A matrix is made up of numbers arranged in rows and columns, and to describe its size we use the order of a matrix. The order of a matrix tells us how many rows and how many columns it has, written as m × n. For example, if a matrix has 3 rows and 4 columns, its order is 3 × 4. Learning how to determine the order of a matrix is the first step in understanding matrix concepts in mathematics, as it helps in solving problems related to addition, multiplication, and other matrix operations. In this article, we will explain how to determine the order of a matrices with simple examples.
This Story also Contains
The order of a matrix is a way to describe its size using the number of rows and columns. In mathematics, every matrix is written in a specific format, and its order helps us understand the structure clearly. We will discuss about the order of matrix in detail below:
The order of a matrix is defined as the number of rows and columns it contains. If a matrix has $m$ rows and $n$ columns, then its order is written as:
$Order of Matrix = m \times n$
For example, if a matrix $A$ has 2 rows and 3 columns, its order will be $2 \times 3$.
Examples:
1. $\left[\begin{array}{ccc}2 & 4 & -3 \\ 5 & 4 & 6\end{array}\right]$
2. $\left[\begin{array}{cc}2 & 4 i+3 \\ 5 & 4 \\ 3 i & -75\end{array}\right]$
3. $\left[\begin{array}{c}2 \\ -5 \\ 3 i \\ 71\end{array}\right]$
In the first matrix above, elements 2, 4 and -3 lie in the first row and 5, 4 and 6 in the second row. Also, 2, and 5, lie in the first column, 4,4 in the second column, and -3, 6 in the third column. Therefore, the order of a matrix is 2 x 3
Similarly, the Second matrix has order 3 x 2
and the third matrix has order 4 x 1
The general way to represent a matrix order is:
$A = [a_{ij}]_{m \times n}$
A general $m \times n$ matrix is written as:
$\left[\begin{array}{cccc} a_{11} & a_{12} & \ldots & a_{1n} \\ a_{21} & a_{22} & \ldots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{m1} & a_{m2} & \ldots & a_{mn} \end{array}\right]$
This can be expressed in a more compact form as:
$\left[a_{ij}\right]_{m \times n}$
Here, $a_{ij}$ represents the element of the $i^{th}$ row and $j^{th}$ column, where
$i = 1,2,\ldots,m \quad ; \quad j = 1,2,\ldots,n$
For example, in matrix $A$ below, the element in row $2$ and column $3$ is $a_{23}$:
$A = \left[\begin{array}{ccc} a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a_{31} & a_{32} & a_{33} \end{array}\right]$
Note: A matrix is a systematic arrangement of symbols, numbers, or objects in rows and columns. It does not have a numerical value by itself. Usually, a matrix is denoted by capital letters such as $A, B, C$, etc.
Here:
$i$ represents the row number ($1 \leq i \leq m$)
$j$ represents the column number ($1 \leq j \leq n$)
$m$ = number of rows
$n$ = number of columns
For example:
$A = \begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \end{bmatrix}$
This matrix has $m = 2$ rows and $n = 3$ columns, so its order is $2 \times 3$.
Understanding the order of a matrix is essential because:
Two matrices can be added or subtracted only if they have the same order.
Matrix multiplication rules depend on the order of matrices.
The existence of a determinant or inverse is possible only for square matrices (where $m = n$).
To determine the order of a matrix, you need to count the number of rows and columns it has. The result is always written in the form $m \times n$, where $m$ = rows and $n$ = columns.
Write down the matrix clearly.
Count the number of horizontal rows.
Count the number of vertical columns.
Express the order as $m \times n$.
For example, if a matrix has 4 rows and 2 columns, its order is $4 \times 2$.
2 × 2 Matrix
$A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix}$
Here, $m = 2$, $n = 2$. Order = $2 \times 2$.
3 × 3 Matrix
$B = \begin{bmatrix} 1 & 0 & -1 \\ 2 & 3 & 4 \\ 5 & 6 & 7 \end{bmatrix}$
Here, $m = 3$, $n = 3$. Order = $3 \times 3$.
Rectangular Matrix (3 × 4)
$C = \begin{bmatrix} 1 & 2 & 3 & 4 \\ 5 & 6 & 7 & 8 \\ 9 & 10 & 11 & 12 \end{bmatrix}$
Here, $m = 3$, $n = 4$. Order = $3 \times 4$.
Rows : Horizontal (count lines across).
Columns : Vertical (count lines downward).
Always write rows first, columns second.
Order is never written as $n \times m$ by mistake.
Students often confuse matrix order with matrix size. Let’s clarify.
Order = number of rows × number of columns.
Size = total number of elements.
Formula:
$Size = m \times n$
Example: If a matrix is of order $3 \times 4$, then size = $3 \times 4 = 12$ elements.
Writing columns first instead of rows (e.g., $n \times m$ instead of $m \times n$).
Confusing order with number of elements.
Forgetting that $2 \times 3$ and $3 \times 2$ are different matrices.
Different types of matrices have specific orders, and questions on this are common in competitive exams and board exams.
A square matrix has equal rows and columns.
Order: $n \times n$
Example:
$\begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix}$ → Order $2 \times 2$
Row matrix: A matrix containing only one row is called a row matrix.
So a matrix $\mathrm{A}=\left[\mathrm{a}_{\mathrm{ij}}\right]_{\mathrm{m} \times \mathrm{n}}$ is said to be a row matrix when $\mathrm{m}=1$.
It can be denoted by
$
\left[\begin{array}{llllll}
a_{11} & a_{12} & a_{13} & \ldots & \ldots & a_{1 n}
\end{array}\right]_{1 \times \mathrm{n}}
$
Example: $\left[\begin{array}{llll}1 & 32 & 81 & -32\end{array}\right]$ has only 1 row. It has order $1 \times 4$
$\begin{bmatrix} 5 & 6 & 7 & 8 \end{bmatrix}$ → Order $1 \times 4$
Column matrix: A matrix containing only one column is known as a column matrix. So a matrix $\mathrm{A}=\left[\mathrm{a}_{\mathrm{ij}}\right]_{\mathrm{m} \times \mathrm{n}}$ is said to be a column matrix when $\mathrm{n}=1$.
Order: $m \times 1$
It is denoted by
$
\begin{aligned}
& {\left[\begin{array}{c}
a_{11} \\
a_{21} \\
a_{31} \\
\ldots \\
\ldots \\
a_{m 1}
\end{array}\right]_{\mathrm{m} \times 1}} \\
& \text { Example, }\left[\begin{array}{c}
2 \\
32 \\
3 \\
7
\end{array}\right]
\end{aligned}
$
This matrix has order 4 x 1
$\begin{bmatrix} 2 \\ 4 \\ 6 \\ 8 \end{bmatrix}$ → Order $4 \times 1$
Note: A matrix that contains only one row or one column is also known as a vector i.e. row vectors and column vectors.
Zero Matrix: Can be of any order $m \times n$, but all elements are zero.
Example: $\begin{bmatrix} 0 & 0 \\ 0 & 0 \end{bmatrix}$ → Order $2 \times 2$
Identity Matrix: Always a square matrix ($n \times n$), with 1’s on the diagonal and 0’s elsewhere.
Example: $\begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}$ → Order $3 \times 3$
Example 1: The given matrix $\left[\begin{array}{l}3 \\ 8 \\ 9\end{array}\right]$ is
Solution:
As we have understood, there are 3 rows and only one column in the given matrix, i.e. m=3 and n=1, therefore, the order of the given matrix is 3x1.
Hence, the answer is the option 4.
Solution:
Column Matrix:
Single-column matrix order is $m \times 1$
- wherein
eg. $\begin{bmatrix} 7\\ -2\\ 1 \end{bmatrix}$
Let $C = \begin{bmatrix} a\\ b\\ c \end{bmatrix}$
Thus, $\begin{bmatrix} 1\\ 3\\ 4 \end{bmatrix}-\begin{bmatrix} a\\ b\\ c \end{bmatrix}= \begin{bmatrix} -1\\ 0\\ 5 \end{bmatrix}$
$1-a= -1, \quad 3-b = 0, \quad 4-c= 5$
$a=2, \quad b= 3, \quad c= -1$
$\begin{bmatrix} 2\\ 3\\ -1 \end{bmatrix}$
Example 3: The given matrix \left[\begin{array}{lll}{1} & {2} & {3}\end{array}\right] is a
Solution:
The given matrix has one row and three columns, i.e. m=1 and n=3.
Therefore the given matrix is of order 1x3.
Hence, the answer is the option 1.
Example 4: If the given matrix $\mathrm{A}$ is of order $5 \times 4$ and the element $a_{i j}=i^2+j^2$, then which of the following is true?
1) $a_{23}+a_{11}=a_{34}$
2) $a_{36}=a_{63}$
3) $a_{21}+a_{23}=a_{33}$
4) $a_{12}+a_{32}=a_{44}$
Solution:
$
\begin{aligned}
& a_{21}=2^2+1^2=5 \\
& a_{23}=2^2+3^2=13 \\
& a_{33}=3^2+3^2=18
\end{aligned}
$
Thus $a_{33}=a_{23}+a_{21}$
Hence, the answer is option 3.
Solution:
Row Matrix -
Single-row matrix order is 1 \times n
- wherein
$ \begin{bmatrix} 1 & 2 & 3 & 7 \end{bmatrix} $
We can only add matrices in the same order so,
$A = \begin{bmatrix} 5 &7 & 9 &11\end{bmatrix}$, then $B= \begin{bmatrix} 3 &1 &1 &0 \end{bmatrix}$
The order of a matrix is a basic yet important concept in linear algebra and competitive exams. To master it, students should also study related topics like types of matrices, matrix operations, determinants, and inverse of matrices. Here is a list of key topics connected with the order of matrix that will strengthen your preparation.
The NCERT resources for Class 12 Maths Chapter 3 – Matrices provide notes, solutions, and exemplar problems to understand matrix order, types, and operations easily.
NCERT Class 12 Maths Notes for Chapter 3 - Matrices
NCERT Solutions for Class 12 Maths for Chapter 3 - Matrices
NCERT Exemplar Class 12 Maths Solutions for Chapter 3 - Matrices
Practicing questions on the order of a matrix builds a clear understanding of rows and columns, improves problem-solving speed, and helps in scoring well in exams.
Determine The Order Of Matrix - Practice Question MCQ
You can practice questions on the related topics using the links shared below:
Frequently Asked Questions (FAQs)
Block matrices are partitioned into submatrices. The order of the overall matrix and the orders of the submatrices determine how operations can be performed on the block matrix.
For 2 × 2 matrices, there's a simple relationship between trace, determinant, and eigenvalues. This relationship becomes more complex for higher-order matrices.
In graph theory, the adjacency matrix of a graph with n vertices is an n × n matrix. The order of this matrix directly represents the number of vertices in the graph.
For any matrix, the column rank and row rank are always equal, and this common value (the rank) is limited by the smaller dimension of the matrix's order.
Various matrix factorization methods (like LU, QR, SVD) are influenced by the matrix's order. The dimensions of the factorized matrices depend on the original matrix's order.
The Frobenius norm of a matrix involves summing the squares of all its elements. The order determines how many elements are included in this calculation.
In Markov chains, the order of the transition matrix represents the number of states in the system. Each row and column corresponds to a specific state.
The nullity of a matrix (dimension of its null space) is related to its order. For an m × n matrix, the rank-nullity theorem states that rank + nullity = n.
Orthogonal matrices, which have orthonormal columns and rows, are always square. Their order determines the dimension of the space in which they represent rotations or reflections.
The Jordan canonical form, applicable to square matrices, has a structure determined by the matrix's order, eigenvalues, and their algebraic multiplicities.