Matrix is one of the most important topics of linear algebra that has several applications across the domain. In this article, we will discuss different types of matrix such as square matrix, row matrix, column matrix, and many more.

In Linear Algebra, Matrices are one of the most important topics of mathematics. The application of matrix is not just limited to mathematical solving problems; it has its applications across all the domains such as Data Science, Machine Learning, Cryptography, Wireless Communication, and many more. In this article, we will discuss different types of matrix with examples. In the final section, we will also discuss some special matrices, such as the Idempotent Matrix, Involutory Matrix, and Orthogonal Matrix.

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**Table of Content**

**What is Matrix?**

A matrix is a rectangular arrangement of data in rows and columns to form an array.

- The horizontal arrangement is called a row.
- A vertical arrangement is called a column.

**Notation:** Any matrix having ** m** rows and

**columns is given by: [A]**

*n*_{m x n}.

- Each member of the matrix is called an element of the matrix.
**Order of a Matrix:**If a matrix hasrows and*m*columns, then it is said to be a matrix of order*n*.*mn*

Now, moving forward, we will know the different types of matrix.

**Types of Matrix**

**Singleton Matrix**

A matrix having only one element is known as a singleton matrix.

- A= [a
_{ij}]is a singleton if_{mn}*i = j =*1*.* **Example**: [1], [0], [9]

**Row Matrix**

A matrix having only one row is called a row matrix.

- It can have any number of the column.
- A = [a
_{ij}]is a row matrix if_{mn}*i =*1*<= j <= n**.* **Example:**A = [1, 2, 3, 4, 5], B = [ -3, -6, -9]

**Column Matrix**

A matrix having only one column is called a column matrix.

- It can have any number of rows.
- A= [a
_{ij}]is a row matrix if_{mn}**1***<= i <= m*and*=*1*.* **Example:**

**Null Matrix/Zero Matrix:**

If all the entries of a matrix are zero, it is called a null matrix.

- It can be a square or rectangular matrix.
- A = [a
_{ij}]is called a zero matrix if_{mn}*a*_{ij}*=*0*.* **Example:**

**Square Matrix**

Any matrix is said to be a square matrix if it has the same number of rows and a same number of columns.

- A= [a
_{ij}]is a square matrix if_{mn}*m = n*. **Example:**

**Note: **The vector of values from the diagonal of the matrix from the top left to the bottom right is known as a Principal diagonal.

**Diagonal Matrix**

If in a square matrix, all the elements of a principal diagonal are non-zero and all the other entries are zero, then it is called a diagonal matrix.

- A= [a
_{ij}]is a diagonal matrix if_{mn}for all*a*_{ij}= 0(*i*!=*j**i*not equal to*j*) and for,*i = j*can take any value from real numbers.*a*_{ij} **Example**

**Note: **Null matrix is also a diagonal matrix.

**Scalar Matrix**

It is a particular case of a diagonal matrix in which all the elements of the diagonal are identical.

- In simple terms, if all the elements of a diagonal matrix are equal, it is called a scalar matrix.
- A= [a
_{ij}]is a scalar matrix if_{mn}for all*a*0_{ij}=(*i*!=*j**i*not equal to*j*) andfor all*a*=_{ij}*a**i = j,**a*

**Identity Matrix**

It is a particular case of the scalar matrix. If all the non-zero elements of the scalar matrix are equal to 1, then it is termed as an Identity matrix.

- A= [a
_{ij}]is a scalar matrix if_{mn}for all*a*0_{ij}=(*i*!=*j**i*not equal to*j*) andfor all*a*= 1_{ij}*i = j* **Example**

**Triangular Matrix**

A square matrix that has all the values in the upper-right or lower left and the remaining values are filled with zero, then such matrices are called Triangular matrix.

These are of two types:

- Upper Triangular Matrix
- Lower Triangular Matrix

**Note:** Zero/Null Matric is also a type of Triangular Matrix.

**Upper Triangular Matrix**

A square matrix in which all the elements below the principal diagonal are zero is known as a upper triangular matrix.

- A= [a
_{ij}]is a scalar matrix if_{mn}when*a*0,_{ij}=*i > j.* **Example:**

**Lower Triangular Matrix**

A square matrix in which all the elements above the principal diagonal are zero is known as Lower Triangular Matrix.

- A= [a
_{ij}]is a scalar matrix if_{mn}when*a*0,_{ij}=*i < j.* **Example:**

**Symmetric Matrix**

A square matrix that is equal to its transpose is called a symmetric matrix, i.e., if A is any square matrix, then A is said to be a symmetric matrix if and only if:

**A = A ^{T}**

**Representation:**

If A= [a_{ij}]* _{mn}* is a symmetric matrix, then a

_{ij}= a

_{ji}, where:

- 1
*<= i <= n*, 1*<= j <= n*and*n*belong Natural Number. - a
_{ij}is an element at the position (*i, j*), which is*i*row and^{th}*j*column.^{th}

**Example:**

Also Read: All About Symmetric Matrix

**Skew-Symmetric Matrix**

A square matrix that is equal to its transpose is called a symmetric matrix i.e., if A is any square matrix, then A is said to be a symmetric matrix if and only if:

**A = -A ^{T}**

**Representation**

If A= [a_{ij}]* _{mn}* is a symmetric matrix, then a

_{ij}= – a

_{ji}, where:

- 1
*<= i <= n*, 1*<= j <= n*and*n*belong Natural Number. - a
_{ij}is an element at the position (*i, j*), which is*i*row and^{th}*j*column.^{th}

**Example**:

**Special Matrices**

**Idempotent Matrix**

A square matrix **A** is said to be Idempotent if:

*A ^{2} = A*

**Example:**

**Involutory Matrix**

A square matrix A is said to be Involutory if:

*A ^{2} = I*

where ** I** is an identity matrix.

**Example:**

**Orthogonal Matrix**

A square matrix A is said to be an orthogonal matrix if it satisfies the following:

*A.A ^{T} = I*

where ** A^{T} ** is the transpose of a matrix.

and ** I** is an Identity matrix.

**Example:**

**Conclusion**

In this article, we have discussed different types of matrices, such as Singleton Matrix, Row Matrix, Column Matrix, Null Matrix, Square Matrix, Diagonal Matrix, Triangular Matrix, Symmetric Matrix, and Skew-Symmetric Matrix.

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## FAQs

**What is a matrix?**

A matrix is a rectangular array of numbers, symbols, or expressions arranged in rows and columns. It is used to represent and manipulate data in various fields such as mathematics, computer science, physics, and engineering.

**What are the types of matrices depending on dimension?**

On the basis of dimensions, matrices are classified into: Row Matrix, Column Matrix, Rectangular Matrix, and Square Matrix.

**What are the types of matrices depending on elements?**

On the basis of elements, matrices are classified into: Zero Matrix, Identity Matrix, Diagonal Matrix, Scalar Matrix, Symmetric Matrix, Skew-Symmetric Matrix, and Triangular Matrix (Upper and Lower).

**What is a Diagonal Matrix?**

If in a square matrix, all the elements of a principal diagonal are non-zero and all the other entries are zero, then it is called a diagonal matrix.

**What is a Scalar Matrix?**

It is a particular case of a diagonal matrix in which all the elements of the diagonal are identical. In simple terms, if all the elements of a diagonal matrix are equal, it is called a scalar matrix.

**What is a Triangular Matrix?**

A square matrix that has all the values in the upper-right or lower left and the remaining values are filled with zero, then such matrices are called a Triangular matrix. There are of two types: Upper Triangular Matrix Lower Triangular Matrix

**What is an Orthogonal Matrix?**

A square matrix with real numbers or elements is said to be an orthogonal matrix if its transpose is equal to its inverse matrix. Or we can say when the product of a square matrix and its transpose gives an identity matrix, then the square matrix is known as an orthogonal matrix.

**About the Author**

Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio

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