Chapter 3: Matrices

Complete Board Exam Focused Notes with Solved Examples

Introduction to Matrices

Definition: A matrix is an ordered rectangular array of numbers or functions. The numbers or functions are called the elements or entries of the matrix.

Matrices are denoted by capital letters like A, B, C, etc. The elements are arranged in rows and columns.

Examples of Matrices:

Matrix A: $A = \begin{bmatrix} 5 & -2 \\ 0 & 5 \\ 3 & 6 \end{bmatrix}$ (3 rows, 2 columns)


Matrix B: $B = \begin{bmatrix} 1+i & 2 \\ 3.5 & -1 \\ 3 & 5/7 \end{bmatrix}$ (3 rows, 3 columns)


Matrix C: $C = \begin{bmatrix} 1+x & 3 \\ \cos x & \tan x \\ \sin 2x & x^3 \end{bmatrix}$ (2 rows, 3 columns)

Key Points:
• Horizontal lines of elements are called rows
• Vertical lines of elements are called columns
• Matrix A has 3 rows and 2 columns
• Matrix B has 3 rows and 3 columns
• Matrix C has 2 rows and 3 columns

Order of a Matrix

A matrix having $m$ rows and $n$ columns is called a matrix of order $m \times n$ (read as "m by n matrix")

General Form:

An $m \times n$ matrix has the form:

$A = \begin{bmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ a_{21} & a_{22} & \cdots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{m1} & a_{m2} & \cdots & a_{mn} \end{bmatrix}_{m \times n}$

Or simply: $A = [a_{ij}]_{m \times n}$, where $1 \leq i \leq m$ and $1 \leq j \leq n$

Important Notes:
• The $i^{th}$ row consists of: $a_{i1}, a_{i2}, a_{i3}, ..., a_{in}$
• The $j^{th}$ column consists of: $a_{1j}, a_{2j}, a_{3j}, ..., a_{mj}$
• $a_{ij}$ is the element in the $i^{th}$ row and $j^{th}$ column
• Total number of elements in an $m \times n$ matrix = $mn$
Example: If a matrix has 8 elements, what are the possible orders?

Solution: Since $mn = 8$, possible ordered pairs $(m,n)$ are: $(1,8), (8,1), (2,4), (4,2)$
Therefore, possible orders are: $1 \times 8$, $8 \times 1$, $2 \times 4$, $4 \times 2$

Types of Matrices ⭐⭐⭐⭐⭐

1. Column Matrix

A matrix with only one column is called a column matrix.
Example: $A = \begin{bmatrix} 0 \\ 3 \\ -1 \\ 1/2 \end{bmatrix}$ is a $4 \times 1$ column matrix

2. Row Matrix

A matrix with only one row is called a row matrix.
Example: $B = \begin{bmatrix} 1/2 & 5 & -2 & 3 \end{bmatrix}$ is a $1 \times 4$ row matrix

3. Square Matrix

A matrix in which the number of rows equals the number of columns is called a square matrix.
Example: $A = \begin{bmatrix} 3 & -1 & 0 \\ 3/2 & 2 & 1 \\ 4 & -3 & 1 \end{bmatrix}$ is a square matrix of order 3
Diagonal of Square Matrix: Elements $a_{11}, a_{22}, a_{33}, ..., a_{nn}$ constitute the diagonal.
In the above matrix, diagonal elements are: 3, 2, 1

4. Diagonal Matrix

A square matrix where all non-diagonal elements are zero.
Example: $A = \begin{bmatrix} 1.1 & 0 & 0 \\ 0 & -2 & 0 \\ 0 & 0 & 3 \end{bmatrix}$

Condition: $b_{ij} = 0$ when $i \neq j$

5. Scalar Matrix

A diagonal matrix where all diagonal elements are equal to the same constant $k$.
Example: $A = \begin{bmatrix} 3 & 0 & 0 \\ 0 & 3 & 0 \\ 0 & 0 & 3 \end{bmatrix}$

Condition: $b_{ij} = 0$ when $i \neq j$, and $b_{ij} = k$ when $i = j$

6. Identity Matrix

A square matrix where all diagonal elements are 1 and all other elements are 0.
Examples: $[1]$, $\begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}$, $\begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}$

Denoted by $I_n$ (or simply $I$ when order is clear from context)

7. Zero Matrix (Null Matrix)

A matrix where all elements are zero.
Examples: $[0]$, $\begin{bmatrix} 0 & 0 \\ 0 & 0 \end{bmatrix}$, $\begin{bmatrix} 0 & 0 & 0 \\ 0 & 0 & 0 \end{bmatrix}$

Denoted by $O$

Equality of Matrices

Two matrices $A = [a_{ij}]$ and $B = [b_{ij}]$ are equal if:

(i) They have the same order
(ii) Each element of A equals the corresponding element of B
That is, $a_{ij} = b_{ij}$ for all $i$ and $j$
Example: $\begin{bmatrix} 2 & 3 \\ 0 & 1 \end{bmatrix}$ and $\begin{bmatrix} 2 & 3 \\ 0 & 1 \end{bmatrix}$ are equal matrices

But $\begin{bmatrix} 3 & 2 \\ 0 & 1 \end{bmatrix}$ and $\begin{bmatrix} 2 & 3 \\ 0 & 1 \end{bmatrix}$ are NOT equal
Example: If $\begin{bmatrix} x-1.5 & y \\ z & a \\ b & c \end{bmatrix} = \begin{bmatrix} 0 & 2 \\ 6 & 3 \\ 2 & 0 \end{bmatrix}$

Then: $x = 1.5$, $y = 2$, $z = 6$, $a = 3$, $b = 2$, $c = 0$

Operations on Matrices 🔥🔥🔥

1. Addition of Matrices

Two matrices can be added only if they have the same order.

If $A = [a_{ij}]$ and $B = [b_{ij}]$ are both $m \times n$ matrices, then:
$A + B = [a_{ij} + b_{ij}]_{m \times n}$
Example:
$A = \begin{bmatrix} 3 & -1 & 1 \\ 2 & 3 & 0 \end{bmatrix}$ and $B = \begin{bmatrix} 2 & 5 & -1 \\ -1 & 2 & 3/2 \end{bmatrix}$

$A + B = \begin{bmatrix} 3+2 & -1+5 & 1-1 \\ 2-1 & 3+2 & 0+3/2 \end{bmatrix} = \begin{bmatrix} 5 & 4 & 0 \\ 1 & 5 & 3/2 \end{bmatrix}$

2. Scalar Multiplication

If $A = [a_{ij}]_{m \times n}$ and $k$ is a scalar, then:
$kA = k[a_{ij}]_{m \times n} = [ka_{ij}]_{m \times n}$

Each element of A is multiplied by the scalar $k$.
Example:
If $A = \begin{bmatrix} 3 & -1 & 1.5 \\ 5 & 7 & -3 \\ 2 & 0 & 5 \end{bmatrix}$, then $3A = \begin{bmatrix} 9 & -3 & 4.5 \\ 15 & 21 & -9 \\ 6 & 0 & 15 \end{bmatrix}$

3. Negative of a Matrix

$-A = (-1)A$

Each element of A is multiplied by -1.

4. Subtraction of Matrices

$A - B = A + (-1)B = A + (-B)$

Subtraction is the addition of the negative matrix.

Properties of Matrix Addition

For matrices A, B, and C of the same order:

1. Commutative Law: $A + B = B + A$

2. Associative Law: $(A + B) + C = A + (B + C)$

3. Additive Identity: $A + O = O + A = A$ (O is zero matrix)

4. Additive Inverse: $A + (-A) = (-A) + A = O$

Properties of Scalar Multiplication

For matrices A and B of same order, and scalars $k$ and $l$:

1. $k(A + B) = kA + kB$

2. $(k + l)A = kA + lA$
Important: These properties make matrix addition and scalar multiplication very similar to regular number operations!

Multiplication of Matrices 🎯🎯🎯

Condition for Multiplication: The product $AB$ is defined only if the number of columns in A equals the number of rows in B.

If $A$ is $m \times n$ and $B$ is $n \times p$, then $AB$ is $m \times p$.
Formula:
If $A = [a_{ij}]_{m \times n}$ and $B = [b_{jk}]_{n \times p}$, then $C = AB = [c_{ik}]_{m \times p}$

where $c_{ik} = \sum_{j=1}^{n} a_{ij}b_{jk} = a_{i1}b_{1k} + a_{i2}b_{2k} + ... + a_{in}b_{nk}$
Example:
$A = \begin{bmatrix} 6 & 9 \\ 2 & 3 \end{bmatrix}$ and $B = \begin{bmatrix} 2 & 6 & 0 \\ 7 & 9 & 8 \end{bmatrix}$

$AB = \begin{bmatrix} 6(2)+9(7) & 6(6)+9(9) & 6(0)+9(8) \\ 2(2)+3(7) & 2(6)+3(9) & 2(0)+3(8) \end{bmatrix}$

$= \begin{bmatrix} 75 & 117 & 72 \\ 25 & 39 & 24 \end{bmatrix}$
Very Important:
• Matrix multiplication is NOT commutative: $AB \neq BA$ (in general)
• Even if both $AB$ and $BA$ are defined, they may not be equal
• Even if they have the same order, $AB$ and $BA$ can be different

Zero Product Property

Important: If $AB = O$ (zero matrix), it does NOT necessarily mean that $A = O$ or $B = O$.

Example: $A = \begin{bmatrix} 0 & -1 \\ 0 & 2 \end{bmatrix}$ and $B = \begin{bmatrix} 3 & 5 \\ 0 & 0 \end{bmatrix}$

Then $AB = \begin{bmatrix} 0 & 0 \\ 0 & 0 \end{bmatrix} = O$, but neither $A$ nor $B$ is a zero matrix.

Properties of Matrix Multiplication

1. Associative Law: $(AB)C = A(BC)$ (whenever both sides are defined)

2. Distributive Laws:
• $A(B + C) = AB + AC$
• $(A + B)C = AC + BC$
(whenever all operations are defined)

3. Multiplicative Identity: For every square matrix $A$ of order $n$:
$IA = AI = A$ (where $I$ is identity matrix of order $n$)
Key Difference from Numbers:
• For numbers: if $ab = 0$, then $a = 0$ or $b = 0$
• For matrices: $AB = O$ does NOT imply $A = O$ or $B = O$

• For numbers: $ab = ac$ and $a \neq 0$ implies $b = c$
• For matrices: $AB = AC$ does NOT necessarily imply $B = C$

Transpose of a Matrix ⭐⭐⭐⭐⭐

Definition: If $A = [a_{ij}]$ is an $m \times n$ matrix, then the transpose of A is obtained by interchanging rows and columns.

Transpose of A is denoted by $A'$ or $A^T$

If $A = [a_{ij}]_{m \times n}$, then $A' = [a_{ji}]_{n \times m}$
Example:
If $A = \begin{bmatrix} 3 & 3 & -3 \\ 3 & -1 & 5 \\ -1 & 1 & 5 \end{bmatrix}$, then $A' = \begin{bmatrix} 3 & 3 & -1 \\ 3 & -1 & 1 \\ -3 & 5 & 5 \end{bmatrix}$

Properties of Transpose

For any matrices A and B of suitable orders:

1. $(A')' = A$

2. $(kA)' = kA'$ (where $k$ is any constant)

3. $(A + B)' = A' + B'$

4. $(AB)' = B'A'$ (Note the order reversal!)
Important: For property 4, the order is reversed!
$(AB)' = B'A'$, NOT $A'B'$

Symmetric and Skew Symmetric Matrices 🔥🔥🔥

Symmetric Matrix

A square matrix $A = [a_{ij}]$ is called symmetric if $A' = A$

That is, $a_{ij} = a_{ji}$ for all $i$ and $j$
Example:
$A = \begin{bmatrix} 3 & 2 & -3 \\ 2 & -1.5 & 1 \\ -3 & 1 & 1 \end{bmatrix}$ is symmetric because $A' = A$

Skew Symmetric Matrix

A square matrix $A = [a_{ij}]$ is called skew symmetric if $A' = -A$

That is, $a_{ji} = -a_{ij}$ for all $i$ and $j$

Important: All diagonal elements of a skew symmetric matrix are zero.
(Because $a_{ii} = -a_{ii}$ implies $a_{ii} = 0$)
Example:
$B = \begin{bmatrix} 0 & e & -f \\ -e & 0 & g \\ f & -g & 0 \end{bmatrix}$ is skew symmetric because $B' = -B$

Important Theorems

Theorem 1: For any square matrix A:
• $A + A'$ is a symmetric matrix
• $A - A'$ is a skew symmetric matrix

Theorem 2: Any square matrix can be expressed as the sum of a symmetric and a skew symmetric matrix:
$A = \frac{1}{2}(A + A') + \frac{1}{2}(A - A')$
Quick Check:
• Symmetric: Elements are mirror images across diagonal
• Skew Symmetric: Elements are negative mirror images across diagonal, and diagonal is all zeros

Invertible Matrices

Definition: If A is a square matrix of order $m$, and if there exists another square matrix B of the same order such that:
$AB = BA = I$
then B is called the inverse matrix of A and is denoted by $A^{-1}$

In that case, A is said to be invertible.
Example:
Let $A = \begin{bmatrix} 2 & 3 \\ 1 & 2 \end{bmatrix}$ and $B = \begin{bmatrix} 2 & -3 \\ -1 & 2 \end{bmatrix}$

Then $AB = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = I$ and $BA = I$

Therefore, $B = A^{-1}$ and $A = B^{-1}$

Important Theorems

Theorem 1 (Uniqueness): The inverse of a matrix, if it exists, is unique.

Theorem 2: If A and B are invertible matrices of the same order, then:
$(AB)^{-1} = B^{-1}A^{-1}$
(Note the order reversal!)
Important Notes:
• Only square matrices can have inverses
• Not all square matrices have inverses
• If B is the inverse of A, then A is also the inverse of B

Solved Examples (Board Pattern)

Example 1: Construct a $3 \times 2$ matrix whose elements are given by $a_{ij} = \frac{1}{2}|i - 3j|$

Solution:

A $3 \times 2$ matrix has the form: $A = \begin{bmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \\ a_{31} & a_{32} \end{bmatrix}$


Given: $a_{ij} = \frac{1}{2}|i - 3j|$


$a_{11} = \frac{1}{2}|1 - 3(1)| = \frac{1}{2}|-2| = 1$

$a_{12} = \frac{1}{2}|1 - 3(2)| = \frac{1}{2}|-5| = \frac{5}{2}$

$a_{21} = \frac{1}{2}|2 - 3(1)| = \frac{1}{2}|-1| = \frac{1}{2}$

$a_{22} = \frac{1}{2}|2 - 3(2)| = \frac{1}{2}|-4| = 2$

$a_{31} = \frac{1}{2}|3 - 3(1)| = \frac{1}{2}|0| = 0$

$a_{32} = \frac{1}{2}|3 - 3(2)| = \frac{1}{2}|-3| = \frac{3}{2}$


Answer: $A = \begin{bmatrix} 1 & 5/2 \\ 1/2 & 2 \\ 0 & 3/2 \end{bmatrix}$

Example 2: If $A = \begin{bmatrix} 1 & 2 & 3 \\ 2 & 3 & 1 \end{bmatrix}$ and $B = \begin{bmatrix} 3 & -1 & 3 \\ -1 & 0 & 2 \end{bmatrix}$, find $2A - B$

Solution:

$2A - B = 2\begin{bmatrix} 1 & 2 & 3 \\ 2 & 3 & 1 \end{bmatrix} - \begin{bmatrix} 3 & -1 & 3 \\ -1 & 0 & 2 \end{bmatrix}$


$= \begin{bmatrix} 2 & 4 & 6 \\ 4 & 6 & 2 \end{bmatrix} - \begin{bmatrix} 3 & -1 & 3 \\ -1 & 0 & 2 \end{bmatrix}$


$= \begin{bmatrix} 2-3 & 4-(-1) & 6-3 \\ 4-(-1) & 6-0 & 2-2 \end{bmatrix}$


Answer: $= \begin{bmatrix} -1 & 5 & 3 \\ 5 & 6 & 0 \end{bmatrix}$

Example 3: If $A = \begin{bmatrix} 3 & -4 \\ 1 & -1 \end{bmatrix}$, show that $A^2 - 2A + I = O$

Solution:

First, find $A^2$:

$A^2 = \begin{bmatrix} 3 & -4 \\ 1 & -1 \end{bmatrix}\begin{bmatrix} 3 & -4 \\ 1 & -1 \end{bmatrix} = \begin{bmatrix} 5 & -8 \\ 2 & -3 \end{bmatrix}$


Now, $A^2 - 2A + I$:

$= \begin{bmatrix} 5 & -8 \\ 2 & -3 \end{bmatrix} - 2\begin{bmatrix} 3 & -4 \\ 1 & -1 \end{bmatrix} + \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}$


$= \begin{bmatrix} 5 & -8 \\ 2 & -3 \end{bmatrix} - \begin{bmatrix} 6 & -8 \\ 2 & -2 \end{bmatrix} + \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}$


$= \begin{bmatrix} 5-6+1 & -8+8+0 \\ 2-2+0 & -3+2+1 \end{bmatrix} = \begin{bmatrix} 0 & 0 \\ 0 & 0 \end{bmatrix} = O$


Hence proved.

Example 4: Express $B = \begin{bmatrix} 2 & -2 & -4 \\ -1 & 3 & 4 \\ 1 & -2 & -3 \end{bmatrix}$ as the sum of a symmetric and a skew symmetric matrix

Solution:

We know: $B = \frac{1}{2}(B + B') + \frac{1}{2}(B - B')$


First, find $B'$: $B' = \begin{bmatrix} 2 & -1 & 1 \\ -2 & 3 & -2 \\ -4 & 4 & -3 \end{bmatrix}$


$P = \frac{1}{2}(B + B') = \frac{1}{2}\begin{bmatrix} 4 & -3 & -3 \\ -3 & 6 & 2 \\ -3 & 2 & -6 \end{bmatrix} = \begin{bmatrix} 2 & -3/2 & -3/2 \\ -3/2 & 3 & 1 \\ -3/2 & 1 & -3 \end{bmatrix}$


$Q = \frac{1}{2}(B - B') = \frac{1}{2}\begin{bmatrix} 0 & -1 & -5 \\ 1 & 0 & 6 \\ 5 & -6 & 0 \end{bmatrix} = \begin{bmatrix} 0 & -1/2 & -5/2 \\ 1/2 & 0 & 3 \\ 5/2 & -3 & 0 \end{bmatrix}$


Verify: $P' = P$ (symmetric) and $Q' = -Q$ (skew symmetric)

Therefore: $B = P + Q$ where P is symmetric and Q is skew symmetric

Formula Sheet (Quick Revision) 📋

Matrix Operations

1. Addition: $A + B = [a_{ij} + b_{ij}]$ (same order required)

2. Scalar Multiplication: $kA = [ka_{ij}]$

3. Matrix Multiplication: $(AB)_{ik} = \sum_{j=1}^{n} a_{ij}b_{jk}$

4. Transpose: If $A = [a_{ij}]_{m \times n}$, then $A' = [a_{ji}]_{n \times m}$

Properties

5. $A + B = B + A$ (Commutative)

6. $(A + B) + C = A + (B + C)$ (Associative)

7. $(AB)C = A(BC)$ (Associative)

8. $A(B + C) = AB + AC$ (Distributive)

9. $(A')' = A$

10. $(A + B)' = A' + B'$

11. $(AB)' = B'A'$ (Order reversed!)

12. $(kA)' = kA'$

Special Matrices

13. Symmetric: $A' = A$

14. Skew Symmetric: $A' = -A$ (diagonal elements are 0)

15. $A + A'$ is always symmetric

16. $A - A'$ is always skew symmetric

17. Any matrix: $A = \frac{1}{2}(A + A') + \frac{1}{2}(A - A')$

18. Inverse: $AA^{-1} = A^{-1}A = I$

19. $(AB)^{-1} = B^{-1}A^{-1}$

Practice Problems (Self-Test)

Level 1: Basic

Q1. If a matrix has 18 elements, what are the possible orders it can have?

Q2. Construct a $2 \times 2$ matrix $A = [a_{ij}]$ whose elements are given by $a_{ij} = \frac{(i+j)^2}{2}$

Q3. If $A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix}$ and $B = \begin{bmatrix} 2 & 3 \\ 4 & 5 \end{bmatrix}$, find $A + B$ and $A - B$

Level 2: Intermediate

Q4. If $A = \begin{bmatrix} 1 & -1 \\ 2 & 3 \end{bmatrix}$ and $B = \begin{bmatrix} 4 & 1 \\ 0 & 2 \end{bmatrix}$, find $AB$ and $BA$. Are they equal?

Q5. For the matrix $A = \begin{bmatrix} 1 & 5 \\ 6 & 7 \end{bmatrix}$, verify that $(A + A')$ is symmetric and $(A - A')$ is skew symmetric.

Q6. If $A = \begin{bmatrix} \cos\alpha & -\sin\alpha \\ \sin\alpha & \cos\alpha \end{bmatrix}$, verify that $A'A = I$

Level 3: Advanced

Q7. Express the matrix $A = \begin{bmatrix} 3 & 3 & -1 \\ -2 & -2 & 1 \\ -4 & -5 & 2 \end{bmatrix}$ as the sum of a symmetric and a skew symmetric matrix.

Q8. If $A$ and $B$ are symmetric matrices of the same order, show that $AB - BA$ is a skew symmetric matrix.

Q9. If $A = \begin{bmatrix} 3 & -1 \\ -1 & 2 \end{bmatrix}$, show that $A^2 - 5A + 7I = O$

Common Mistakes & Exam Tips

Common Mistakes 🚨

Mistake 1: Trying to add matrices of different orders. Remember: only matrices of same order can be added!
Mistake 2: Assuming $AB = BA$. Matrix multiplication is NOT commutative!
Mistake 3: Forgetting to check order compatibility for matrix multiplication. $A_{m \times n} \cdot B_{p \times q}$ is only defined if $n = p$.
Mistake 4: Writing $(AB)' = A'B'$. The correct formula is $(AB)' = B'A'$ (order reversed!)
Mistake 5: Assuming diagonal elements can be non-zero in skew symmetric matrices. They must all be zero!
Mistake 6: Not simplifying final answers. Always write matrices in simplest form.

Exam Tips 🏆

Tip 1: Always check order compatibility before multiplying matrices. Write down orders: $A_{m \times n} \times B_{n \times p} = C_{m \times p}$
Tip 2: For transpose questions, carefully interchange rows and columns. Double-check your work.
Tip 3: To express a matrix as sum of symmetric and skew symmetric: Always use the formula $A = \frac{1}{2}(A + A') + \frac{1}{2}(A - A')$
Tip 4: Show all steps in matrix multiplication. Write out at least one complete calculation to show method.
Tip 5: For "show that" or "prove" questions, always write "LHS" and "RHS" and show they are equal.
Tip 6: In symmetric/skew symmetric verification, explicitly write $A' = A$ or $A' = -A$ at the end.

Important NCERT Results & Theorems

Result 1: For any square matrix A with real entries:
• $A + A'$ is a symmetric matrix
• $A - A'$ is a skew symmetric matrix
Result 2: Any square matrix can be expressed as the sum of a symmetric and a skew symmetric matrix.
$A = \frac{1}{2}(A + A') + \frac{1}{2}(A - A')$
Result 3: If A and B are symmetric matrices of the same order, then:
• $A + B$ is symmetric
• $AB$ is symmetric if and only if $AB = BA$
Result 4: If A and B are symmetric matrices, then $AB - BA$ is a skew symmetric matrix.
Result 5: The inverse of a square matrix, if it exists, is unique.
Result 6: If A and B are invertible matrices of the same order, then:
$(AB)^{-1} = B^{-1}A^{-1}$

Quick Reference Table

Matrix Type Condition Example
Row Matrix $1 \times n$ order $[1\ 2\ 3]$
Column Matrix $m \times 1$ order $\begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix}$
Square Matrix $m = n$ $2 \times 2, 3 \times 3$, etc.
Diagonal Matrix $a_{ij} = 0$ for $i \neq j$ $\begin{bmatrix} 1 & 0 \\ 0 & 2 \end{bmatrix}$
Scalar Matrix Diagonal with all equal $\begin{bmatrix} 3 & 0 \\ 0 & 3 \end{bmatrix}$
Identity Matrix Diagonal elements = 1 $\begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}$
Zero Matrix All elements = 0 $\begin{bmatrix} 0 & 0 \\ 0 & 0 \end{bmatrix}$
Symmetric $A' = A$ $\begin{bmatrix} 1 & 2 \\ 2 & 3 \end{bmatrix}$
Skew Symmetric $A' = -A$ $\begin{bmatrix} 0 & 2 \\ -2 & 0 \end{bmatrix}$

Summary

• A matrix is an ordered rectangular array of numbers or functions


• A matrix having $m$ rows and $n$ columns is of order $m \times n$


• $A = [a_{ij}]_{m \times n}$ is a column matrix if $n = 1$, row matrix if $m = 1$


• A square matrix has $m = n$


• $A = [a_{ij}]_{n \times n}$ is diagonal if $a_{ij} = 0$ when $i \neq j$


• $A = [a_{ij}]_{n \times n}$ is scalar if $a_{ij} = 0$ when $i \neq j$ and $a_{ij} = k$ when $i = j$


• Identity matrix has $a_{ij} = 1$ when $i = j$ and $a_{ij} = 0$ when $i \neq j$


• Zero matrix has all elements equal to zero


• Matrices are equal if they have same order and corresponding elements are equal


• Matrix addition: $A + B = [a_{ij} + b_{ij}]$ (same order required)


• Scalar multiplication: $kA = [ka_{ij}]$


• Matrix multiplication $AB$ is defined only when columns of $A$ = rows of $B$


• Properties: $(AB)C = A(BC)$, $A(B+C) = AB + AC$, $(A+B)C = AC + BC$


• Transpose: If $A = [a_{ij}]_{m \times n}$, then $A' = [a_{ji}]_{n \times m}$


• $(A')' = A$, $(kA)' = kA'$, $(A+B)' = A' + B'$, $(AB)' = B'A'$


• Symmetric matrix: $A' = A$


• Skew symmetric matrix: $A' = -A$ (all diagonal elements are 0)


• Any square matrix can be expressed as sum of symmetric and skew symmetric matrix


• If $AB = BA = I$, then $B = A^{-1}$ and $A = B^{-1}$


• Inverse of a matrix, if it exists, is unique


• $(AB)^{-1} = B^{-1}A^{-1}$

All Chapters