Chapter 3: Matrices
Complete Board Exam Focused Notes with Solved Examples
Introduction to Matrices
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)
• 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
General Form:
An $m \times n$ matrix has the form:
Or simply: $A = [a_{ij}]_{m \times n}$, where $1 \leq i \leq m$ and $1 \leq j \leq n$
• 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 = $mn$
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
Example: $A = \begin{bmatrix} 0 \\ 3 \\ -1 \\ 1/2 \end{bmatrix}$ is a $4 \times 1$ column matrix
2. Row Matrix
Example: $B = \begin{bmatrix} 1/2 & 5 & -2 & 3 \end{bmatrix}$ is a $1 \times 4$ row matrix
3. 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
In the above matrix, diagonal elements are: 3, 2, 1
4. Diagonal Matrix
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
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
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)
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
(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 $j$
But $\begin{bmatrix} 3 & 2 \\ 0 & 1 \end{bmatrix}$ and $\begin{bmatrix} 2 & 3 \\ 0 & 1 \end{bmatrix}$ are NOT equal
Then: $x = 1.5$, $y = 2$, $z = 6$, $a = 3$, $b = 2$, $c = 0$
Operations on Matrices 🔥🔥🔥
1. Addition of Matrices
If $A = [a_{ij}]$ and $B = [b_{ij}]$ are both $m \times n$ matrices, then:
$A + B = [a_{ij} + b_{ij}]_{m \times n}$
$A = \begin{bmatrix} 3 & -1 & 1 \\ 2 & 3 & 0 \end{bmatrix}$ 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
$kA = k[a_{ij}]_{m \times n} = [ka_{ij}]_{m \times n}$
Each element of A is multiplied by the scalar $k$.
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
Each element of A is multiplied by -1.
4. Subtraction of Matrices
Subtraction is the addition of the negative matrix.
Properties of Matrix Addition
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
1. $k(A + B) = kA + kB$
2. $(k + l)A = kA + lA$
Multiplication of Matrices 🎯🎯🎯
If $A$ is $m \times n$ and $B$ is $n \times p$, then $AB$ is $m \times p$.
If $A = [a_{ij}]_{m \times n}$ and $B = [b_{jk}]_{n \times p}$, then $C = AB = [c_{ik}]_{m \times p}$
where $c_{ik} = \sum_{j=1}^{n} a_{ij}b_{jk} = a_{i1}b_{1k} + a_{i2}b_{2k} + ... + a_{in}b_{nk}$
$A = \begin{bmatrix} 6 & 9 \\ 2 & 3 \end{bmatrix}$ and $B = \begin{bmatrix} 2 & 6 & 0 \\ 7 & 9 & 8 \end{bmatrix}$
$AB = \begin{bmatrix} 6(2)+9(7) & 6(6)+9(9) & 6(0)+9(8) \\ 2(2)+3(7) & 2(6)+3(9) & 2(0)+3(8) \end{bmatrix}$
$= \begin{bmatrix} 75 & 117 & 72 \\ 25 & 39 & 24 \end{bmatrix}$
• 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
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
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 $n$)
• 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 ⭐⭐⭐⭐⭐
Transpose of A is denoted by $A'$ or $A^T$
If $A = [a_{ij}]_{m \times n}$, then $A' = [a_{ji}]_{n \times m}$
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
1. $(A')' = A$
2. $(kA)' = kA'$ (where $k$ is any constant)
3. $(A + B)' = A' + B'$
4. $(AB)' = B'A'$ (Note the order reversal!)
$(AB)' = B'A'$, NOT $A'B'$
Symmetric and Skew Symmetric Matrices 🔥🔥🔥
Symmetric Matrix
That is, $a_{ij} = a_{ji}$ for all $i$ and $j$
$A = \begin{bmatrix} 3 & 2 & -3 \\ 2 & -1.5 & 1 \\ -3 & 1 & 1 \end{bmatrix}$ is symmetric because $A' = A$
Skew Symmetric Matrix
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 $a_{ii} = 0$)
$B = \begin{bmatrix} 0 & e & -f \\ -e & 0 & g \\ f & -g & 0 \end{bmatrix}$ is skew symmetric because $B' = -B$
Important Theorems
• $A + A'$ 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:
$A = \frac{1}{2}(A + A') + \frac{1}{2}(A - A')$
• Symmetric: Elements are mirror images across diagonal
• Skew Symmetric: Elements are negative mirror images across diagonal, and diagonal is all zeros
Invertible Matrices
$AB = BA = I$
then B is called the inverse matrix of A and is denoted by $A^{-1}$
In that case, A is said to be invertible.
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 2: If A and B are invertible matrices of the same order, then:
$(AB)^{-1} = B^{-1}A^{-1}$
(Note the order reversal!)
• 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 🚨
Exam Tips 🏆
Important NCERT Results & Theorems
• $A + A'$ is a symmetric matrix
• $A - A'$ is a skew symmetric matrix
$A = \frac{1}{2}(A + A') + \frac{1}{2}(A - A')$
• $A + B$ is symmetric
• $AB$ is symmetric if and only if $AB = BA$
$(AB)^{-1} = B^{-1}A^{-1}$
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
• $(AB)^{-1} = B^{-1}A^{-1}$