) and \(B\) are both of order \(2\times2\) then

\[\begin{split} A+B=\begin{pmatrix} 1+0&3+1\\4+2&5+3\end{pmatrix}=\begin{pmatrix} 1&4\\ 6&8\end{pmatrix} \qquad \mbox{and} \qquad B-A=\begin{pmatrix} 0-1&1-3\\2-4&3-5\end{pmatrix}=\begin{pmatrix}-1&-2 \\ -2&-2\end{pmatrix}. \end{split}\]

Rules for addition and subtraction of matrices:

  • \(A+B=B+A\)

  • \((A+B)+C=A+(B+C)\)

  • \(A+0=0+A=A\)

  • \(A+(-A)=0\)

  • \((A+B)^T=A^T+B^T\)

Multiplication by a scalar \(c\) means multiplying every element of the matrix by \(c\):

\[\begin{split} cA=\begin{pmatrix} ca_{11}&\cdots&ca_{1n}\\ \vdots&&\vdots \\ca_{m1}&\cdots&ca_{mn}\end{pmatrix}. \end{split}\]

For example, if \(A= \begin{pmatrix}1&3\\4&5\end{pmatrix}\) and \(c = 2\) then \(cA=\begin{pmatrix}2\times 1&2\times 3\\2\times 4&2\times 5 \end{pmatrix}=\begin{pmatrix} 2&6\\8&10\end{pmatrix}\).

Rules for multiplication by a scalar:

  • \(cA=Ac\)

  • \(d(cA)=(dc)A\)

  • \((c\pm d)A=cA\pm dA\)

  • \(c(A\pm B)=cA\pm cB\)


Multiplication by a vector An \(m\times n\) matrix \(A\) can be multiplied by \(n\times 1\) column vector \(x\) yielding a \(m\times1\) column vector providing the number of columns in \(A\) is equal to the number of rows in \(x\):

\[\begin{split} Ax=\begin{pmatrix}a_{11}&\cdots&a_{1n}\\ \vdots&&\vdots \\ a_{m1}&\cdots&a_{mn}\end{pmatrix} \begin{pmatrix}x_{1}\\ \vdots \\ x_{n}\end{pmatrix} = \begin{pmatrix} a_{11} x_{1} + a_{12} x_{2}+ \cdots + a_{1n} x_{n} \\ \vdots \\ a_{m1} x_{1} + a_{m2} x_{2}+ \cdots + a_{mn} x_{n} \end{pmatrix}=y \end{split}\]

i.e. \(y_i= \sum_{j=1}^n (a_{ij} x_j)\) for \(i=1,…,m\).

For example, suppose \(A= \begin{pmatrix}1&2&3\\2&2&1\end{pmatrix}\) and \(x=\begin{pmatrix}1\\2\\1\end{pmatrix}\). Note that matrix \(A\) is of order \(2\times3\) and column vector \(x\) is of order \(3\times1\), i.e. the number of columns in \(A\) is equal to the number of rows in \(x\). Then the product \(Ax\) is defined yielding a \(2\times1\) vector:

\[\begin{split} Ax=\begin{pmatrix}1&2&3\\2&2&1\end{pmatrix}\begin{pmatrix} 1\\2\\1\end{pmatrix} = \begin{pmatrix}1\times 1+2\times2+ 3\times1 \\ 2\times1+2\times2+ 1\times1\end{pmatrix}=\begin{pmatrix}8 \\ 7 \end{pmatrix} \end{split}\]

Multiplication of matrices The product \(AB=C\) is defined only when the number of columns in \(A\) is the same as the number of rows in \(B\). If \(A\) is a matrix of order \(m\times n\), and \(B\) is a matrix of order \(n\times r\), then the product \(AB=C\) will be of order \(m\times r\). The elements of \(C\) are given as

\[ c_{ij}=\sum_{k=1}^n a_{ik} b_{kj} \qquad \mbox{where} \qquad i=1,…,m \ \mbox{and} \ j=1,…,r. \]

For example, if \(A=\begin{pmatrix} 1&2&3 \\ 2&2&1 \end{pmatrix}\), i.e. of order \(2\times3\), and \(B= \begin{pmatrix} 1&2\\2&3\\3&4 \end{pmatrix}\), i.e. of order \(3\times2\), then

\[\begin{split} AB=\begin{pmatrix} 1&2&3 \\ 2&2&1\end{pmatrix}\begin{pmatrix}1&2 \\2&3 \\ 3&4\end{pmatrix}=\begin{pmatrix}1 \times1+2\times2+ 3\times3&1\times2+2\times3+ 3\times4\\ 2\times1+2\times2+ 1\times3&2\times2+2\times3+ 1\times4\end{pmatrix}=\begin{pmatrix}14&20 \\ 9&14\end{pmatrix} \end{split}\]

which is of order \(2\times2\), as expected.

Rules for multiplication of matrices:

  • \(AB\neq BA\)

  • \((AB)C=A(BC)=ABC\)

  • \(A(B+C)=AB+AC\)

  • \((B+C)A=BA+CA\)

  • \(IA=AI=A\) where \(I\) is the identity matrix with the same number of rows (and columns since \(I\) is square) as columns in \(A\).

So if \(A=\begin{pmatrix} 1&2&3 \\ 2&2&1\end{pmatrix}\) and \(I=\begin{pmatrix} 1&0&0 \\ 0&1&0 \\ 0&0&1\end{pmatrix}\), i.e. the identity matrix of order \(3\times 3\), then

\[\begin{split} AI= \begin{pmatrix} 1&2&3 \\ 2&2&1 \end{pmatrix} \begin{pmatrix} 1&0&0 \\ 0&1&0 \\ 0&0&1 \end{pmatrix} = \begin{pmatrix} 1&2&3 \\ 2&2&1 \end{pmatrix} =A. \end{split}\]

If \(A=\begin{pmatrix} 1&2 \\ 3&4\end{pmatrix}\) and \(J=\begin{pmatrix} 1&1 \\ 1&1\end{pmatrix}\), i.e. a matrix of order \(2 \times 2\), then

\[\begin{split} AJ=\begin{pmatrix} 1 &2 \\ 3&4\end{pmatrix}\begin{pmatrix} 1&1 \\ 1&1\end{pmatrix}=\begin{pmatrix} 3&3 \\ 7&7\end{pmatrix}. \end{split}\]

Further definitions

The determinant of a second order square matrix is

\[\begin{split} det(A) = |A| = \left| \begin{matrix} a_{11} &a_{12} \\ a_{21} &a_{22} \end{matrix} \right| = a_{11} a_{22} - a_{12} a_{21} \end{split}\]

For example, if \(A= \begin{pmatrix} 1&2 \\ 3&4 \end{pmatrix}\) then \(det(A)= | A | =1 \times 4 - 2\times 3=-2.\)

The inverse of a matrix \(A\), \(A^{-1}\), if it exists, is a matrix whose product with \(A\) is the identity matrix, i.e.

\[ AA^{-1}=A^{-1} A=I \]

Note that both \(A\) and \(A^{-1}\) must be square.

For second order matrices:

\[\begin{split} A^{-1}= \frac{1}{det(A)} \begin{pmatrix} a_{22} &-a_{12} \\ -a_{21} &a_{11} \end{pmatrix} \end{split}\]

For example, if \(A= \begin{pmatrix}1&2 \\ 3&4\end{pmatrix}\) then \(det(A)=|A|=1\times 4-2\times 3=-2\). Then the inverse of \(A\) is given by

\[\begin{split} A^{-1} = -\frac{1}{2} \begin{pmatrix} 4&-2\\ -3&1 \end{pmatrix} = \begin{pmatrix}-2&1 \\ 3/2&-1/2 \end{pmatrix} \end{split}\]


\[\begin{split} AA^{-1}=\begin{pmatrix} 1&2 \\ 3&4 \end{pmatrix} \begin{pmatrix}-2&1 \\ 3/2&-1/2 \end{pmatrix} =\begin{pmatrix}-2+3&1-1 \\ -6+6&3-2 \end{pmatrix} = \begin{pmatrix}1&0 \\ 0&1 \end{pmatrix}. \end{split}\]

A singular or non-invertible matrix has the property \(det(A)=0\).

For example, if \(A=\begin{pmatrix} 1&2 \\ 2&4 \end{pmatrix}\) then \(det(A)=1 \times 4-2 \times 2=0\) and therefore \(A\) is a singular matrix.

An idempotent matrix \(A\) is a square matrix with the property that \(AA=A^2=A\).

For example, if \(A=\begin{pmatrix}0.5&0.5 \\0.5&0.5 \end{pmatrix}\) then

\[\begin{split} AA= \begin{pmatrix}0.5&0.5 \\ 0.5&0.5\end{pmatrix}\begin{pmatrix} 0.5&0.5 \\ 0.5&0.5\end{pmatrix}= \begin{pmatrix}0.25+0.25&0.25+0.25 \\ 0.25+0.25&0.25+0.25\end{pmatrix}= \begin{pmatrix}0.5&0.5 \\ 0.5&0.5\end{pmatrix}=A. \end{split}\]

An orthogonal matrix \(A\) is a square matrix whose product with its transpose (\(A^T\)) is equal to the identity matrix, i.e. \(AA^T=A^T A=I\). Equivalently, it is a square matrix whose transpose is equal to its inverse \(A^T=A^{-1}\).

For example, \(A= \begin{pmatrix}1&0 \\ 0&-1\end{pmatrix}\) is orthogonal since

\[\begin{split} A^T= \begin{pmatrix}1&0 \\ 0&-1\end{pmatrix} = A \ \ \mbox{and} \ \ A^{-1}= \frac{1}{-1}\begin{pmatrix}-1&0 \\ 0&1\end{pmatrix} = \begin{pmatrix}1&0 \\ 0&-1\end{pmatrix} = A = A^T \end{split}\]

so that

\[\begin{split} AA^T= \begin{pmatrix}1&0 \\ 0&-1\end{pmatrix} \begin{pmatrix}1&0 \\ 0&-1\end{pmatrix} =I. \end{split}\]