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Chapter 4  ·  Class XII Mathematics

The Number That Encodes a Matrix

Determinants

One Scalar, Infinite Power — Areas, Inverses and Systems of Equations

Chapter Snapshot

10Concepts
13Formulae
6–8%Exam Weight
2–4Avg Q's
HighDifficulty

Why This Chapter Matters for Entrance Exams

CBSEJEE MainJEE Advanced

CBSE Boards reliably include a 5-mark "solve the system using matrix method" question built on this chapter. JEE Main and Advanced test determinant properties (row/column operations that simplify a determinant to zero) as a recurring, high-yield question type.

Key Concept Highlights

Determinant of Order 1, 2 and 3
Properties of Determinants
Area of a Triangle Using Determinants
Collinearity Condition via Determinants
Minors
Cofactors
Adjoint of a Matrix
Inverse of a Matrix via Adjoint
Singular and Non-Singular Matrices
Solving a System of Linear Equations (Matrix Method / Cramer's Rule)

Important Formula Capsules

$\text{Area} = \dfrac{1}{2}\begin{vmatrix}x_1 & y_1 & 1\\ x_2 & y_2 & 1\\ x_3 & y_3 & 1\end{vmatrix}$
$\mathrm{adj}(A) = [\text{Cofactor matrix of }A]^T$
$A^{-1} = \dfrac{1}{|A|}\,\mathrm{adj}(A),\ |A| \ne 0$
$A(\mathrm{adj}\,A) = (\mathrm{adj}\,A)A = |A|\,I$
$|kA| = k^n|A|\ \text{(n = order of A)}$
$\text{System consistent \& unique solution} \iff |A| \ne 0$
$x = \dfrac{D_x}{D},\ y=\dfrac{D_y}{D},\ z=\dfrac{D_z}{D}\ \text{(Cramer's Rule)}$

What You Will Learn

Navigate to Chapter Resources

🏆 Exam Strategy & Preparation Tips

Learn the standard row-operation tricks (R1→R1−R2, taking common factors) — they turn intimidating determinants into near-zero rows and are JEE Advanced's favourite pattern. For Boards, drill the matrix-method system-solving format exactly as NCERT presents it. Time investment: 3 days.

Chapter 4 · CBSE · Class XII

Introduction to Determinants

Determinants NCERT Class 12 Class 12 Mathematics Mathematics Notes CBSE Class 12 NCERT Notes Determinant of Matrix Determinants of Order 1, 2 and 3 Minors and Cofactors Expansion of Determinants Properties of Determinants Area of Triangle Adjoint of Matrix Inverse of Matrix Singular Matrix Non Singular Matrix System of Linear Equations Matrix Inverse Method Determinant Theorems Determinant Formulae Solved Examples Board Exam JEE Main JEE Advanced CUET Competitive Exams
📖 Introduction
📘 Definition
🗒️ Matrix and Its Determinant: Important Distinction
It is essential to distinguish between a matrix and its determinant.
  • A matrix is an ordered rectangular arrangement of elements.
  • A determinant is a scalar value associated with a square matrix.
  • A matrix can be rectangular, but a determinant exists only for a square matrix.
  • The notation \(|A|\) can represent the determinant of \(A\), depending on context.
  • The determinant of a matrix is not another matrix. It is a single number.
📘 Determinant of a Matrix of Order 2
💡 Concept Behind the Formula
📘 Order of a Determinant
🗒️ Elements Of A Determinant
Consider
\[ A=[a_{ij}]_{n\times n}. \]
The symbol \(a_{ij}\) represents the element located at the intersection of the \(i\)-th row and \(j\)-th column.
  • \(i\) identifies the row.
  • \(j\) identifies the column.
  • \(a_{ij}\) is therefore the \((i,j)\)-th element.
For example, in
\[ A= \begin{bmatrix} 2 & 5 & 7\\ 1 & 4 & 8\\ 3 & 6 & 9 \end{bmatrix}, \]
we have
\[ a_{11}=2,\qquad a_{12}=5,\qquad a_{23}=8,\qquad a_{31}=3. \]
📘 Principal Diagonal
📌 Determinant as a Function
💡 Key Concept: Determinant and Invertibility
🗒️ Important Terminology
Square Matrix
A matrix having the same number of rows and columns.
Determinant
A scalar uniquely associated with a square matrix according to determinant rules.
Singular Matrix
A square matrix \(A\) for which \(|A|=0\).
Non-singular Matrix
A square matrix \(A\) for which \(|A|\neq0\).
Order of a Determinant
The number of rows or columns in its square array.
Element \(a_{ij}\)
The element in the \(i\)-th row and \(j\)-th column.
🔢 Formula
✏️ Example
1
Question
Find the determinant of
\[ A= \begin{bmatrix} 5 & 2\ 3 & 4 \end{bmatrix}. \]
For a \(2\times2\) matrix, use
\[ \begin{vmatrix} a & b\ c & d \end{vmatrix} =ad-bc. \]
  1. 1
    Identify \(a,b,c,d\).
  2. 2
    Calculate \(ad\).
  3. 3
    Calculate \(bc\).
  4. 4
    Subtract \(bc\) from \(ad\).
  1. Here,
    \[ a=5,\quad b=2,\quad c=3,\quad d=4. \]
  2. Therefore,
    \[ |A|=ad-bc \]
    \[ =5(4)-2(3) \]
    \[ =20-6=14. \]
  3. Hence,
    \[ \boxed{|A|=14} \]
2
Question
Determine whether
\[ A= \begin{bmatrix} 2 & 6\\ 1 & 3 \end{bmatrix} \]
is singular or non-singular.
A square matrix is singular if and only if its determinant is zero.
  1. 1
    Evaluate \(|A|\)
  2. 2
    Compare the result with zero.
  3. 3
    If \(|A|=0\), conclude that \(A\) is singular.
Given:
\[ |A|= \begin{vmatrix} 2 & 6\\ 1 & 3 \end{vmatrix} \]
  1. \[\begin{aligned} |A|&= \begin{vmatrix} 2 & 6\\ 1 & 3 \end{vmatrix} \\ &=2(3)-6(1)\\ &=6-6=0 \end{aligned}\]
  2. Therefore,
    \[ \boxed{A\text{ is singular}} \]
  3. Consequently,
    \(A^{-1}\) does not exist.
3
Question
Find the value of \(x\) for which the matrix
\[ A= \begin{bmatrix} x & 2\\ 3 & 6 \end{bmatrix} \]
is singular.
For a singular matrix,
\[ |A|=0. \]
  1. 1
    Set the determinant equal to zero.
  2. 2
    Evaluate the determinant using \(ad-bc\).
  3. 3
    Solve the resulting equation.
Given:
\[ \begin{vmatrix} x & 2\\ 3 & 6 \end{vmatrix}=0 \]
  1. \[\begin{aligned} \begin{vmatrix} x & 2\\ 3 & 6 \end{vmatrix}&=0\\ 6x-6&=0\\ x&=1 \end{aligned}\]
4
Question
For what values of \(k\) does the matrix
\[ A= \begin{bmatrix} k & 1\\ 4 & k \end{bmatrix} \]
have an inverse?
A square matrix has an inverse if and only if its determinant is non-zero.
Given:
\[ A= \begin{bmatrix} k & 1\\ 4 & k \end{bmatrix} \]
  1. \[ |A|=k^2-4. \]
  2. For an inverse to exist,
    \[ k^2-4\neq0. \]
  3. Thus,
    \[ k^2\neq4 \]
  4. and hence
    \[ \boxed{k\neq\pm2} \]
📐 Derivation of the \(2\times2\) Determinant Formula

For a matrix

\[ A= \begin{bmatrix} a & b\\ c & d \end{bmatrix}, \]

the determinant is defined through the two possible products formed by selecting one element from each row and each column.

The product obtained from the principal diagonal is

\[ ad\]

The product obtained from the other diagonal is

\[ bc \]

The determinant assigns opposite signs to these two products. Therefore,

\[ \boxed{\det(A)=ad-bc} \]

This formula is the fundamental computational rule for second-order determinants and also serves as the basis for several results involving \(2\times2\) matrices.

🌟 Why Determinants Matter
🗒️ CBSE Exam Significance
  • define a determinant correctly;
  • distinguish a determinant from a matrix;
  • evaluate determinants of orders \(2\) and \(3\);
  • apply determinant properties efficiently;
  • calculate minors and cofactors;
  • use determinants to find the inverse of a matrix;
  • solve linear equations using determinants;
  • apply determinants to coordinate geometry problems.
Competitive Examination Significance

For JEE and other entrance examinations, determinants are often tested through algebraic manipulation rather than direct evaluation. A strong understanding of determinant properties can significantly reduce computation time.

Typical competitive-examination patterns include:

  • finding unknown parameters for which a determinant vanishes;
  • identifying singular and non-singular matrices;
  • using determinant properties to simplify large expressions;
  • evaluating determinants containing variables;
  • connecting determinant values with inverse matrices;
  • using determinants in systems of equations;
  • recognising symmetry, repeated rows or proportional rows.
⚡ Exam Tip
❌ Common Mistakes
  • Mistake 1: Trying to find the determinant of a rectangular matrix.
  • Mistake 2: Writing \(ad+bc\) instead of \(ad-bc\).
  • Mistake 3: Treating the determinant as a matrix rather than a scalar.
  • Mistake 4: Assuming that \(|A|=0\) means \(A^{-1}\) exists. The correct result is the opposite: \(A^{-1}\) does not exist.
  • Mistake 5: Using the \(2\times2\) diagonal-product shortcut directly for a \(3\times3\) determinant.
  • Mistake 6: Losing the negative sign in \(ad-bc\) during calculation.
📋 CBSE Case Study and HOTS

A school laboratory represents a linear transformation using the matrix

\[ A=\begin{bmatrix} 3 & 2\\ 6 & k \end{bmatrix}. \]

The teacher wants to determine the value of \(k\) for which the transformation becomes non-invertible.

Question 1

What condition must be satisfied for \(A\) to be non-invertible?

Answer:

\[ |A|=0. \]

Question 2

Find the value of \(k\).

Solution:

\[ \begin{vmatrix} 3 & 2\\ 6 & k \end{vmatrix}=0 \]

\[ 3k-12=0 \]

\[ 3k=12 \]

\[ \boxed{k=4} \]

HOTS Interpretation

At \(k=4\),

\[ A= \begin{bmatrix} 3 & 2\\ 6 & 4 \end{bmatrix}. \]

The second row is twice the first row. Hence the rows are linearly dependent and

\[ |A|=0. \]

This provides a structural explanation for the determinant being zero, not merely a computational one.

⚡ Quick Revision
  • Only square matrices have determinants.
  • The determinant of \(A\) is denoted by \(|A|\), \(\det A\), or \(\det(A)\).
  • For \(A=[a_{ij}]_{n\times n}\), \(a_{ij}\) is the \((i,j)\)-th element.
  • For \(A=\begin{bmatrix}a&b\\c&d\end{bmatrix}\), \(\det(A)=ad-bc\).
  • \(|A|=0\) means \(A\) is singular.
  • \(|A|\neq0\) means \(A\) is non-singular.
  • \(A^{-1}\) exists if and only if \(|A|\neq0\).
  • The absolute value of a \(2\times2\) determinant gives the area of the parallelogram formed by the corresponding vectors.
  • The determinant is a scalar, not a matrix.

Order of Determinant

🗂️ Determinant of a Matrix of Order 1
  • Determinant of a Matrix of Order 1
    Let \(A=[a]\) be a square matrix of order \(1\). Since it contains only one element, its determinant is defined to be equal to that element.

    \[A=[a]\]
    Then,
    \[\boxed{|A|=\det(A)=a}\]
    Thus, a determinant of order \(1\) is simply the single element of the matrix.
    Example
    Evaluate the determinant
    \[\begin{vmatrix}7\end{vmatrix}\]
    Solution
    Since the determinant is of order \(1\), its value is the only element:
    \[\begin{vmatrix}7\end{vmatrix}=7\]
    Key Point
    The order \(1\) determinant provides the simplest starting point for the recursive definition of higher-order determinants.
  • Determinant of a Matrix of Order 2
    Let \(A\) be a square matrix of order \(2\), given by
    \[A=\begin{bmatrix}a_{11} & a_{12}\\a_{21} & a_{22}\end{bmatrix}\]
    The determinant of \(A\) is denoted by \(|A|\), \(\det(A)\), \(\det A\), or \(\Delta\), and is defined as
    \[\begin{aligned}\det(A)&=|A|\\&=\Delta\\&=\begin{vmatrix}a_{11} & a_{12}\\a_{21} & a_{22}\end{vmatrix}\\&=a_{11}a_{22}-a_{12}a_{21}\end{aligned}\]
    Therefore, the fundamental formula for a determinant of order \(2\) is
    \[\begin{vmatrix}a_{11} & a_{12}\\a_{21} & a_{22}\end{vmatrix}=a_{11}a_{22}-a_{12}a_{21}\]
    How to Remember the \(2\times2\) Formula
    For
    \[\begin{vmatrix}a & b\\c & d\end{vmatrix},\]
    multiply the elements of the principal diagonal and subtract the product of the elements of the other diagonal: [\boxed{ad-bc}\] In other words,
    \[\text{Determinant}=\text{product of main diagonal}-\text{product of other diagonal}\]
    Sign Pattern for a \(2\times2\) Determinant
    The signs associated with the two terms are
    \[\begin{array}{cc}+ & -\\- & +\end{array}\]
    However, for direct evaluation of a \(2\times2\) determinant, the easiest formula to remember is simply \(ad-bc\).
    Example 1: Direct Evaluation of a \(2\times2\) Determinant
    Question: Evaluate
    \[\begin{vmatrix}2 & 4\\-1 & 2\end{vmatrix}\]
    Concept: Use the formula
    \[\begin{vmatrix}a & b\\c & d\end{vmatrix}=ad-bc\]
    Solution Roadmap:
    1. Identify the four elements.
    2. Multiply the principal diagonal elements.
    3. Multiply the other diagonal elements.
    4. Subtract the second product from the first.
    Solution:
    \[\begin{aligned}\begin{vmatrix}2 & 4\\-1 & 2\end{vmatrix}&=2(2)-4(-1)\\&=4+4\\&=8\end{aligned}\]
    Hence,
    \[\boxed{8}\]
    Example 2: Determinant Containing a Variable
    Question: Evaluate
    \[\begin{vmatrix}x & x+1\\x-1 & x\end{vmatrix}\]
    Concept: Apply the \(2\times2\) determinant formula and simplify the resulting algebraic expression. Solution:
    \[\begin{aligned}\begin{vmatrix}x & x+1\\x-1 & x\end{vmatrix}&=x(x)-(x+1)(x-1)\\&=x^2-(x^2-1)\\&=x^2-x^2+1\\&=1\end{aligned}\]
    Therefore,
    \[\begin{vmatrix}x & x+1\\x-1 & x\end{vmatrix}=1\]
  • Determinant of a Matrix of Order \(3\)
    Definition and Concept
    Consider the square matrix of order \(3\):
    \[A=\begin{bmatrix}a_{11} & a_{12} & a_{13}\\a_{21} & a_{22} & a_{23}\\a_{31} & a_{32} & a_{33}\end{bmatrix}\]
    Its determinant is
    \[|A|=\begin{vmatrix}a_{11} & a_{12} & a_{13}\\a_{21} & a_{22} & a_{23}\\a_{31} & a_{32} & a_{33}\end{vmatrix}\]
    A determinant of order \(3\) can be evaluated by expressing it in terms of determinants of order \(2\). This process is called expansion of a determinant or cofactor expansion. A determinant of order \(3\) can be expanded along any row or any column. Therefore, there are six possible expansion choices:
    • Expansion along \(R_1\)
    • Expansion along \(R_2\)
    • Expansion along \(R_3\)
    • Expansion along \(C_1\)
    • Expansion along \(C_2\)
    • Expansion along \(C_3\)
    All six expansions produce the same determinant value, provided the signs and corresponding minors are used correctly.
📌 Minor of an Element
📎 Cofactor of an Element
The cofactor of the element \(a_{ij}\) is denoted by \(A_{ij}\) and is defined by
\[\boxed{A_{ij}=(-1)^{i+j}M_{ij}}\]
Thus, the cofactor is obtained by multiplying the corresponding minor by the appropriate sign factor.
Cofactor Sign Pattern
The signs of the cofactors in a \(3\times3\) determinant follow the checkerboard pattern:
\[\begin{bmatrix}+ & - & +\\- & + & -\\+ & - & +\end{bmatrix}\]
This sign pattern is extremely important when expanding determinants. Memory rule: Start with \(+\) in the top-left corner and alternate signs horizontally and vertically.
Expansion of a Determinant Along the First Row
Consider
\[|A|=\begin{vmatrix}a_{11} & a_{12} & a_{13}\\a_{21} & a_{22} & a_{23}\\a_{31} & a_{32} & a_{33}\end{vmatrix}\]
When expanding along the first row, each element of \(R_1\) is multiplied by its corresponding cofactor.

Therefore,
\[|A|=a_{11}A_{11}+a_{12}A_{12}+a_{13}A_{13}\]
Since
\[A_{11}=M_{11},\]
\[A_{12}=-M_{12},\]
and
\[A_{13}=M_{13},\]
we obtain
\[\begin{aligned}|A|&=a_{11}\begin{vmatrix}a_{22} & a_{23}\\a_{32} & a_{33}\end{vmatrix}- a_{12}\begin{vmatrix}a_{21} & a_{23}\\a_{31} & a_{33}\end{vmatrix}\\&\quad+ a_{13}\begin{vmatrix}a_{21} & a_{22}\\a_{31} & a_{32}\end{vmatrix}\end{aligned}\]
Fully Expanded Formula
Using the \(2\times2\) determinant formula for each minor,
\[\begin{aligned}|A|&=a_{11}(a_{22}a_{33}-a_{23}a_{32})\\&\quad-a_{12}(a_{21}a_{33}-a_{23}a_{31})\\&\quad+a_{13}(a_{21}a_{32}-a_{22}a_{31})\end{aligned}\]
Hence, the standard expansion along the first row is
\[\begin{aligned}|A|&=a_{11}(a_{22}a_{33}-a_{23}a_{32})-a_{12}(a_{21}a_{33}-a_{23}a_{31})\\&\quad+a_{13}(a_{21}a_{32}-a_{22}a_{31})\end{aligned}\]
Expansion Along the Second Row
For
\[|A|=\begin{vmatrix}a_{11} & a_{12} & a_{13}\\a_{21} & a_{22} & a_{23}\\a_{31} & a_{32} & a_{33}\end{vmatrix},\]
expansion along \(R_2\) gives
\[|A|=a_{21}A_{21}+a_{22}A_{22}+a_{23}A_{23}\]
The signs along the second row are
\[-,\quad +,\quad -\]
Hence,
\[\begin{aligned}|A|&=-a_{21}\begin{vmatrix}a_{12} & a_{13}\\a_{32} & a_{33}\end{vmatrix}+ a_{22}\begin{vmatrix}a_{11} & a_{13}\\a_{31} & a_{33}\end{vmatrix}\\&\quad- a_{23}\begin{vmatrix}a_{11} & a_{12}\\a_{31} & a_{32}\end{vmatrix}\end{aligned}\]
Using \(2\times2\) determinant evaluation,
\[\begin{aligned}|A|&=-a_{21}(a_{12}a_{33}-a_{13}a_{32})\\&\quad+a_{22}(a_{11}a_{33}-a_{13}a_{31})\\&\quad-a_{23}(a_{11}a_{32}-a_{12}a_{31})\end{aligned}\]
Expansion Along the Third Row
Expansion along \(R_3\) gives
\[|A|=a_{31}A_{31}+a_{32}A_{32}+a_{33}A_{33}\]
The signs in the third row are
\[+,\quad -,\quad +\]
Therefore,
\[\begin{aligned}|A|&=a_{31}\begin{vmatrix}a_{12} & a_{13}\\a_{22} & a_{23}\end{vmatrix}- a_{32}\begin{vmatrix}a_{11} & a_{13}\\a_{21} & a_{23}\end{vmatrix}\\&\quad+ a_{33}\begin{vmatrix}a_{11} & a_{12}\\a_{21} & a_{22}\end{vmatrix}\end{aligned}\]
Expansion Along the First Column
Expansion along \(C_1\) gives
\[|A|=a_{11}A_{11}+a_{21}A_{21}+a_{31}A_{31}\]
The signs along the first column are
\[+,\quad -,\quad +\]
Hence,
\[\begin{aligned}|A|&=a_{11}\begin{vmatrix}a_{22} & a_{23}\\a_{32} & a_{33}\end{vmatrix}- a_{21}\begin{vmatrix}a_{12} & a_{13}\\a_{32} & a_{33}\end{vmatrix}\\&\quad+ a_{31}\begin{vmatrix}a_{12} & a_{13}\\a_{22} & a_{23}\end{vmatrix}\end{aligned}\]
Expansion Along the Second Column
Expansion along \(C_2\) gives
\[|A|=a_{12}A_{12}+a_{22}A_{22}+a_{32}A_{32}\]
The signs in the second column are
\[-,\quad +,\quad -\]
Therefore,
\[\begin{aligned}|A|&=-a_{12}\begin{vmatrix}a_{21} & a_{23}\\a_{31} & a_{33}\end{vmatrix}+ a_{22}\begin{vmatrix}a_{11} & a_{13}\\a_{31} & a_{33}\end{vmatrix}\\&\quad- a_{32}\begin{vmatrix}a_{11} & a_{13}\\a_{21} & a_{23}\end{vmatrix}\end{aligned}\]
Expansion Along the Third Column
Expansion along \(C_3\) gives
\[|A|=a_{13}A_{13}+a_{23}A_{23}+a_{33}A_{33}\]
The signs in the third column are
\[+,\quad -,\quad +\]
Hence,
\[\begin{aligned}|A|&=a_{13}\begin{vmatrix}a_{21} & a_{22}\\a_{31} & a_{32}\end{vmatrix}- a_{23}\begin{vmatrix}a_{11} & a_{12}\\a_{31} & a_{32}\end{vmatrix}\\&\quad+ a_{33}\begin{vmatrix}a_{11} & a_{12}\\a_{21} & a_{22}\end{vmatrix}\end{aligned}\]
Six Equivalent Expansions of a \(3\times3\) Determinant

For a determinant of order \(3\), any one of its three rows or three columns may be selected for expansion.

Expansion General Form Sign Pattern
First row \(R_1\) \(a_{11}A_{11}+a_{12}A_{12}+a_{13}A_{13}\) \(+,-,+\)
Second row \(R_2\) \(a_{21}A_{21}+a_{22}A_{22}+a_{23}A_{23}\) \(-,+,-\)
Third row \(R_3\) \(a_{31}A_{31}+a_{32}A_{32}+a_{33}A_{33}\) \(+,-,+\)
First column \(C_1\) \(a_{11}A_{11}+a_{21}A_{21}+a_{31}A_{31}\) \(+,-,+\)
Second column \(C_2\) \(a_{12}A_{12}+a_{22}A_{22}+a_{32}A_{32}\) \(-,+,-\)
Third column \(C_3\) \(a_{13}A_{13}+a_{23}A_{23}+a_{33}A_{33}\) \(+,-,+\)
How to Choose the Best Row or Column for Expansion
Although every row and column gives the same determinant value, some choices make the calculation considerably shorter.

For examination problems, preferably choose a row or column containing:
  • the largest number of zeros;
  • simple numerical values;
  • common factors;
  • terms that simplify easily;
  • repeated or related expressions that allow quick factorisation.
If a row contains two zeros, the determinant can often be evaluated using only one cofactor. This is a major time-saving strategy in CBSE and competitive examinations.
✏️ Example
1
Question
Evaluate
\[D=\begin{vmatrix}2 & 0 & 3\\0 & 4 & 0\\5 & 0 & 1\end{vmatrix}\]
The second row contains two zeros. Therefore, expansion along \(R_2\) is the most efficient method.
  1. 1
    Select \(R_2\).
  2. 2
    Use the sign pattern \(-,+,-\).
  3. 3
    Only the middle term survives.
  4. 4
    Evaluate the remaining \(2\times2\) determinant.
\[\begin{aligned}D&=0+4\begin{vmatrix}2 & 3\\5 & 1\end{vmatrix}+0\\&=4(2-15)\\&=4(-13)\\&=-52\end{aligned}\]
Therefore,
\[\boxed{D=-52}\]
2
Question
Evaluate
\[D=\begin{vmatrix}1 & 2 & 3\\0 & 4 & 5\\1 & 0 & 6\end{vmatrix}\]
Expand along \(R_1\), whose sign pattern is \(+,-,+\):
\[\begin{aligned}D&=1\begin{vmatrix}4 & 5\\0 & 6\end{vmatrix}- 2\begin{vmatrix}0 & 5\\1 & 6\end{vmatrix}+ 3\begin{vmatrix}0 & 4\\1 & 0\end{vmatrix}\\&=1(24)-2(-5)+3(-4)\\&=24+10-12\\&=22\end{aligned}\]
Hence,
\[\boxed{D=22}\]
3
Question
Find the value of \(x\) if
\[\begin{vmatrix}x & 1 & 1\\1 & x & 1\\1 & 1 & x\end{vmatrix}=0\]
This is a parameter-based determinant equation. Evaluate the determinant and then solve the resulting algebraic equation.
Expanding along \(R_1\),
\[\begin{aligned}D&=x\begin{vmatrix}x & 1\\1 & x\end{vmatrix}- \begin{vmatrix}1 & 1\\1 & x\end{vmatrix}+\begin{vmatrix}1 & x\\1 & 1\end{vmatrix}\\&=x(x^2-1)-(x-1)+(1-x)\\&=x^3-x-x+1+1-x\\&=x^3-3x+2\end{aligned}\]
Thus
\[x^3-3x+2=0\]
Factorising,
\[x^3-3x+2=(x-1)^2(x+2)\]
Therefore,
\[\boxed{x=1,-2}\]
📐 Derivation of the Cofactor Expansion
For an element \(a_{ij}\), deleting its \(i\)-th row and \(j\)-th column produces its minor \(M_{ij}\). The corresponding cofactor is obtained by assigning the sign \((-1)^{i+j}\):
\[A_{ij}=(-1)^{i+j}M_{ij}\]
Consequently, if a determinant is expanded along the \(i\)-th row, its value is the sum of the products of the elements of that row and their corresponding cofactors:
\[|A|=\sum_{j=1}^{n}a_{ij}A_{ij}\]
Similarly, expansion along the \(j\)-th column gives
\[\boxed{|A|=\sum_{i=1}^{n}a_{ij}A_{ij}}\]
For a \(3\times3\) determinant, these general results reduce to the six row and column expansions described above.
🔗 Relations
Relationship Between Minor and Cofactor
Do not confuse a minor with a cofactor.

The relationship is
\[\boxed{A_{ij}=(-1)^{i+j}M_{ij}}\]
For example,
\[A_{12}=(-1)^{1+2}M_{12}=-M_{12}\]
Similarly,
\[A_{23}=(-1)^{2+3}M_{23}=-M_{23}\]
But
\[A_{22}=(-1)^{2+2}M_{22}=+M_{22}\]
⚡ Exam Tip
❌ Common Mistakes
  • Using the wrong minor after deleting a row and column.
  • Forgetting the negative sign associated with positions such as \(a_{12}\), \(a_{21}\), \(a_{23}\), and \(a_{32}\).
  • Writing the minor \(M_{ij}\) when the question requires the cofactor \(A_{ij}\).
  • Using the \(ad-bc\) shortcut incorrectly for a \(3\times3\) determinant.
  • Expanding along a complicated row when another row or column contains zeros.
  • Making an arithmetic error while simplifying the \(2\times2\) minors.
  • Assuming that the six possible expansions have different values. They all represent the same determinant.
🗒️ Signficance

For CBSE Class 12 Mathematics, this section forms the foundation for several later topics. Students should be comfortable with:

  • determinants of order \(1\), \(2\), and \(3\);
  • evaluation using direct formulae;
  • minor \(M_{ij}\);
  • cofactor \(A_{ij}\);
  • cofactor sign pattern;
  • expansion along any row or column;
  • choosing an efficient expansion;
  • parameter-based determinant equations.
Competitive Examination Focus

For JEE Main, JEE Advanced and other entrance examinations, determinant questions frequently reward pattern recognition and strategic expansion. Instead of mechanically expanding every term, first inspect the determinant for zeros, equal entries, proportional rows or columns, symmetry, and algebraic patterns.

A determinant that appears computationally lengthy may often collapse after selecting an appropriate row or column. Therefore, the ability to choose an efficient expansion is as important as knowing the expansion formula itself.

⚡ Quick Revision

Order 1:

\[\boxed{|a|=a}\]

Order 2:

\[\begin{vmatrix}a&b\\c&d\end{vmatrix}=ad-bc\]

Minor:

\[\boxed{M_{ij}=\text{determinant obtained by deleting row }i\text{ and column }j}\]

Cofactor:

\[\boxed{A_{ij}=(-1)^{i+j}M_{ij}}\]

Expansion along the \(i\)-th row:

\[\boxed{|A|=\sum_{j=1}^{n}a_{ij}A_{ij}}\]

Expansion along the \(j\)-th column:

\[\boxed{|A|=\sum_{i=1}^{n}a_{ij}A_{ij}}\]

Cofactor sign matrix for order 3:

\[\begin{bmatrix}+&-&+\\-&+&-\\+&-&+\end{bmatrix}\]

📝 Conceptual Summary

Example 1

❓ Question
Evaluate the determinant
\[A=\begin{vmatrix}1 & 2 & 4\\-1 & 3 & 0\\4 & 1 & 0\end{vmatrix}\]
💡 Concept
🗺️ Roadmap
  1. Identify a row or column containing the maximum number of zeros.

  2. Choose \(C_3\) for expansion.

  3. Use the cofactor sign corresponding to \(a_{13}\), which is positive.

  4. Delete the first row and third column to obtain the minor of \(a_{13}\).

  5. Evaluate the resulting \(2\times2\) determinant.

🗒️ Soution
Given:
\[A=\begin{vmatrix}1 & 2 & 4\\-1 & 3 & 0\\4 & 1 & 0\end{vmatrix}\]
  1. Expanding along the third column \(C_3\),
    \[\begin{aligned}|A|&=4\begin{vmatrix}-1 & 3\\4 & 1\end{vmatrix}-0\begin{vmatrix}1 & 2\\4 & 1\end{vmatrix}+0\begin{vmatrix}1 & 2\\-1 & 3\end{vmatrix}\end{aligned}\]
  2. Since the terms containing zero vanish,
    \[|A|=4\begin{vmatrix}-1 & 3\\4 & 1\end{vmatrix}\]
  3. Using the formula
    \[\begin{vmatrix}a&b\\c&d\end{vmatrix}=ad-bc,\]
  4. we get
    \[\begin{aligned}|A|&=4[(-1)(1)-(3)(4)]\\&=4[-1-12]\\&=4(-13)\\&=-52\end{aligned}\]
  5. Therefore,
    \[\boxed{|A|=-52}\]
🤔 Did You Know?
Why Expansion Along \(C_3\) Is Better?
The third column is
\[\begin{bmatrix}4\\0\\0\end{bmatrix}\]
so two of the three cofactor terms immediately become zero. Only one \(2\times2\) determinant needs to be calculated.

This illustrates an important examination strategy: always inspect the determinant before starting the expansion. Choosing the most convenient row or column can significantly reduce calculation time and the possibility of arithmetic errors.
⚡ Exam Tip
❌ Common Mistakes
When expanding along \(C_3\), the element \(4=a_{13}\) has cofactor sign
\[(-1)^{1+3}=+1\]
Therefore, its contribution is positive:
\[+4\begin{vmatrix}-1&3\\4&1\end{vmatrix}\]
A common error is to introduce an unnecessary negative sign.
🔑 Key Takeaway

Example 2

❓ Question
Evaluate
\[\Delta=\begin{vmatrix}0 & \sin\alpha & -\cos\alpha\\-\sin\alpha & 0 & \sin\beta\\\cos\alpha & -\sin\beta & 0\end{vmatrix}\]
🗒️ Concept
This is a determinant of order \(3\). The main idea is to use cofactor expansion efficiently and carefully handle the signs of the cofactors.

The first row contains a zero, so expansion along \(R_1\) is convenient. The cofactor sign pattern along the first row is
\[+,\quad -,\quad +\]
Therefore, for
\[\begin{vmatrix}a&b&c\\d&e&f\\g&h&i\end{vmatrix},\]
expansion along the first row gives
\[a\begin{vmatrix}e&f\\h&i\end{vmatrix}-b\begin{vmatrix}d&f\\g&i\end{vmatrix}+c\begin{vmatrix}d&e\\g&h\end{vmatrix}\]
🗺️ Roadmap
  1. Expand the determinant along the first row.

  2. The first term vanishes because its coefficient is \(0\).

  3. Evaluate the two remaining \(2\times2\) determinants.

  4. Use the fact that multiplication is commutative to identify cancellation.

🧩 Solution
Given:
\[\Delta=\begin{vmatrix}0 & \sin\alpha & -\cos\alpha\\-\sin\alpha & 0 & \sin\beta\\\cos\alpha & -\sin\beta & 0\end{vmatrix}\]
  1. Expanding along the first row, whose signs are \(+,-,+\), we obtain
    \[\begin{aligned}\Delta&=0\begin{vmatrix}0&\sin\beta\\-\sin\beta&0\end{vmatrix}- \sin\alpha\begin{vmatrix}-\sin\alpha&\sin\beta\\\cos\alpha&0\end{vmatrix}\\&\quad+ (-\cos\alpha)\begin{vmatrix}-\sin\alpha&0\\\cos\alpha&-\sin\beta\end{vmatrix}\end{aligned}\]
  2. The first term is zero. Therefore,
    \[\begin{aligned}\Delta&=-\sin\alpha\left[(-\sin\alpha)(0)-(\sin\beta)(\cos\alpha)\right]\\&\quad -\cos\alpha\left[(-\sin\alpha)(-\sin\beta)-(0)(\cos\alpha)\right]\end{aligned}\]
  3. Thus,
    \[\begin{aligned}\Delta&=-\sin\alpha(-\cos\alpha\sin\beta)-\cos\alpha(\sin\alpha\sin\beta)\\&=\sin\alpha\cos\alpha\sin\beta- \cos\alpha\sin\alpha\sin\beta\\&=0\end{aligned}\]
  4. Hence,
    \[\boxed{\Delta=0}\]
Given:
\[A=\begin{bmatrix}0 & \sin\alpha & -\cos\alpha\\-\sin\alpha & 0 & \sin\beta\\\cos\alpha & -\sin\beta & 0\end{bmatrix}\]
Alternative Conceptual Method: Skew-Symmetric Matrix
  1. Transpose of given MAtrix
    \[A^T=\begin{bmatrix}0 & -\sin\alpha & \cos\alpha\\\sin\alpha & 0 & -\sin\beta\\-\cos\alpha & \sin\beta & 0\end{bmatrix}\]
  2. Therefore,
    \[A^T=-A\]
    Hence \(A\) is a skew-symmetric matrix
  3. A fundamental property of skew-symmetric matrices is that a skew-symmetric matrix of odd order has determinant zero. Since \(A\) is of order \(3\), which is odd.,
  4. Hence,
    \[\boxed{\det(A)=0}\]
🌟 Exam-Focused Insight

This example can be solved in two ways:

  1. Direct method: Expand the determinant and simplify the resulting terms.
  2. Property-based method: Recognise the matrix as skew-symmetric of odd order and immediately conclude that its determinant is zero.

The second method is considerably faster in competitive examinations. However, for CBSE board examinations, students should understand the direct expansion as well because it demonstrates the determinant calculation explicitly.

❌ Common Mistakes
When expanding the third element of the first row, remember that the element itself is \(-\cos\alpha\), while its cofactor position \((1,3)\) has a positive sign. Therefore, its contribution is
\[(+)(-\cos\alpha)\begin{vmatrix}-\sin\alpha&0\\\cos\alpha&-\sin\beta\end{vmatrix}\]
not
\[+\cos\alpha\begin{vmatrix}-\sin\alpha&0\\\cos\alpha&-\sin\beta\end{vmatrix}\]
⚡ Exam Tip

Example 3

❓ Question
Find the value(s) of \(x\) for which
\[\begin{vmatrix}3 & x\\x & 1\end{vmatrix}=\begin{vmatrix}3 & 2\\4 & 1\end{vmatrix}\]
💡 Concept
🗺️ Roadmap
  1. Evaluate the determinant containing \(x\).

  2. Evaluate the numerical determinant on the right-hand side.

  3. Equate the two resulting expressions.

  4. Solve the resulting quadratic equation.

  5. Since the equation involves \(x^2\), consider both positive and negative roots.

🧩 Solution
Given:
\[\begin{vmatrix}3&x\\x&1\end{vmatrix}=\begin{vmatrix}3&2\\4&1\end{vmatrix}\]
  1. Using
    \[\begin{vmatrix}a&b\\c&d\end{vmatrix}=ad-bc\]
  2. the left-hand determinant becomes
    \[\begin{aligned}\begin{vmatrix}3&x\\x&1\end{vmatrix}&=3(1)-x(x)\\&=3-x^2\end{aligned}\]
  3. The determinant on the right-hand side is
    \[\begin{aligned}\begin{vmatrix}3&2\\4&1\end{vmatrix}&=3(1)-2(4)\\&=3-8\\&=-5\end{aligned}\]
  4. Therefore,
    \[3-x^2=-5\]
  5. Hence,
    \[\begin{aligned}-x^2&=-8\\x^2&=8\end{aligned}\]
  6. Taking square roots,
    \[x=\pm\sqrt{8}=\pm2\sqrt{2}\]
  7. Therefore, the required values are
    \[\boxed{x=\pm2\sqrt{2}}\]
🤔 Did You Know?
Why Two Values of \(x\) Occur?
The determinant on the left contains \(x\) in both off-diagonal positions:
\[\begin{vmatrix}3&x\\x&1\end{vmatrix}=3-x^2\]
Thus, its value depends on \(x^2\), not directly on the sign of \(x\). Consequently, \(x\) and \(-x\) produce the same determinant value. This explains why both
\[2\sqrt2\quad\text{and}\quad-2\sqrt2\]
satisfy the equation.

⚡ Exam Tip
❌ Common Mistakes
  • Using \(ad+bc\) instead of \(ad-bc\) for a \(2\times2\) determinant.
  • Calculating \(2\times4=6\) instead of \(8\).
  • Forgetting that \(x\times x=x^2\).
  • Writing only \(x=2\sqrt2\) and omitting \(x=-2\sqrt2\).
  • Taking \(\sqrt{x^2}=x\) without considering the negative root.
🌟 Significance
Board Examination Significance
This type of question tests two fundamental skills: evaluation of a determinant of order \(2\) and conversion of a determinant equation into an algebraic equation. Similar questions may involve parameters, unknown entries, or conditions on the determinant and are useful practice for the NCERT Class 12 Determinants chapter and CBSE board examinations.

Area of a Triangle Using Determinants

📖 Introduction
🔢 Formula for the Area of a Triangle
🤔 Did You Know?
Why the Third Column Contains \(1\)
The coordinate pair of a point is normally written as \((x,y)\). For determinant-based calculations, each point is represented in the form
\[(x,y,1)\]
Thus,
\[A(x_1,y_1)\rightarrow(x_1,y_1,1),\]
\[B(x_2,y_2)\rightarrow(x_2,y_2,1),\]
\[C(x_3,y_3)\rightarrow(x_3,y_3,1)\]
Therefore, the coordinate determinant becomes
\[\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]
The third column of \(1\)'s is what allows the coordinate expression to be represented as a \(3\times3\) determinant.
📐 Derivation of the Area Formula from the Determinant
Consider
\[D=\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]
Expanding along the first row,
\[\begin{aligned}D&=x_1\begin{vmatrix}y_2&1\\y_3&1\end{vmatrix}-y_1\begin{vmatrix}x_2&1\\x_3&1\end{vmatrix}+ 1\begin{vmatrix}x_2&y_2\\x_3&y_3\end{vmatrix}\\&=x_1(y_2-y_3)-y_1(x_2-x_3)+(x_2y_3-x_3y_20)\end{aligned}\]
Rearranging the terms,
\[\begin{aligned}D&=x_1(y_2-y_3)+x_2(y_3-y_1)+x_3(y_1-y_2)\end{aligned}\]
Hence,
\[\boxed{D=x_1(y_2-y_3)+x_2(y_3-y_1)+x_3(y_1-y_2)}\]
Since the area is half the magnitude of this determinant,
\[\text{Area}=\frac12\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]
✏️ Example
1
Question
Find the area of the triangle whose vertices are
\[A(1,2),\qquad B(3,4),\qquad C(5,1)\]
Use
\[\text{Area}=\frac12\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]
  1. Area of Triangle
    \[\begin{aligned}\text{Area}&=\frac12\left(\begin{vmatrix}1&2&1\\3&4&1\\5&1&1\end{vmatrix}\right)\end{aligned}\]
  2. Expanding along the first row,
    \[\begin{aligned}\text{Area}&=\frac12\left|1\begin{vmatrix}4&1\\1&1\end{vmatrix}- 2\begin{vmatrix}3&1\\5&1\end{vmatrix}+\begin{vmatrix}3&4\\5&1\end{vmatrix}\right|\\ &=\frac12|1(4-1)-2(3-5)+(3-20)|\\&=\frac12|3+4-17|\\&=\frac12|-10|\\&=5\end{aligned}\]
  3. Therefore,
    \[\boxed{\text{Area}=5\text{ square units}}.\]
2
Question
Find \(x\) if the area of the triangle with vertices
\[(x,0),\qquad (2,3),\qquad (4,1)\]
is \(5\) square units.
When the area is given, the determinant itself can be positive or negative because the absolute value is taken in the area formula.

Therefore, if
\[\frac12|D|=5\]
then
\[|D|=10\]
which gives
\[D=10\quad\text{or}\quad D=-10\]
  1. Area of Triangle
    \[\frac12\begin{vmatrix}x&0&1\\2&3&1\\4&1&1\end{vmatrix}=5\]
  2. Therefore,
    \[\begin{vmatrix}x&0&1\\2&3&1\\4&1&1\end{vmatrix}=10\]
  3. Expanding the determinant along the first row,
    \[\begin{aligned}D&=x\begin{vmatrix}3&1\\1&1\end{vmatrix}+\begin{vmatrix}2&3\\4&1\end{vmatrix}\\&=x(3-1)+(2-12)\\&=2x-10\end{aligned}\]
  4. Since \(|D|=10\),
    \[2x-10=10\]
    or
    \[2x-10=-10\]
  5. Thus,
    \[2x=20\]
    or
    \[2x=0\]
  6. Hence,
    \[\boxed{x=10\quad\text{or}\quad x=0}\]
🌟 Important Exam Insight
When the area is given, never simply equate the determinant to twice the area without considering its sign. The correct relation is
\[\left|D\right|=2(\text{Area}),\]
so both
\[D=2(\text{Area})\]
and
\[D=-2(\text{Area})\]
must be considered.
📌 Collinearity of Three Points Using Determinants
✏️ Example
3
Question
Show that the points
\[A(1,2),\qquad B(3,4),\qquad C(5,6)\]
are collinear.
  1. \[\begin{aligned}\text{Area}&=\frac12\begin{vmatrix}1&2&1\\3&4&1\\5&6&1\end{vmatrix}\end{aligned}\]
  2. Expanding,
    \[\begin{aligned}\begin{vmatrix}1&2&1\\3&4&1\\5&6&1\end{vmatrix}&=1(4-6)-2(3-5)+(18-20)\\&=-2+4-2\\&=0\end{aligned}\]
  3. Therefore,
    \[\text{Area}=0\]
  4. Hence,
    \[\boxed{\text{The three points are collinear.}}\]
🧭 Condition for Collinearity
🧭
Condition The determinant criterion for three points
\[(x_1,y_1),\quad(x_2,y_2),\quad(x_3,y_3)\]
to be collinear is
\[\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}=0\]
Equivalently,
\[x_1(y_2-y_3)+x_2(y_3-y_1)+x_3(y_1-y_2)=0\]
🔍 Geometrical Meaning of the Determinant Sign
The determinant
\[D=\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]
may be positive or negative depending upon the ordering of the three vertices.

If the order of the points is changed, the sign of the determinant may change, but its absolute value remains unchanged. Since geometric area does not depend on the direction in which the vertices are listed, the area is
\[\frac12|D|\]
Effect of Interchanging Two Points
Interchanging any two rows of a determinant changes its sign. Therefore, if two vertices are interchanged, the determinant changes from \(D\) to \(-D\).

However,
\[|-D|=|D|\]
Hence, the area remains unchanged.

This explains algebraically why the area of a triangle is independent of the order in which its vertices are named.
🔢 Formula for Twice the Area
🗒️ Important Remarks
  1. Area is always positive:
    \[\text{Area}=\frac12|D|.\]
  2. If area is given: use
    \[D=\pm2(\text{Area}).\]
  3. For collinear points:
    \[D=0.\]
  4. Three non-collinear points:
    \[D\neq0.\]
  5. The unit of area is the square of the coordinate unit, such as square units,\(\mathrm{cm}^2\), or \(\mathrm{m}^2\).
  6. The determinant must contain \(1\) in the third column:
    \[\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]
❌ Common Mistakes
  • Forgetting the factor \(\frac12\).
  • Forgetting the absolute value when calculating an actual area.
  • Writing \(0\) in the third column instead of \(1\).
  • Using the coordinates in the wrong rows or columns inconsistently.
  • When the area is given, considering only \(D=2(\text{Area})\) and forgetting \(D=-2(\text{Area})\).
  • Concluding that the area is negative because the determinant evaluates to a negative number.
  • For collinearity questions, calculating the determinant as non-zero due to an arithmetic or sign error.
⚡ Exam Tip
📋 CBSE Case Study / HOTS

The vertices of a triangle are

\[A(2,3),\qquad B(5,7),\qquad C(k,11).\]
If the area of the triangle is \(6\) square units, find the possible values of \(k\).

Concept

Since the area is given, use

\[\begin{vmatrix}2&3&1\\5&7&1\\k&11&1\end{vmatrix}=12\]

Solution

\[\begin{aligned}D&=\begin{vmatrix}2&3&1\\5&7&1\\k&11&1\end{vmatrix}\\&=2(7-11)-3(5-k)+(55-7k)\\&=-8-15+3k+55-7k\\&=32-4k\end{aligned}\]

Since the area is \(6\),

\[|D|=12\]

Therefore,

\[32-4k=12\]
or
\[32-4k=-12\]

From the first equation,

\[4k=20\quad\Rightarrow\quad k=5\]

From the second equation,

\[4k=44\quad\Rightarrow\quad k=11\]

Hence,

\[\boxed{k=5\text{ or }11}\]

HOTS Insight

The two values arise because the determinant represents an oriented area whose sign depends on the ordering and position of the points, whereas the geometrical area uses its absolute value.

⚡ Quick Revision

For

\[A(x_1,y_1),\quad B(x_2,y_2),\quad C(x_3,y_3),\]
the area of \(\triangle ABC\) is

\[\text{Area}=\frac12\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]

or equivalently,

\[\text{Area}=\frac12\left|x_1(y_2-y_3)+x_2(y_3-y_1)+x_3(y_1-y_2)\right|\]

For a given area \(K\),

\[\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}=\pm2K\]

For collinear points,

\[\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}=0\]

🔑 Key Takeaway

Example 4

❓ Question
Find the area of the triangle whose vertices are
\[A(3,8),\qquad B(-4,2),\qquad C(5,1)\]
💡 Concept
🗺️ Roadmap
  1. Write the three coordinates in the \(3\times3\) determinant.
  2. Multiply the determinant by \(\frac12\).
  3. Evaluate the determinant carefully, keeping track of negative coordinates.
  4. Take the absolute value of the determinant.
  5. State the answer in square units.
🧩 Solution
Given:
\[A(3,8),\qquad B(-4,2),\qquad C(5,1)\]
  1. Using the determinant formula for the area of a triangle,
    \[\begin{aligned}\Delta&=\frac12\left(\begin{vmatrix}3&8&1\\-4&2&1\\5&1&1\end{vmatrix}\right)\end{aligned}\]
  2. Expanding the determinant along the first row,
    \[\begin{aligned}\Delta&=\frac12\left(3\begin{vmatrix}2&1\\1&1\end{vmatrix}- 8\begin{vmatrix}-4&1\\5&1\end{vmatrix}+1\begin{vmatrix}-4&2\\5&1\end{vmatrix}\right)\\ &=\frac12\left(3(2-1)-8[(-4)(1)-(1)(5)]+[(-4)(1)-(2)(5)]\right)\end{aligned}\]
  3. Now simplify each term:
    \[\begin{aligned}\Delta&=\frac12\left(3(1)-8(-9)+(-14)\right)\\&=\frac12|3+72-14|\\&=\frac12|61|\\&=\frac{61}{2}\end{aligned}\]
  4. Therefore,
    \[\boxed{\text{Area}=\frac{61}{2}\text{ square units}}\]
  5. or
    \[\boxed{\text{Area}=30.5\text{ square units}}.\]
⚡ Exam Tip
❌ Common Mistakes
  • Forgetting the factor \(\frac12\).
  • Forgetting the absolute value around the determinant.
  • Dropping the negative sign from the coordinate \((-4,2)\).
  • Using \(ad+bc\) instead of \(ad-bc\) while evaluating the \(2\times2\) minors.
  • Reporting \(61\) as the area instead of \(\frac{61}{2}\).
  • Forgetting to mention square units.
🌟 Board Examination Significance
This is a standard application of determinants and tests whether a student can translate coordinate data into a determinant and evaluate it accurately. Questions of this type are useful for CBSE board preparation and also form the foundation for higher-level problems involving collinearity, unknown coordinates, parameters, and prescribed areas.
⚡ Quick Revision
For three points
\[(x_1,y_1),\quad(x_2,y_2),\quad(x_3,y_3),\]
remember
\[\boxed{\text{Area}=\frac12\left|\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\right|}\]
For the present example,
\[\begin{aligned}\Delta&=\frac12\left|\begin{vmatrix}3&8&1\\-4&2&1\\5&1&1\end{vmatrix}\right|\\&=\frac12|61|\\&=\frac{61}{2}\end{aligned}\]
Therefore,
\[\boxed{\text{Area}=30.5\text{ square units}}.\]

Example 5

❓ Question
Find the equation of the line joining the points
\[ A(1,3)\quad\text{and}\quad B(0,0) \]
using determinants. Also find \(k\) if \(D(k,0)\) is a point such that the area of triangle \(ABD\) is \(3\) square units.
💡 Concept
🗺️ Roadmap
  1. Let \(P(x,y)\) be any point on the line \(AB\).
  2. Since \(A\), \(B\), and \(P\) are collinear, the area of triangle \(ABP\) is zero.
  3. Write the zero-area condition as a determinant.
  4. Expand and simplify the determinant to obtain the equation of the line.
🧩 Solution
Part (a)
Equation of the Line \(AB\)
  1. Let \(P(x,y)\) be an arbitrary point on the line joining \(A(1,3)\) and \(B(0,0)\).

    Since \(A\), \(B\), and \(P\) are collinear,
    \[\text{Area of }\triangle ABP=0\]
  2. Therefore,
    \[\begin{vmatrix}x&y&1\\1&3&1\\0&0&1\end{vmatrix}=0\]
  3. Expanding along the first row,
    \[\begin{aligned}0&=x\begin{vmatrix}3&1\\0&1\end{vmatrix}-y\begin{vmatrix}1&1\\0&1\end{vmatrix}+ \begin{vmatrix}1&3\\0&0\end{vmatrix}\\&=x(3)-y(1)+0\end{aligned}\]
  4. Hence,
    \[3x-y=0\]
  5. Therefore, the equation of the line \(AB\) is
    \[\boxed{y=3x}\]
  6. or equivalently,
    \[\boxed{3x-y=0}\]
Given:
\[A(1,3),\qquad B(0,0),\qquad D(k,0),\]
and
\[\text{Area of }\triangle ABD=3\text{ square units}\]
Part (b)
Finding \(k\) Using the Area of Triangle \(ABD\)
Formula: 
\[\text{Area}=\frac12\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\]
  1. Area of triangle ABD is 3sq units, therefore,
    \[\frac12\begin{vmatrix}1&3&1\\0&0&1\\k&0&1\end{vmatrix}=3\]
  2. Multiply both sides by \(2\):
    \[\begin{vmatrix}1&3&1\\0&0&1\\k&0&1\end{vmatrix}=6\]
  3. Expanding the determinant along the first row,
    \[\begin{aligned}D&=1\begin{vmatrix}0&1\\0&1\end{vmatrix}-3\begin{vmatrix}0&1\\k&1\end{vmatrix}+ 1\begin{vmatrix}0&0\\k&0\end{vmatrix}\\&=1(0)-3(0-k)+1(0)\\&=3k\end{aligned}\]
  4. Hence,
    \[|3k|=6\]
  5. Therefore,
    \[3k=6\quad\text{or}\quad 3k=-6\]
  6. Thus,
    \[k=2\quad\text{or}\quad k=-2\]
  7. Therefore, the possible values are
    \[\boxed{k=\pm2}\]
🔢 Key Formula: Equation of a Line Using Determinants
⚡ Exam Tip
❌ Common Mistakes
  • Writing the determinant equal to the area instead of twice the area.
  • Forgetting the absolute value when the area is specified.
  • Assuming \(k\) must be positive without an explicit condition in the question.
  • Using the point \(D(k,0)\) incorrectly as \((0,k)\).
  • Making a sign error in the cofactor expansion of the second element \(3\).

Minor and Cofactor of an Element of a Determinant

🗺️ Overview
Minor and cofactor are fundamental concepts in the evaluation of determinants of order \(3\) and higher. They provide the building blocks for cofactor expansion, also called Laplace expansion, which is an important technique in NCERT Class 12 Mathematics Chapter 4: Determinants.

A clear understanding of minors and cofactors is essential for solving problems involving expansion of determinants, finding determinants efficiently, calculating adjoint and inverse of a matrix, and proving determinant identities.
📘 Minor of an Element
🧰 How to Find a Minor
  1. Locate the element \(a_{ij}\).
  2. Delete its entire \(i\)-th row.
  3. Delete its entire \(j\)-th column.
  4. The determinant that remains is the minor \(M_{ij}\).
  5. Thus,
    \[\boxed{M_{ij}=\text{determinant obtained after deleting row }i\text{ and column }j}\]
📘 Order of a Minor
📘 Cofactor of an Element
⚖️ Minor vs Cofactor
Concept Notation Definition
Minor \(M_{ij}\) Determinant obtained after deleting the \(i\)-th row and \(j\)-th column.
Cofactor \(A_{ij}\) \(A_{ij}=(-1)^{i+j}M_{ij}\)
The important distinction is that a minor has no additional sign factor, whereas a cofactor includes the sign \((-1)^{i+j}\).
🗒️ Cofactor Sign Pattern
The sign of a cofactor depends only on the position \((i,j)\) of the element. The signs follow an alternating checkerboard pattern:
\[\boxed{\begin{matrix}+&-&+&-&\cdots\\-&+&-&+&\cdots\\+&-&+&-&\cdots\\-&+&-&+&\cdots\end{matrix}}\]
For a \(3\times3\) determinant, the cofactor signs are
\[\boxed{\begin{matrix}+&-&+\\-&+&-\\+&-&+\end{matrix}}\]
This pattern follows directly from
\[(-1)^{i+j}\]
🤔 Did You Know?
Why Does the Sign Alternate?
If \(i+j\) is even, then
\[(-1)^{i+j}=+1,\]
so
\[A_{ij}=M_{ij}\]
If \(i+j\) is odd, then
\[(-1)^{i+j}=-1,\]
so
\[A_{ij}=-M_{ij}\]
✏️ Example
1
Question
Find the minor \(M_{12}\) of the element \(a_{12}\) in
\[D=\begin{vmatrix}2&5&7\\1&3&4\\6&8&9\end{vmatrix}\]
The element \(a_{12}=5\) lies in the first row and second column. To find \(M_{12}\), delete the first row and second column.
  1. After deleting \(R_1\) and \(C_2\), the remaining determinant is
    \[M_{12}=\begin{vmatrix}1&4\\6&9\end{vmatrix}\]
  2. Therefore,
    \[\begin{aligned}M_{12}&=1(9)-4(6)\\&=9-24\\&=-15\end{aligned}\]
  3. Hence,
    \[\boxed{M_{12}=-15}\]
2
Question
For
\[D=\begin{vmatrix}2&5&7\\1&3&4\\6&8&9\end{vmatrix},\]
find the cofactor \(A_{12}\) of \(a_{12}\).
  1. From the previous example,
    \[M_{12}=-15\]
  2. Using the cofactor formula,
    \[ A_{12}=(-1)^{1+2}M_{12}\]
  3. Since
    \[(-1)^3=-1\]
  4. we obtain
    \[A_{12}=(-1)(-15)=15\]
  5. Therefore,
    \[\boxed{A_{12}=15}\]
  6. Important Observation
    Notice that
    \[M_{12}=-15\]
    but
    \[A_{12}=15\]
    The sign changed because the position \((1,2)\) has a negative cofactor sign.
3
Question
Find the matrix of minors and the matrix of cofactors of
\[A=\begin{bmatrix}1&2&3\\4&5&6\\7&8&9\end{bmatrix}\]
For each element, delete its row and column to obtain its minor.
  1. First Row Minors
    \[\begin{aligned}M_{11}&=\begin{vmatrix}5&6\\8&9\end{vmatrix}=45-48=-3,\end{aligned}\]
    \[\begin{aligned}M_{12}&=\begin{vmatrix}4&6\\7&9\end{vmatrix}=36-42=-6,\end{aligned}\]
    \[\begin{aligned}M_{13}&=\begin{vmatrix}4&5\\7&8\end{vmatrix}=32-35=-3\end{aligned}\]
  2. Second Row Minors
    \[\begin{aligned}M_{21}&=\begin{vmatrix}2&3\\8&9\end{vmatrix}=18-24=-6,\end{aligned}\]
    \[\begin{aligned}M_{22}&=\begin{vmatrix}1&3\\7&9\end{vmatrix}=9-21=-12,\end{aligned}\]
    \[\begin{aligned}M_{23}&=\begin{vmatrix}1&2\\7&8\end{vmatrix}=8-14=-6\end{aligned}\]
  3. Third Row Minors
    \[\begin{aligned}M_{31}&=\begin{vmatrix}2&3\\5&6\end{vmatrix}=12-15=-3,\end{aligned}\]
    \[\begin{aligned}M_{32}&=\begin{vmatrix}1&3\\4&6\end{vmatrix}=6-12=-6,\end{aligned}\]
    \[\begin{aligned}M_{33}&=\begin{vmatrix}1&2\\4&5\end{vmatrix}=5-8=-3\end{aligned}\]
  4. Therefore, the matrix of minors is
    \[[M_{ij}]=\begin{bmatrix}-3&-6&-3\\-6&-12&-6\\-3&-6&-3\end{bmatrix}\]
  5. Matrix of Cofactors
    Apply the sign pattern
    \[\begin{bmatrix}+&-&+\\-&+&-\\+&-&+\end{bmatrix}\]
    to the corresponding minors.
  6. Thus,
    \[\begin{aligned}A_{11}&=(-3)=-3,\\A_{12}&=-(-6)=6,\\A_{13}&=(-3)=-3,\\ A_{21}&=-(-6)=6,\\A_{22}&=(-12)=-12,\\A_{23}&=-(-6)=6,\\ A_{31}&=(-3)=-3,\\A_{32}&=-(-6)=6,\\A_{33}&=(-3)=-3\end{aligned}\]
  7. Therefore, the matrix of cofactors is
    \[[A_{ij}]=\begin{bmatrix}-3&6&-3\\6&-12&6\\-3&6&-3\end{bmatrix}\]
⚖️ Minor and Cofactor: A Direct Comparison
Consider the element \(a_{23}\). Its position is row \(2\), column \(3\). Its minor is
\[M_{23}=\begin{vmatrix}a_{11}&a_{12}\\a_{31}&a_{32}\end{vmatrix}\]
Its cofactor is
\[A_{23}=(-1)^{2+3}M_{23}=-M_{23}\]
Thus, the minor tells us which determinant remains, while the cofactor tells us which signed minor contributes to a determinant expansion.
🔗 Relationship Between an Element, Its Minor and Its Cofactor
For an element \(a_{ij}\), the three related quantities are:
\[\boxed{a_{ij}}\qquad \boxed{M_{ij}} \qquad \boxed{A_{ij}}\]
They are connected by
\[\boxed{A_{ij}=(-1)^{i+j}M_{ij}}.\]
The element \(a_{ij}\) itself is not part of the minor. The row and column containing \(a_{ij}\) are removed before the minor is calculated
🤔 Did You Know?
Why Cofactors Are Important
Cofactors are not merely an alternative notation. They are central to several important results in matrix algebra and determinants.
  • Expansion of a determinant along any row.
  • Expansion of a determinant along any column.
  • Calculation of the adjoint of a matrix.
  • Finding the inverse of a non-singular matrix.
  • Proofs involving determinant properties.
  • Solving many parameter-based determinant problems efficiently.
🔗 Connection with Cofactor Expansion
Once the cofactors are known, a determinant can be expanded along any row or column. For a \(3\times3\) determinant
\[D=\begin{vmatrix}a_{11}&a_{12}&a_{13}\\a_{21}&a_{22}&a_{23}\\a_{31}&a_{32}&a_{33}\end{vmatrix},\]
expansion along the first row gives
\[\boxed{D=a_{11}A_{11}+a_{12}A_{12}+a_{13}A_{13}}.\]
Similarly, expansion along the second row gives
\[\boxed{D=a_{21}A_{21}+a_{22}A_{22}+a_{23}A_{23}}.\]
Expansion along the third row gives
\[\boxed{D=a_{31}A_{31}+a_{32}A_{32}+a_{33}A_{33}}.\]
This is called cofactor expansion along a row.
📎 General Cofactor Expansion Formula
For an \(n\times n\) determinant \(D=[a_{ij}]\), expansion along the \(i\)-th row is
\[\boxed{D=a_{i1}A_{i1}+a_{i2}A_{i2}+\cdots+a_{in}A_{in}}.\]
Expansion along the \(j\)-th column is
\[\boxed{D=a_{1j}A_{1j}+a_{2j}A_{2j}+\cdots+a_{nj}A_{nj}}\]
These formulas are the foundation of Laplace expansion.
📐 Derivation of the Cofactor Sign Pattern
By definition,
\[A_{ij}=(-1)^{i+j}M_{ij}.\]
If \(i+j\) is even, then
\[(-1)^{i+j}=1.\]
Therefore, the cofactor is positive relative to its minor:
\[A_{ij}=M_{ij}.\]
If \(i+j\) is odd, then
\[(-1)^{i+j}=-1.\]
Therefore,
\[A_{ij}=-M_{ij}.\]
This produces the alternating sign arrangement
\[\boxed{\begin{matrix}+&-&+&-&+&\cdots\\-&+&-&+&-&\cdots\\+&-&+&-&+&\cdots\\-&+&-&+&-&\cdots\end{matrix}}.\]
⚡ Exam Tip
❌ Common Mistakes
  • Deleting only the element \(a_{ij}\) instead of deleting its entire row and column.
  • Forgetting the factor \((-1)^{i+j}\) while finding a cofactor.
  • Using the wrong sign for \(A_{12}\), \(A_{21}\), \(A_{23}\), or \(A_{32}\).
  • Confusing the notation \(A_{ij}\) for a cofactor with the original matrix \(A\).
  • Writing the minor and cofactor as if they are always equal.
  • Using the wrong \(2\times2\) determinant after deleting a row and column.
🌟 CBSE and Competitive Exam Perspective
Minor and cofactor questions are often embedded within larger determinant and matrix problems. A question may ask you to calculate a particular cofactor, expand a determinant along a specified row or column, find the adjoint of a matrix, or determine the inverse of a matrix.

For competitive examinations, the key advantage is speed. If a determinant contains a row or column with several zeros, cofactor expansion can reduce a complicated calculation to one or two small determinants.
⚡ Quick Revision
Term Meaning Formula
Element Entry in row \(i\), column \(j\) \(a_{ij}\)
Minor Delete row \(i\) and column \(j\), then take the remaining determinant \(M_{ij}\)
Cofactor Signed minor \(A_{ij}=(-1)^{i+j}M_{ij}\)
Cofactor expansion Sum of element × corresponding cofactor \(D=\sum a_{ij}A_{ij}\)
🔑 Key Takeaway

Example 6

❓ Question
Find the minor of the element \(6\) in the determinant
\[\Delta=\begin{vmatrix}1&2&3\\4&5&6\\7&8&9\end{vmatrix}\]
💡 Concept
🗺️ Roadmap
  1. Locate the given element \(6\).

  2. Identify its row and column: \(2\)nd row and \(3\)rd column.

  3. Delete the 2nd row and 3rd column.

  4. Evaluate the remaining \(2\times2\) determinant.

🧩 Solution
Given:
\[\Delta=\begin{vmatrix}1&2&3\\4&5&6\\7&8&9\end{vmatrix}\]
  1. The element \(6\) is situated at the position
    \[(2,3)\]
    📝 Hence,
    \[M_{23}\]
    is obtained by deleting the 2nd row and the 3rd column.
  2. After deleting \(R_2\) and \(C_3\), the remaining determinant is
    \[M_{23}=\begin{vmatrix}1&2\\7&8\end{vmatrix}\]
  3. Using
    \[\begin{vmatrix}a&b\\c&d\end{vmatrix}=ad-bc,\]
  4. we get
    \[\begin{aligned}M_{23}&=1(8)-2(7)\\&=8-14\\&=-6\end{aligned}\]
  5. Minor vs Cofactor: Important Distinction
  6. 📝 The question asks for the minor, so the answer is simply
    \[\boxed{M_{23}=-6}\]
    If the question instead asked for the cofactor of \(6\), we would have to apply the sign factor:
    \[A_{23}=(-1)^{2+3}M_{23}\]
    Therefore,
    \[\begin{aligned}A_{23}&=(-1)^5(-6)\\&=(-1)(-6)\\&=6\end{aligned}\]
    Hence,
    \[\boxed{M_{23}=-6,\qquad A_{23}=6}\]
⚡ Exam Tip
❌ Common Mistakes
  • Deleting the wrong row or column.
  • Deleting only the element \(6\) instead of its complete row and column.
  • Writing \(M_{32}\) instead of \(M_{23}\).
  • Applying the cofactor sign while the question asks only for the minor.
  • Using \(ad+bc\) instead of \(ad-bc\) for the \(2\times2\) determinant.

Example 7

❓ Question
Find the minors and cofactors of all the elements of the determinant
\[\Delta=\begin{vmatrix}1&-2\\4&3\end{vmatrix}\]
💡 Concept
🗺️ Roadmap
  1. Identify the position of each element.

  2. Delete its row and column to obtain its minor.

  3. Use \(A_{ij}=(-1)^{i+j}M_{ij}\) to obtain its cofactor.

  4. Present the results in matrices for quick revision.

🗒️ Soution
Given: Determinant
\[\Delta=\begin{vmatrix}1&-2\\4&3\end{vmatrix}\]
Formula: 
\[\boxed{A_{ij}=(-1)^{i+j}M_{ij}}\]
  1. The elements are
    \[a_{11}=1,\qquad a_{12}=-2,\qquad a_{21}=4,\qquad a_{22}=3\]
  2. 1. Minor of \(a_{11}=1\)
    \[M_{11}=\begin{vmatrix}3\end{vmatrix}=3\]
    📝 The element \(a_{11}\) lies in the first row and first column. Delete row \(1\) and column \(1\):
  3. Therefore,
    \[\boxed{M_{11}=3}\]
  4. 2. Minor of \(a_{12}=-2\)
    \[M_{12}=\begin{vmatrix}4\end{vmatrix}=4\]
    📝 The element \(a_{12}\) lies in the first row and second column. Delete row \(1\) and column \(2\):
  5. Hence,
    \[\boxed{M_{12}=4}\]
  6. 3. Minor of \(a_{21}=4\)
    \[M_{21}=\begin{vmatrix}-2\end{vmatrix}=-2\]
    📝 The element \(a_{21}\) lies in the second row and first column. Delete row \(2\) and column \(1\):
  7. Therefore,
    \[\boxed{M_{21}=-2}\]
  8. 4. Minor of \(a_{22}=3\)
    \[_{22}=\begin{vmatrix}1\end{vmatrix}=1\]
    📝 The element \(a_{22}\) lies in the second row and second column. Delete row \(2\) and column \(2\):
  9. Hence,
    \[\boxed{M_{22}=1}\]
  10. Matrix of Minors

    Arranging the minors in their corresponding positions gives the matrix of minors:
  11. \[\boxed{[M_{ij}]=\begin{bmatrix}M_{11}&M_{12}\\M_{21}&M_{22}\end{bmatrix}=\begin{bmatrix}3&4\\-2&1\end{bmatrix}}\]
  12. Finding the Cofactors
  13. For a \(2\times2\) determinant, the cofactor sign pattern is
    \[\boxed{\begin{bmatrix}+&-\\-&+\end{bmatrix}}\]
  14. 1. Cofactor of \(a_{11}\)
    \[\begin{aligned}A_{11}&=(-1)^{1+1}M_{11}\\&=M_{11}\\&=3\end{aligned}\]
  15. Hence,
    \[\boxed{A_{11}=3}\]
  16. 2. Cofactor of \(a_{12}\)
    \[\begin{aligned}A_{12}&=(-1)^{1+2}M_{12}\\&=-M_{12}\\&=-4\end{aligned}\]
  17. Hence
    \[\boxed{A_{12}=-4}\]
  18. 3. Cofactor of \(a_{21}\)
    \[\begin{aligned}A_{21}&=(-1)^{2+1}M_{21}\\&=-M_{21}\\&=-(-2)\\&=2\end{aligned}\]
  19. Hence
    \[\boxed{A_{21}=2}\]
  20. 4. Cofactor of \(a_{22}\)
    \[\begin{aligned}A_{22}&=(-1)^{2+2}M_{22}\\&=M_{22}\\&=1\end{aligned}\]
  21. Hence,
    \[\boxed{A_{22}=1}\]
  22. Therefore, the matrix of cofactors is
    \[\boxed{[A_{ij}]=\begin{bmatrix}A_{11}&A_{12}\\A_{21}&A_{22}\end{bmatrix}=\begin{bmatrix}3&-4\\2&1\end{bmatrix}}\]
👁️ Important Observation
⚡ Exam Tip
🗒️ Commo Mistake
  • Writing \(M_{12}=-4\) merely because \(a_{12}=-2\).
  • Forgetting that the minor is found by deleting both the corresponding row and column.
  • Applying the cofactor sign twice.
  • Using the element itself instead of the remaining determinant to calculate a minor.
  • Confusing \(M_{ij}\), the minor, with \(A_{ij}\), the cofactor.

Example 8

❓ Question
Find the minor and cofactor of the elements \(a_{11}\) and \(a_{21}\) in the determinant
\[\Delta=\begin{vmatrix}a_{11}&a_{12}&a_{13}\\a_{21}&a_{22}&a_{23}\\a_{31}&a_{32}&a_{33}\end{vmatrix}\]
💡 Concept
🧩 Solution
Part (a)
Minor and Cofactor of \(a_{11}\)
  1. Step 1: Locate \(a_{11}\)
    📝 The element \(a_{11}\) lies in the first row and first column.
  2. To find its minor, delete the first row and first column:
    \[\begin{vmatrix}\cancel{a_{11}}&\cancel{a_{12}}&\cancel{a_{13}}\\\cancel{a_{21}}&a_{22}&a_{23}\\\cancel{a_{31}}&a_{32}&a_{33}\end{vmatrix}\]
  3. Step 2: Find \(M_{11}\)
    The remaining \(2\times2\) determinant is
    \[M_{11}=\begin{vmatrix}a_{22}&a_{23}\\a_{32}&a_{33}\end{vmatrix}\]
  4. Using
    \[\begin{vmatrix}a&b\\c&d\end{vmatrix}=ad-bc\]
  5. we obtain
    \[\begin{aligned}M_{11}&=a_{22}a_{33}-a_{23}a_{32}\end{aligned}\]
  6. Therefore,
    \[\boxed{M_{11}=a_{22}a_{33}-a_{23}a_{32}}\]
  7. Step 3: Find \(A_{11}\)
    By definition,
    \[A_{11}=(-1)^{1+1}M_{11}\]
  8. Since
    \[(-1)^2=1,\]
    we get
    \[A_{11}=M_{11}\]
  9. Hence,
    \[\boxed{A_{11}=a_{22}a_{33}-a_{23}a_{32}}\]
Part (b)
Minor and Cofactor of \(a_{21}\)
  1. Step 1: Locate \(a_{21}\)
    📝 The element \(a_{21}\) lies in the second row and first column.
  2. To find its minor, delete the second row and first column:
    \[\begin{vmatrix}\cancel{a_{11}}&a_{12}&a_{13}\\\cancel{a_{21}}&\cancel{a_{22}}&\cancel{a_{23}}\\\cancel{a_{31}}&a_{32}&a_{33}\end{vmatrix}.\]
  3. Step 2: Find \(M_{21}\)
    The remaining determinant is
    \[M_{21}=\begin{vmatrix}a_{12}&a_{13}\\a_{32}&a_{33}\end{vmatrix}\]
  4. Therefore,
    \[\begin{aligned}M_{21}&=a_{12}a_{33}-a_{13}a_{32}\end{aligned}\]
  5. Hence,
    \[\boxed{M_{21}=a_{12}a_{33}-a_{13}a_{32}}\]
  6. Step 3: Find \(A_{21}\)
    Using the cofactor formula,
    \[A_{21}=(-1)^{2+1}M_{21}\]
  7. Since
    \[(-1)^3=-1,\]
    we have
    \[A_{21}=-M_{21}\]
  8. Therefore,
    \[\begin{aligned}A_{21}&=-\left(a_{12}a_{33}-a_{13}a_{32}\right)\\&=-a_{12}a_{33}+a_{13}a_{32}\end{aligned}\]
  9. Thus,
    \[\boxed{A_{21}=a_{13}a_{32}-a_{12}a_{33}}\]
Element Minor Cofactor
\(a_{11}\)
\[ M_{11}=a_{22}a_{33}-a_{23}a_{32} \]
\[ A_{11}=a_{22}a_{33}-a_{23}a_{32} \]
\(a_{21}\)
\[ M_{21}=a_{12}a_{33}-a_{13}a_{32} \]
\[ A_{21}=a_{13}a_{32}-a_{12}a_{33} \]
👁️ Important Observation
⚡ Exam Tip
❌ Common Mistakes
  • Confusing \(M_{21}\) with \(M_{12}\). The first suffix denotes the row and the second suffix denotes the column.
  • Forgetting that \(a_{21}\) requires deletion of the second row and first column.
  • Applying a positive cofactor sign to \(A_{21}\). Its sign is negative because \(2+1=3\) is odd.
  • Changing the order of the products incorrectly while evaluating the \(2\times2\) determinant.
  • Confusing the minor with the cofactor.

Example 9

❓ Question
Find the minors and cofactors of all the elements of the determinant
\[\Delta=\begin{vmatrix}2&-3&5\\6&0&4\\1&5&-7\end{vmatrix}\]
and verify that
\[\boxed{a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33}=0}.\]
💡 Concept
🗺️ Roadmap
  1. Calculate the nine minors \(M_{ij}\).

  2. Apply the cofactor formula \(A_{ij}=(-1)^{i+j}M_{ij}\).

  3. Arrange the results in matrices for quick verification.

  4. Substitute \(a_{11},a_{12},a_{13}\) and \(A_{31},A_{32},A_{33}\) into the given identity.

  5. Verify that the resulting sum is zero.

🧩 Solution
Part (a)
Finding the Minors and Cofactors
  1. 1. Minor and Cofactor of \(a_{11}=2\) Delete the first row and first column:
    \[M_{11}=\begin{vmatrix}0&4\\5&-7\end{vmatrix}\]
  2. Therefore,
    \[\begin{aligned}M_{11}&=0(-7)-4(5)\\&=-20\end{aligned}\]
  3. Hence,
    \[\begin{aligned}A_{11}&=(-1)^{1+1}M_{11}\\&=(-1)^2(-20)\\&=-20\end{aligned}\]
  4. \[\boxed{M_{11}=-20,\qquad A_{11}=-20}\]
  5. 2. Minor and Cofactor of \(a_{12}=-3\) Delete the first row and second column:
    \[M_{12}=\begin{vmatrix}6&4\\1&-7\end{vmatrix}\]
  6. \[\begin{aligned}M_{12}&=6(-7)-4(1)\\&=-42-4\\&=-46\end{aligned}\]
  7. Since \(1+2=3\),
    \[\begin{aligned}A_{12}&=(-1)^{1+2}M_{12}\\&=-(-46)\\&=46\end{aligned}\]
  8. \[\boxed{M_{12}=-46,\qquad A_{12}=46}\]
  9. 3. Minor and Cofactor of \(a_{13}=5\) Delete the first row and third column:
    \[M_{13}=\begin{vmatrix}6&0\\1&5\end{vmatrix}\]
  10. \[\begin{aligned}M_{13}&=6(5)-0(1)\\&=30\end{aligned}\]
  11. Since \(1+3=4\),
    \[\begin{aligned}A_{13}&=(-1)^4(30)\\&=30\end{aligned}\]
  12. \[\boxed{M_{13}=30,\qquad A_{13}=30}\]
  13. 4. Minor and Cofactor of \(a_{21}=6\) Delete the second row and first column:
    \[M_{21}=\begin{vmatrix}-3&5\\5&-7\end{vmatrix}\]
  14. \[\begin{aligned}M_{21}&=(-3)(-7)-5(5)\\&=21-25\\&=-4\end{aligned}\]
  15. Since \(2+1=3\),
    \[\begin{aligned}A_{21}&=(-1)^3(-4)\\&=4\end{aligned}\]
  16. \[\boxed{M_{21}=-4,\quad A_{21}=4}\]
  17. 5. Minor and Cofactor of \(a_{22}=0\) Delete the second row and second column:
    \[M_{22}=\begin{vmatrix}2&5\\1&-7\end{vmatrix}\]
  18. \[\begin{aligned}M_{22}&=2(-7)-5(1)\\&=-14-5\\&=-19\end{aligned}\]
  19. Since \(2+2=4\),
    \[\begin{aligned}A_{22}&=(-1)^4(-19)\\&=-19\end{aligned}\]
  20. \[\boxed{M_{22}=-19,\qquad A_{22}=-19}\]
  21. 6. Minor and Cofactor of \(a_{23}=4\) Delete the second row and third column:
    \[M_{23}=\begin{vmatrix}2&-3\\1&5\end{vmatrix}\]
  22. \[\begin{aligned}M_{23}&=2(5)-(-3)(1)\\&=10+3\\&=13\end{aligned}\]
  23. Since \(2+3=5\),
    \[\begin{aligned}A_{23}&=(-1)^5(13)\\&=-13\end{aligned}\]
  24. \[\boxed{M_{23}=13,\qquad A_{23}=-13}\]
  25. 7. Minor and Cofactor of \(a_{31}=1\) Delete the third row and first column:
    \[M_{31}=\begin{vmatrix}-3&5\\0&4\end{vmatrix}\]
  26. \[\begin{aligned}M_{31}&=(-3)(4)-5(0)\\&=-12\end{aligned}\]
  27. Since \(3+1=4\),
    \[\begin{aligned}A_{31}&=(-1)^4(-12)\\&=-12\end{aligned}\]
  28. \[\boxed{M_{31}=-12,\qquad A_{31}=-12}\]
  29. 8. Minor and Cofactor of \(a_{32}=5\) Delete the third row and second column:
    \[M_{32}=\begin{vmatrix}2&5\\6&4\end{vmatrix}\]
  30. \[\begin{aligned}M_{32}&=2(4)-5(6)\\&=8-30\\&=-22\end{aligned}\]
  31. Since \(3+2=5\),
    \[\begin{aligned}A_{32}&=(-1)^5(-22)\\&=22\end{aligned}\]
  32. \[\boxed{M_{32}=-22,\qquad A_{32}=22}\]
  33. 9. Minor and Cofactor of \(a_{33}=-7\) Delete the third row and third column:
    \[M_{33}=\begin{vmatrix}2&-3\\6&0\end{vmatrix}\]
  34. \[\begin{aligned}M_{33}&=2(0)-(-3)(6)\\&=18\end{aligned}\]
  35. Since \(3+3=6\),
    \[\begin{aligned}A_{33}&=(-1)^6(18)\\&=18\end{aligned}\]
  36. \[\boxed{M_{33}=18,\quad A_{33}=18}\]
  37. Complete Matrix of Minors Arranging all the minors in their corresponding positions gives
    \[[M_{ij}]=\begin{bmatrix}-20&-46&30\\-4&-19&13\\-12&-22&18\end{bmatrix}\]
  38. Complete Matrix of Cofactors Applying the alternating sign pattern
    \[\begin{bmatrix}+&-&+\\-&+&-\\+&-&+\end{bmatrix}\]
    gives
    \[[A_{ij}]=\begin{bmatrix}-20&46&30\\4&-19&-13\\-12&22&18\end{bmatrix}\]
Part (b)
Step 2: Verification of the Given Identity
  1. We have to verify
    \[a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33}=0\]
  2. From the determinant,
    \[a_{11}=2,\qquad a_{12}=-3,\qquad a_{13}=5\]
  3. From the matrix of cofactors,
    \[A_{31}=-12,\qquad A_{32}=22,\qquad A_{33}=18\]
  4. Therefore,
    \[\begin{aligned}a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33}&=2(-12)+(-3)(22)+5(18)\\&=-24-66+90\\&=-90+90\\&=0\end{aligned}\]
  5. Hence,
    \[\boxed{a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33}=0}\]
🤔 Did You Know?
Why Does the Sum Become Zero?
The result is not a coincidence. It follows from an important property of determinants:

The sum of the products of the elements of one row (or column) with the corresponding cofactors of a different row (or column) is zero.

In this example,
\[ a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33} \]
uses the elements of the first row but the cofactors of the third row. Since the rows are different, the sum is zero.

More generally, for \(i\ne k\),
\[ \boxed{ a_{i1}A_{k1}+a_{i2}A_{k2}+\cdots+a_{in}A_{kn}=0 }. \]
Similarly, for different columns \(j\ne k\),
\[ \boxed{ a_{1j}A_{1k}+a_{2j}A_{2k}+\cdots+a_{nj}A_{nk}=0 }. \]
📐 Derivation of the Property
Consider the expression
\[a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33}\]
The cofactors \(A_{31},A_{32},A_{33}\) are obtained from the third row. Therefore, this expression can be interpreted as the determinant obtained by replacing the third row of the original determinant by the first row:
\[\begin{vmatrix}a_{11}&a_{12}&a_{13}\\a_{21}&a_{22}&a_{23}\\a_{11}&a_{12}&a_{13}\end{vmatrix}\]
This determinant has two identical rows. A determinant with two identical rows is zero. Hence,
\[\boxed{a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33}=0}\]
This provides a conceptual explanation for the identity rather than treating it merely as a numerical verification.
🗒️ Alternative Direct Verification
Using the calculated cofactors,
\[\begin{aligned}A_{31}&=(-1)^4\begin{vmatrix}-3&5\\0&4\end{vmatrix}=-12,\\A_{32}&=(-1)^5 \begin{vmatrix}2&5\\6&4\end{vmatrix}=22,\\A_{33}&=(-1)^6 \begin{vmatrix}2&-3\\6&0\end{vmatrix}=18 \end{aligned}\]
Hence,
\[\begin{aligned}2(-12)+(-3)(22)+5(18)&=-24-66+90\\&=0\end{aligned}\]
⚡ Exam Tip
❌ Common Mistakes
  • Writing \(A_{32}=-22\) instead of \(22\). Since \(3+2=5\), the cofactor sign is negative, and \(M_{32}=-22\), so \(A_{32}=22\).
  • Forgetting that \(A_{23}=-M_{23}\) because \(2+3\) is odd.
  • Using the wrong \(2\times2\) subdeterminant when finding a minor.
  • Confusing the matrix of minors with the matrix of cofactors.
  • Assuming that a product such as \(a_{13}A_{33}\) must have the same sign as \(a_{13}\). The sign of a cofactor is determined by its position and the value of its minor.

Adjoint and Inverse of a Matrix

🗺️ Overview
The concepts of the adjoint and inverse of a matrix are fundamental applications of determinants, minors and cofactors. They are especially important in Class 12 Mathematics because they connect determinant properties with matrix inversion and the solution of simultaneous linear equations.

For a square matrix \(A\), the adjoint is constructed from its cofactors. If
\[ |A|\ne0, \]
the adjoint provides a direct formula for the inverse:
\[ \boxed{A^{-1}=\frac{1}{|A|}\operatorname{adj}A}. \]
📘 Definition of Adjoint of a Matrix
🗒️ Important Terminology
  • \(M_{ij}\) denotes the minor of \(a_{ij}\).
  • \(A_{ij}\) denotes the cofactor of \(a_{ij}\).
  • \([A_{ij}]\) is the matrix of cofactors.
  • \(\operatorname{adj}A\) is the transpose of the matrix of cofactors.
The cofactor is given by
\[ \boxed{A_{ij}=(-1)^{i+j}M_{ij}}. \]
📌 Adjoint of a \(3\times3\) Matrix
🧰 How to Find the Adjoint of a Matrix
To find \(\operatorname{adj}A\), follow this systematic procedure:
Find the minor \(M_{ij}\) of every element \(a_{ij}\).
Find the corresponding cofactor using \(A_{ij}=(-1)^{i+j}M_{ij}\).
Arrange all cofactors in their original positions to form the cofactor matrix.
Transpose the cofactor matrix.
The resulting matrix is \(\operatorname{adj}A\).
In symbolic form:
\[\boxed{A\longrightarrow M_{ij}]\longrightarrow [A_{ij}]\longrightarrow [A_{ij}]^T=\operatorname{adj}A}\]
📎 Cofactor Sign Pattern
For a \(3\times3\) matrix, the cofactor signs follow the alternating pattern
\[\boxed{\begin{bmatrix}+&-&+\\-&+&-\\+&-&+\end{bmatrix}}\]
Therefore,
\[\begin{aligned}A_{11}&=+M_{11},&A_{12}&=-M_{12},&A_{13}&=+M_{13},\\A_{21}&=-M_{21},&A_{22}&=+M_{22},&A_{23}&=-M_{23},\\A_{31}&=+M_{31},&A_{32}&=-M_{32},&A_{33}&=+M_{33}\end{aligned}\]
🔢 Formula for the Adjoint of a \(3\times3\) Matrix
📐 Derivation
Derivation of the Adjoint Formula

Let

\[C=[A_{ij}]\]
be the cofactor matrix of \(A\).

By definition,

\[\operatorname{adj}A=C^T\]

Since

\[C=\begin{bmatrix}A_{11}&A_{12}&A_{13}\\A_{21}&A_{22}&A_{23}\\A_{31}&A_{32}&A_{33}\end{bmatrix},\]
taking its transpose gives
\[C^T=\begin{bmatrix}A_{11}&A_{21}&A_{31}\\A_{12}&A_{22}&A_{32}\\A_{13}&A_{23}&A_{33}\end{bmatrix}\]

Therefore,

\[\operatorname{adj}A=\begin{bmatrix}A_{11}&A_{21}&A_{31}\\A_{12}&A_{22}&A_{32}\\A_{13}&A_{23}&A_{33}\end{bmatrix}\]

🗒️ Fundamental Property Of Adjoint
A crucial identity connecting a matrix with its adjoint is
\[\boxed{A(\operatorname{adj}A)=(\operatorname{adj}A)A=|A|I}\]
Here, \(I\) denotes the identity matrix of the same order as \(A\). This identity is the theoretical foundation of the formula for the inverse of a matrix.
🤔  Why Does \(A(\operatorname{adj}A)=|A|I\)?
Let
\[ B=\operatorname{adj}A. \]
Since
\[ B= [A_{ji}], \]
the \((i,j)\)-th entry of \(AB\) is
\[ \sum_{k=1}^{n}a_{ik}A_{jk}. \]
If \(i=j\), this becomes
\[ \sum_{k=1}^{n}a_{ik}A_{ik}, \]
which is the cofactor expansion of \(|A|\) along the \(i\)-th row. Therefore,
\[ \sum_{k=1}^{n}a_{ik}A_{ik}=|A|. \]
If \(i\ne j\), the sum
\[ \sum_{k=1}^{n}a_{ik}A_{jk} \]
is zero because it is the sum of the elements of one row multiplied by the corresponding cofactors of a different row.

Hence, the diagonal entries of \(AB\) are \(|A|\), while all off-diagonal entries are zero. Thus,
\[ \boxed{ A(\operatorname{adj}A)=|A|I }. \]
📘 Inverse of a Matrix
🧭 Condition
Condition for the Existence of an Inverse
🧭
Condition for the Existence of an Inverse A square matrix \(A\) is invertible if and only if
\[\boxed{|A|\ne0}\]
Therefore:
  • If \(|A|\ne0\), then \(A\) is non-singular and \(A^{-1}\) exists.
  • If \(|A|=0\), then \(A\) is singular and \(A^{-1}\) does not exist.
📐 Derivation
Derivation of the Inverse Formula
We know that
\[A(\operatorname{adj}A)=|A|I\]
If
\[|A|\ne0,\]
divide both sides by \(|A|\):
\[A\left(\frac{1}{|A|}\operatorname{adj}A\right)=I.\]
Similarly,
\[\left(\frac{1}{|A|}\operatorname{adj}A\right)A=I.\]
Therefore,
\[\boxed{A^{-1}=\frac{1}{|A|}\operatorname{adj}A},\quad |A|\ne0.\]
This is known as the adjoint method for finding the inverse of a matrix.
🧰 Complete Procedure for Finding \(A^{-1}\)
For a square matrix \(A\), use the following roadmap:
  1. 1

    Calculate \(|A|\).

  2. 2

    Check whether \(|A|\ne0\).

  3. 3

    Find all minors \(M_{ij}\).

  4. 4

    Find all cofactors \(A_{ij}=(-1)^{i+j}M_{ij}\).

  5. 5

    Form the cofactor matrix \([A_{ij}]\).

  6. 6

    Transpose the cofactor matrix to obtain \(\operatorname{adj}A\).

  7. 7

    Use

    \[ A^{-1}=\frac{1}{|A|}\operatorname{adj}A. \]

✏️ Example
1
Question
Find the adjoint and inverse of
\[A=\begin{bmatrix}a&b\\c&d\end{bmatrix},\]
assuming \(ad-bc\ne0\).
🗺️ Roadmap
  1. Find the cofactors of all four elements.

  2. Construct the cofactor matrix.

  3. Transpose it to obtain \(\operatorname{adj}A\).

  4. Calculate \(|A|\).

  5. Apply the inverse formula.

🧩 Solution
Cofactors
  1. Step 1: Cofactors for
    \[A=\begin{bmatrix}a&b\\c&d\end{bmatrix},\]
  2. the minors are
    \[M_{11}=d,\qquad M_{12}=c,\quad M_{21}=b,\qquad M_{22}=a\]
  3. Applying the cofactor signs
    \[\begin{bmatrix}+&-\\-&+\end{bmatrix},\]
  4. we obtain
    \[A_{11}=d,\quad A_{12}=-c,\quad A_{21}=-b,\quad A_{22}=a\]
  5. Hence, the cofactor matrix is
    \[C=\begin{bmatrix}d&-c\\-b&a\end{bmatrix}\]
Adjoint
  1. Taking the transpose,
    \[\begin{aligned}\operatorname{adj}A&=C^T\\&=\begin{bmatrix}d&-b\\-c&a\end{bmatrix}\end{aligned}\]
  2. Therefore,
    \[\operatorname{adj}\begin{bmatrix}a&b\\c&d\end{bmatrix}=\begin{bmatrix}d&-b\\-c&a\end{bmatrix}\]
Determinant
  1. \[|A|=\begin{vmatrix}a&b\\c&d\end{vmatrix}=ad-bc\]
  2. Since \(ad-bc\ne0\)
    \[A^{-1}=\frac{1}{ad-bc}\begin{bmatrix}d&-b\\-c&a\end{bmatrix}\]
  3. Hence,
    \[A^{-1}=\frac{1}{ad-bc}\begin{bmatrix}d&-b\\-c&a\end{bmatrix}\]
✏️ Example
2
Question
Find the adjoint and inverse of
\[A=\begin{bmatrix}2&3\\1&4\end{bmatrix}\]
Determinant
  1. \[\begin{aligned}|A|&=2(4)-3(1)\\&=8-3\\&=5.\end{aligned}\]
  2. Since
    \[|A|=5\ne0,\]
    the matrix is non-singular and its inverse exists.
Adjoint
  1. Using the \(2\times2\) formula,
    \[\operatorname{adj}A=\begin{bmatrix}4&-3\\-1&2\end{bmatrix}\]
Inverse
  1. \[\begin{aligned}A^{-1}&=\frac{1}{|A|}\operatorname{adj}A\\&=\frac{1}{5}\begin{bmatrix}4&-3\\-1&2\end{bmatrix}\end{aligned}\]
  2. Therefore,
    \[A^{-1}=\begin{bmatrix}\frac45&-\frac35\\-\frac15&\frac25\end{bmatrix}\]
🗒️ Singular And Non Singular Matrices

The determinant provides an immediate test for invertibility.

Condition Type of Matrix Inverse
\(|A|\ne0\) Non-singular Exists
\(|A|=0\) Singular Does not exist
🔢 Important Formulae
🏷️ Important Properties of the Adjoint
  • \(\operatorname{adj}A\) is defined for every square matrix \(A\), even when \(|A|=0\).
  • The inverse \(A^{-1}\) exists only when \(|A|\ne0\).
  • \[ A(\operatorname{adj}A)=|A|I. \]
  • \[ (\operatorname{adj}A)A=|A|I. \]
  • If \(A\) is non-singular, then
    \[ A^{-1}=\frac{\operatorname{adj}A}{|A|}. \]
  • For a \(2\times2\) matrix,
    \[ \operatorname{adj} \begin{bmatrix} a&b\\ c&d \end{bmatrix} = \begin{bmatrix} d&-b\\ -c&a \end{bmatrix} \]
⚡ Exam Tip
❌ Common Mistakes
  • Writing the cofactor matrix itself as \(\operatorname{adj}A\) without transposing it.
  • Forgetting the negative signs in the cofactor pattern.
  • Using
    \[ A^{-1}=|A|\operatorname{adj}A \]
    instead of
    \[ A^{-1}=\frac{1}{|A|}\operatorname{adj}A. \]
  • Attempting to find an inverse when \(|A|=0\).
  • Confusing \(A^{-1}\) with the reciprocal of every entry of \(A\). Matrix inversion is not performed element-by-element.
  • Confusing the notation \(A_{ij}\) for a cofactor with \(A^{-1}\), which denotes the inverse matrix.
📋 CBSE Case Study / HOTS Insight

Suppose \(A\) is a square matrix satisfying

\[ A(\operatorname{adj}A)=5I. \]
If \(A\) is non-singular, compare this relation with
\[ A(\operatorname{adj}A)=|A|I. \]

Therefore,

\[ |A|=5. \]

Since \(|A|\ne0\), \(A^{-1}\) exists and

\[ A^{-1} = \frac{1}{5}\operatorname{adj}A. \]

Hence,

\[ \boxed{|A|=5,\qquad A^{-1}=\frac15\operatorname{adj}A}. \]

This type of reasoning is useful in higher-order determinant and matrix questions where calculating every entry directly may be unnecessary.

📝 Conceptual Summary
🗒️ Quck Revision

For a square matrix \(A\):

\[ \boxed{A_{ij}=(-1)^{i+j}M_{ij}} \]

\[ \boxed{\operatorname{adj}A=[A_{ij}]^T} \]

\[ \boxed{A(\operatorname{adj}A)=|A|I} \]

\[ \boxed{A^{-1}=\frac{1}{|A|}\operatorname{adj}A,\quad |A|\ne0} \]

Thus,

\[\boxed{|A|\ne0\Longleftrightarrow A\text{ is non-singular}\Longleftrightarrow A^{-1}\text{ exists}}\]

Example 10

❓ Question
Find \(\operatorname{adj}A\), where
\[A=\begin{bmatrix}2&3\\1&4\end{bmatrix}\]
💡 Concept
🗺️ Roadmap
  1. Find the minor of each element.

  2. Apply \(A_{ij}=(-1)^{i+j}M_{ij}\) to obtain the cofactors.

  3. Form the cofactor matrix.

  4. Transpose the cofactor matrix to obtain \(\operatorname{adj}A\).

🧩 Solution
Given:
\[A=\begin{bmatrix}2&3\\1&4\end{bmatrix}\]
Find the Cofactors
  1. The cofactor \(A_{11}\) is
    \[\begin{aligned}A_{11}&=(-1)^{1+1}\begin{vmatrix}4\end{vmatrix}\\&=4\end{aligned}\]
  2. The cofactor \(A_{12}\) is
    \[\begin{aligned}A_{12}&=(-1)^{1+2}\begin{vmatrix}1\end{vmatrix}\\&=-1\end{aligned}\]
  3. The cofactor \(A_{21}\) is
    \[\begin{aligned}A_{21}&=(-1)^{2+1}\begin{vmatrix}3\end{vmatrix}\\&=-3\end{aligned}\]
  4. Note that
    \[(-1)^{2+1}=(-1)^3=-1,\]
    📝 not \((-1)^{-3}\). The exponent in the cofactor formula is always \(i+j\).
  5. Finally,
    \[\begin{aligned}A_{22}&=(-1)^{2+2}\begin{vmatrix}2\end{vmatrix}\\&=2\end{aligned}\]
Form the Cofactor Matrix
  1. Therefore,
    \[[A_{ij}]=\begin{bmatrix}A_{11}&A_{12}\\A_{21}&A_{22}\end{bmatrix}=\begin{bmatrix}4&-1\\-3&2\end{bmatrix}\]
Transpose the Cofactor Matrix
  1. By definition,
    \[\begin{aligned}\operatorname{adj}A&=[A_{ij}]^T\\&=\begin{bmatrix}4&-1\\-3&2\end{bmatrix}^{T}\\&=\begin{bmatrix}4&-3\\-1&2\end{bmatrix}\end{aligned}\]
  2. Hence,
    \[\operatorname{adj}A=\begin{bmatrix}4&-3\\-1&2\end{bmatrix}\]
👁️ Important Observation
⚡ Exam Tip

Theorem 1

🧮 Theorem
🧮 Theorem
Statement
If \(A\) is any square matrix of order \(n\), then
\[\boxed{A(\operatorname{adj}A)=(\operatorname{adj}A)A=|A|I}\]
where \(I\) is the identity matrix of order \(n\).

This is one of the most important results connecting matrices, determinants, cofactors, adjoints and inverse matrices. It is the fundamental identity used to derive the formula
\[A^{-1}=\frac{1}{|A|}\operatorname{adj}A,\qquad |A|\ne0\]
🗒️ Meaning Of The Theorem
The theorem says that when a square matrix is multiplied by its adjoint, the result is always a scalar multiple of the identity matrix. The scalar is the determinant of the original matrix.

Thus,
\[\boxed{A(\operatorname{adj}A)=|A|I}\]
and
\[\boxed{(\operatorname{adj}A)A=|A|I}\]
This identity remains valid even when \(|A|=0\). However, when \(|A|=0\), the inverse of \(A\) does not exist.
💡 Concept Behind the Theorem
🗒️ Verification For A \(3\times3\) Matrix
Let
\[A=\begin{bmatrix}a_{11}&a_{12}&a_{13}\\a_{21}&a_{22}&a_{23}\\a_{31}&a_{32}&a_{33}\end{bmatrix}\]
The adjoint of \(A\) is
\[\operatorname{adj}A=\begin{bmatrix}A_{11}&A_{21}&A_{31}\\A_{12}&A_{22}&A_{32}\\A_{13}&A_{23}&A_{33}\end{bmatrix}\]
where \(A_{ij}\) represents the cofactor of \(a_{ij}\).
Step 1: Multiply \(A\) by \(\operatorname{adj}A\)
We have
\[A(\operatorname{adj}A)=\begin{bmatrix}a_{11}&a_{12}&a_{13}\\a_{21}&a_{22}&a_{23}\\a_{31}&a_{32}&a_{33}\end{bmatrix} \begin{bmatrix}A_{11}&A_{21}&A_{31}\\A_{12}&A_{22}&A_{32}\\A_{13}&A_{23}&A_{33}\end{bmatrix}\]
Therefore,
\[ \begin{aligned} A(\operatorname{adj}A) = \begin{bmatrix} a_{11}A_{11}+a_{12}A_{12}+a_{13}A_{13} & a_{11}A_{21}+a_{12}A_{22}+a_{13}A_{23} & a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33} \\[4pt] a_{21}A_{11}+a_{22}A_{12}+a_{23}A_{13} & a_{21}A_{21}+a_{22}A_{22}+a_{23}A_{23} & a_{21}A_{31}+a_{22}A_{32}+a_{23}A_{33} \\[4pt] a_{31}A_{11}+a_{32}A_{12}+a_{33}A_{13} & a_{31}A_{21}+a_{32}A_{22}+a_{33}A_{23} & a_{31}A_{31}+a_{32}A_{32}+a_{33}A_{33} \end{bmatrix} \end{aligned} \]
Step 2: Evaluate the Diagonal Entries
The first diagonal entry is
\[a_{11}A_{11}+a_{12}A_{12}+a_{13}A_{13}\]
This is the expansion of \(|A|\) along the first row. Hence,
\[a_{11}A_{11}+a_{12}A_{12}+a_{13}A_{13}=|A|\]
Similarly,
\[ a_{21}A_{21}+a_{22}A_{22}+a_{23}A_{23}=|A| \]
and
\[ a_{31}A_{31}+a_{32}A_{32}+a_{33}A_{33}=|A|. \]
Therefore, every diagonal entry of \(A(\operatorname{adj}A)\) is \(|A|\).
Step 3: Evaluate the Off-Diagonal Entries
Consider, for example,
\[a_{11}A_{21}+a_{12}A_{22}+a_{13}A_{23}\]
Here the elements of the first row are multiplied by the corresponding cofactors of the second row. Since the rows are different,
\[a_{11}A_{21}+a_{12}A_{22}+a_{13}A_{23}=0\]
Similarly, all other off-diagonal sums are zero. For example,
\[a_{11}A_{31}+a_{12}A_{32}+a_{13}A_{33}=0\]
Hence, every off-diagonal entry of \(A(\operatorname{adj}A)\) is zero.
Step 4: Write the Resulting Matrix
Consequently,
\[A(\operatorname{adj}A)=\begin{bmatrix}|A|&0&0\\0&|A|&0\\0&0&|A|\end{bmatrix}\]
Taking \(|A|\) common,
\[A(\operatorname{adj}A)=|A|\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\end{bmatrix}\]
Since
\[I=\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\end{bmatrix},\]
we obtain
\[\boxed{A(\operatorname{adj}A)=|A|I}\]
Similarly, \((\operatorname{adj}A)A=|A|I\)
By applying the same cofactor identity to the columns, or by using the corresponding determinant expansion properties, we obtain
\[\boxed{(\operatorname{adj}A)A=|A|I}\]
Therefore,
\[\boxed{A(\operatorname{adj}A)=(\operatorname{adj}A)A=|A|I}\]
🌟 Why This Theorem Is Important
📘 Singular Matrix
✏️ Example
Consider
\[A=\begin{bmatrix}2&4\\1&2\end{bmatrix}\]
Its determinant is
\[|A|=\begin{vmatrix}2&4\\1&2\end{vmatrix}=4-4=0\]
Hence,
\[\boxed{A\text{ is singular}}\]
Consequently, \(A^{-1}\) does not exist.
📘 Non-Singular Matrix
✏️ Example of a Non-Singular Matrix
Consider
\[A=\begin{bmatrix}2&3\\1&4\end{bmatrix}\]
Its determinant is
\[|A|=\begin{vmatrix}2&3\\1&4\end{vmatrix}=8-3=5\]
Since
\[|A|=5\ne0,\]
the matrix is non-singular and its inverse exists.
⚖️ Singular vs Non-Singular Matrix
Property Singular Matrix Non-Singular Matrix
Determinant \(|A|=0\) \(|A|\ne0\)
Inverse Does not exist Exists
\(\operatorname{adj}A\) Can still be calculated Can be calculated
\(\frac{1}{|A|}\operatorname{adj}A\) Not defined Gives \(A^{-1}\)
Rank Less than the order of \(A\) Equal to the order of \(A\)
🗒️ Important Logical Equivalences

For a square matrix \(A\) of order \(n\), the following statements are equivalent:

\[ \boxed{ \begin{aligned} |A|&\ne0\\ &\Longleftrightarrow A\text{ is non-singular}\\ &\Longleftrightarrow A^{-1}\text{ exists}\\ &\Longleftrightarrow \operatorname{rank}(A)=n \end{aligned} } \]

Similarly,

\[ \boxed{ |A|=0 \Longleftrightarrow A\text{ is singular} \Longleftrightarrow A^{-1}\text{ does not exist} }. \]

⚡ Exam Tip
❌ Common Mistakes
  • Writing \((A)\) instead of \(|A|\) for the determinant.
  • Forgetting the identity matrix \(I\) in
    \[ A(\operatorname{adj}A)=|A|I. \]
  • Assuming that \(\operatorname{adj}A\) exists only when \(A\) is non-singular. The adjoint can exist even when \(|A|=0\).
  • Concluding that \(A^{-1}\) exists merely because \(A\) is square. A square matrix must also satisfy \(|A|\ne0\).
  • Confusing a zero determinant with a zero matrix. A singular matrix need not be the zero matrix.
📋 CBSE Case Study / HOTS

Let \(A\) be a square matrix of order \(3\) satisfying

\[ A(\operatorname{adj}A)=7I. \]
Determine whether \(A\) is singular or non-singular and state the inverse of \(A\) in terms of \(\operatorname{adj}A\).

Solution

By the theorem,

\[ A(\operatorname{adj}A)=|A|I. \]

Given

\[ A(\operatorname{adj}A)=7I. \]
Therefore,
\[ |A|I=7I. \]
Hence,
\[ |A|=7. \]

Since

\[ |A|=7\ne0, \]
\(A\) is non-singular and \(A^{-1}\) exists.

Using

\[ A^{-1}=\frac{1}{|A|}\operatorname{adj}A, \]
we get
\[ \boxed{ A^{-1}=\frac17\operatorname{adj}A }. \]

⚡ Quick Revision

The complete concept can be summarised as

\[\boxed{A(\operatorname{adj}A)=(\operatorname{adj}A)A=|A|I}\]

If

\[ |A|=0, \]
then
\[ \boxed{A\text{ is singular and }A^{-1}\text{ does not exist}.} \]

If

\[ |A|\ne0, \]
then
\[ \boxed{ A\text{ is non-singular and } A^{-1}=\frac{1}{|A|}\operatorname{adj}A }. \]

Therefore, the most important chain to remember for board examinations and competitive entrance tests is

\[\boxed{A(\operatorname{adj}A)=|A|I\quad\Longrightarrow\quad A^{-1}=\frac{\operatorname{adj}A}{|A|}\quad(|A|\ne0)}\]

Theorem 2

🧮 Theorem
🧮 Theorem
Statement
If \(A\) and \(B\) are non-singular matrices of the same order, then both \(AB\) and \(BA\) are also non-singular matrices of the same order.

If \(A\) and \(B\) are square matrices of order \(n\), then
\[ |A|\ne0,\qquad |B|\ne0. \]
Consequently,
\[ \boxed{AB\text{ and }BA\text{ are non-singular}.} \]
Proof
  1. Since \(A\) and \(B\) are non-singular,
    \[|A|\ne0\quad\text{and}\quad |B|\ne0\]
  2. By the determinant product theorem,
    \[|AB|=|A||B|\]
  3. Since both \(|A|\) and \(|B|\) are non-zero,
    \[|A||B|\ne0\]
  4. Hence,
    \[|AB|\ne0\]
  5. Therefore,
    \[\boxed{AB\text{ is non-singular}}\]
  6. Similarly,
    \[|BA|=|B||A|\]
  7. Since
    \[|B||A|\ne0,\]
  8. we have
    \[|BA|\ne0\]
  9. Hence,
    \[\boxed{BA\text{ is non-singular}}\]
  10. Therefore,
    \[\boxed{A,B\text{ non-singular}\Longrightarrow AB,BA\text{ non-singular}}\]
🌟 Important Consequence
✏️ Example
1
question
If
\[A=\begin{bmatrix}2&1\\1&1\end{bmatrix},\qquad B=\begin{bmatrix}1&2\\0&1\end{bmatrix}\]
show that \(AB\) is non-singular.
Part (a)
  1. First,
    \[|A|=\begin{vmatrix}2&1\\1&1\end{vmatrix}=2-1=1\ne0\]
    Therefore, \(A\) is non-singular.
  2. Also,
    \[|B|=\begin{vmatrix}1&2\\0&1\end{vmatrix}=1\ne0\]
    Therefore, \(B\) is non-singular.
  3. Hence,
    \[|AB|=|A||B|=1\times1=1\ne0.\]
    Therefore,
    \[\boxed{AB\text{ is non-singular}.}\]
⚡ Exam Tip

Theorem 3

🧮 Theorem
🧮 Theorem
Statement
If \(A\) and \(B\) are square matrices of the same order, then
\[\boxed{|AB|=|A||B|}\]
In words: the determinant of the product of two square matrices is equal to the product of their determinants.
🧭 Conditions
🧭
Condition
  • \(A\) and \(B\) must be square matrices.
  • They must be of the same order so that \(AB\) is defined.
  • The result is valid even when one or both determinants are zero.
Thus, unlike matrix multiplication itself, determinants satisfy a multiplicative property:
\[ \boxed{\det(AB)=\det(A)\det(B)}. \]
🗒️ Important Clarification
Determinants are multiplicative, but matrices are generally not commutative. In general,
\[ AB\ne BA. \]
However,
\[ |AB|=|A||B| \]
and
\[ |BA|=|B||A|. \]
Since multiplication of numbers is commutative,
\[ |A||B|=|B||A|, \]
and therefore
\[ \boxed{|AB|=|BA|}. \]
This does not imply
\[ AB=BA. \]
It only implies that the two products have the same determinant.
🌟 Useful Extensions of Theorem 3
For three square matrices \(A,B,C\) of the same order,
\[ \boxed{|ABC|=|A||B||C|}. \]
More generally, for \(n\) square matrices,
\[ \boxed{ |A_1A_2\cdots A_k| = |A_1||A_2|\cdots|A_k| }. \]
For a scalar \(k\) and an \(n\times n\) matrix \(A\),
\[ \boxed{|kA|=k^n|A|}. \]
This last formula is particularly important in objective questions.
✏️ Example
1
Question
If \(A\) and \(B\) are \(3\times3\) matrices such that
\[ |A|=4,\qquad |B|=-2, \]
find \(|AB|\).
  1. Using
    \[|AB|=|A||B|,\]
  2. we get
    \[|AB|=4(-2)=-8\]
  3. Hence,
    \[\boxed{|AB|=-8}\]
2
Question
If \(A\) and \(B\) are \(3\times3\) matrices with
\[|A|=2,\qquad |B|=3,\]
find the determinant of \(2AB\).
  1. Since \(2AB\) is a \(3\times3\) matrix,
    \[|2AB|=2^3|AB|\]
  2. Also,
    \[|AB|=|A||B|=2\times3=6\]
  3. Therefore,
    \[|2AB|=8\times6=48\]
  4. Hence,
    \boxed{|2AB|=48}
❌ Common Mistakes
Do not write
\[ |kA|=k|A|. \]
For an \(n\times n\) matrix,
\[ \boxed{|kA|=k^n|A|}. \]

Theorem 4

🧮 Theorem
🧮 Theorem
Statement
A square matrix \(A\) is invertible if and only if \(A\) is a non-singular matrix. Equivalently,
\[\boxed{A^{-1}\text{ exists}\Longleftrightarrow |A|\ne0}\]
This is an if and only if theorem, so both directions must be proved:
  1. If \(A\) is invertible, then \(A\) is non-singular.
  2. If \(A\) is non-singular, then \(A\) is invertible.
Part
  1. Assume that \(A\) is invertible.
  2. There exists a square matrix $B$ of the same order such that
    \[ AB=BA=I \]
    Reason: By the definition of an inverse
  3. Taking determinants on both sides of
    \[ AB=I \]
  4. we get
    \[ |AB|=|I| \]
  5. By Theorem 3
    \[ |A||B|=|I| \]
  6. Since the determinant of an identity matrix is \(1\),
    \[ |A||B|=1 \]
  7. A product of two real or complex numbers can equal \(1\) only if neither factor is zero. Hence,
    \[ |A|\ne0 \]
  8. A product of two real or complex numbers can equal \(1\) only if neither factor is zero. Hence,
    \[ |A|\ne0 \]
  9. Therefore, \(A\) is non-singular.
  10. Thus,
    \[ \boxed{A\text{ invertible}\Longrightarrow A\text{ non-singular}} \]
Part
  1. Conversely, suppose that \(A\) is non-singular.
  2. By definition,
    \[ |A|\ne0 \]
  3. From Theorem 1,
    \[ A(\operatorname{adj}A)=(\operatorname{adj}A)A=|A|I. \]
  4. Since \(|A|\ne0\), we can divide by \(|A|\):
    \[ A\left(\frac{1}{|A|}\operatorname{adj}A\right)=\left(\frac{1}{|A|}\operatorname{adj}A\right)A=SI \]
  5. Let
    \[ B=\frac{1}{|A|}\operatorname{adj}A \]
  6. Then
    \[ AB=BA=I \]
  7. Therefore, \(B\) is the inverse of \(A\). Hence,
    \[ \boxed{A^{-1}=\frac{1}{|A|}\operatorname{adj}A} \]
  8. Thus,
    \[ \boxed{A\text{ non-singular}\Longrightarrow A\text{ invertible}} \]
  9. Conclusion
  10. Combining both parts,
    \[ \boxed{A\text{ is invertible}\Longleftrightarrow A\text{ is non-singular}\Longleftrightarrow |A|\ne0} \]
📐 Derivation of the Inverse Formula
Theorem 4 gives the standard adjoint formula for the inverse of a square matrix:
\[\boxed{A^{-1}=\frac{\operatorname{adj}A}{|A|}},\quad |A|\ne0\]
The derivation follows directly from
\[A(\operatorname{adj}A)=|A|I\]
Dividing by \(|A|\),
\[A\left(\frac{\operatorname{adj}A}{|A|}\right)=I\]
Therefore,
\[\boxed{A^{-1}=\frac{\operatorname{adj}A}{|A|}}\]
✏️ Example
1
Question
Find \(A^{-1}\), where
\[A=\begin{bmatrix}2&3\\1&4\end{bmatrix}\]
Part (a)
  1. First calculate the determinant:
    \[|A|=\begin{vmatrix}2&3\\1&4\end{vmatrix}=8-3=5\]
  2. Since
    \[|A|=5\ne0,\]
    \(A\) is non-singular and therefore invertible.
  3. For
    \[A=\begin{bmatrix}a&b\\c&d\end{bmatrix},\]
    \[\operatorname{adj}A=\begin{bmatrix}d&-b\\-c&a\end{bmatrix}\]
  4. Hence,
    \[\operatorname{adj}A=\begin{bmatrix}4&-3\\-1&2\end{bmatrix}\]
  5. Therefore,
    \[\begin{aligned}A^{-1}&=\frac{1}{|A|}\operatorname{adj}A\\&=\frac15\begin{bmatrix}4&-3\\-1&2\end{bmatrix}\end{aligned}\]
  6. Thus,
    \[\boxed{A^{-1}=\frac15\begin{bmatrix}4&-3\\-1&2\end{bmatrix}}\]
🌟 High-Value Consequences for Board and Competitive Exams

The three theorems together generate several frequently tested results.

1. Determinant of an Inverse

If \(A\) is invertible, then

\[ AA^{-1}=I. \]
Taking determinants,
\[ |A||A^{-1}|=1. \]
Therefore,
\[ \boxed{ |A^{-1}|=\frac{1}{|A|} }. \]

2. Determinant of a Product

\[ \boxed{|AB|=|A||B|}. \]

3. Product of Non-Singular Matrices

\[ |A|\ne0,\ |B|\ne0 \Longrightarrow |AB|\ne0. \]
Therefore,
\[ \boxed{AB\text{ is non-singular}.} \]

4. Powers of a Matrix

For a square matrix \(A\),

\[ \boxed{|A^m|=|A|^m}, \]
where \(m\) is a positive integer.

In particular,

\[ |A^2|=|A|^2, \qquad |A^3|=|A|^3. \]

5. Determinant of the Identity Matrix

\[ \boxed{|I|=1}. \]

6. Determinant of a Scalar Matrix

If \(A\) is an \(n\times n\) matrix, then

\[ \boxed{|kA|=k^n|A|}. \]

❌ Common Mistakes
  • Writing
    \[ |AB|=|A|+|B| \]
    instead of
    \[ \boxed{|AB|=|A||B|}. \]
  • Assuming that \(AB=BA\) because
    \[ |AB|=|BA|. \]
    Equality of determinants does not imply equality of matrices.
  • Forgetting that Theorem 3 requires \(A\) and \(B\) to be square matrices of the same order.
  • Using
    \[ A^{-1}=\frac{\operatorname{adj}A}{|A|} \]
    when \(|A|=0\). This formula is valid only when
    \[ \boxed{|A|\ne0}. \]
  • In the proof of Theorem 4, claiming \(|A|\ne0\) merely because \(A\) is invertible without justification. The determinant equation
    \[ |A||A^{-1}|=1 \]
    provides the required proof.
  • Reversing the order in the inverse of a product. The correct result is
    \[ \boxed{(AB)^{-1}=B^{-1}A^{-1}}, \]
    not \(A^{-1}B^{-1}\).
⚡ Exam Tip
🔢 Formula
📋 Case Study
CBSE/JEE-Level Concept Check

Question: Let \(A\) and \(B\) be \(3\times3\) matrices such that

\[ |A|=-2,\qquad |B|=5. \]
Determine whether \(AB\) is singular and find
\[ |(AB)^{-1}|. \]

Solution

Using Theorem 3,

\[ |AB|=|A||B|=(-2)(5)=-10. \]

Since

\[ |AB|=-10\ne0, \]
\(AB\) is non-singular and therefore invertible.

Now,

\[ |(AB)^{-1}| = \frac{1}{|AB|} = \frac{1}{-10}. \]

Hence,

\[ \boxed{ AB\text{ is non-singular} } \]
and
\[ \boxed{ |(AB)^{-1}|=-\frac{1}{10}. } \]

This single question combines Theorem 3, the definition of a non-singular matrix, Theorem 4 and the determinant-of-inverse formula, making these results especially useful for higher-order objective and case-based questions.

Example 11

❓ Question
Let
\[A=\begin{bmatrix}1&3&3\\1&4&3\\1&3&4\end{bmatrix}\]
Verify that
\[A(\operatorname{adj}A)=|A|I\]
and hence find \(A^{-1}\)
💡 Concept
🗺️ Roadmap
  1. Find the minors and cofactors of all elements of \(A\).

  2. Construct the cofactor matrix.

  3. Transpose the cofactor matrix to obtain \(\operatorname{adj}A\).

  4. Find \(|A|\).

  5. Verify \(A(\operatorname{adj}A)=|A|I\).

  6. Use \(A^{-1}=\dfrac{1}{|A|}\operatorname{adj}A\).

🧩 Solution
Given:
\[A=\begin{bmatrix}1&3&3\\1&4&3\\1&3&4\end{bmatrix}\]
Step 1: Find the Cofactors
  1. Recall that the cofactor of \(a_{ij}\) is
    \[\boxed{A_{ij}=(-1)^{i+j}M_{ij}}\]
    📝 where \(M_{ij}\) is the minor corresponding to \(a_{ij}\).
  2. The signs of the cofactors of a \(3\times3\) determinant follow the pattern
    \[\begin{bmatrix}+&-&+\\-&+&-\\+&-&+\end{bmatrix}\]
  3. Cofactor \(A_{11}\)
    \[M_{11}=\begin{vmatrix}4&3\\3&4\end{vmatrix}=16-9=7\]
  4. Therefore,
    \[A_{11}=(-1)^{1+1}M_{11}=7\]
  5. Cofactor \(A_{12}\)
    \[M_{12}=\begin{vmatrix}1&3\\1&4\end{vmatrix}=4-3=1\]
  6. Hence,
    \[A_{12}=(-1)^{1+2}(1)=-1\]
  7. Cofactor \(A_{13}\)
    \[M_{13}=\begin{vmatrix}1&4\\1&3\end{vmatrix}=3-4=-1\]
  8. Therefore,
    \[A_{13}=(-1)^{1+3}(-1)=-1\]
  9. Cofactor \(A_{21}\)
    \[M_{21}=\begin{vmatrix}3&3\\3&4\end{vmatrix}=12-9=3\]
  10. Hence,
    \[A_{21}=(-1)^{2+1}(3)=-3\]
  11. Cofactor \(A_{22}\)
    \[M_{22}=\begin{vmatrix}1&3\\1&4\end{vmatrix}=4-3=1\]
  12. Therefore,
    \[A_{22}=(-1)^{2+2}(1)=1\]
  13. Cofactor \(A_{23}\)
    \[M_{23}=\begin{vmatrix}1&3\\1&3\end{vmatrix}=3-3=0\]
  14. Hence,
    \[A_{23}=(-1)^{2+3}(0)=0\]
  15. Cofactor \(A_{31}\)
    \[M_{31}=\begin{vmatrix}3&3\\4&3\end{vmatrix}=9-12=-3\]
  16. Therefore,
    \[A_{31}=(-1)^{3+1}(-3)=-3\]
  17. Cofactor \(A_{32}\)
    \[M_{32}=\begin{vmatrix}1&3\\1&3\end{vmatrix}=3-3=0\]
  18. Hence,
    \[A_{32}=(-1)^{3+2}(0)=0\]
  19. Cofactor \(A_{33}\)
    \[M_{33}=\begin{vmatrix}1&3\\1&4\end{vmatrix}=4-3=1\]
  20. Therefore,
    \[A_{33}=(-1)^{3+3}(1)=1\]
Step 2: Form the Cofactor Matrix
  1. Thus, the cofactor matrix is
    \[C=\begin{bmatrix}A_{11}&A_{12}&A_{13}\\A_{21}&A_{22}&A_{23}\\A_{31}&A_{32}&A_{33}\end{bmatrix}= \begin{bmatrix}7&-1&-1\\-3&1&0\\-3&0&1\end{bmatrix}\]
Step 3: Find the Adjoint
  1. The adjoint of \(A\) is the transpose of its cofactor matrix:
    \[\operatorname{adj}A=C^T\]
  2. Therefore,
    \[\operatorname{adj}A=\begin{bmatrix}7&-3&-3\\-1&1&0\\-1&0&1\end{bmatrix}\]
    .
    📝 Important: The adjoint is obtained by transposing the cofactor matrix. It is not obtained by simply transposing the original matrix
Step 4: Find the Determinant \(|A|\)
  1. Expand along the first row:
    \[\begin{aligned}|A|&=1\begin{vmatrix}4&3\\3&4\end{vmatrix}-3\begin{vmatrix}1&3\\1&4\end{vmatrix}+3 \begin{vmatrix}1&4\\1&3\end{vmatrix}\\&=1(16-9)-3(4-3)+3(3-4)\\&=7-3-3\\&=1\end{aligned}\]
  2. Hence,
    \[\boxed{|A|=1}\]
  3. Since
    \[|A|=1\ne0\]
    📝 \(A\) is non-singular and therefore invertible.
Step 5: Verify \(A(\operatorname{adj}A)=|A|I\)
  1. We have
    \[A=\begin{bmatrix}1&3&3\\1&4&3\\1&3&4\end{bmatrix}\]
    and
    \[\operatorname{adj}A=\begin{bmatrix}7&-3&-3\\-1&1&0\\-1&0&1\end{bmatrix}\]
  2. Therefore,
    \[\begin{aligned}A(\operatorname{adj}A)&=\begin{bmatrix}1&3&3\\1&4&3\\1&3&4\end{bmatrix} \begin{bmatrix}7&-3&-3\\-1&1&0\\-1&0&1\end{bmatrix}\\ &=\begin{bmatrix}7-3-3&-3+3+0&-3+0+3\\7-4-3&-3+4+0&-3+0+3\\7-3-4&-3+3+0&-3+0+4\end{bmatrix}\\ &=\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\end{bmatrix}\end{aligned}\]
  3. Since \(|A|=1,\), we have
    \[|A|I=1\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\end{bmatrix}=\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\end{bmatrix}\]
  4. Hence,
    \[\boxed{A(\operatorname{adj}A)=|A|I}\]
Step 6: Find \(A^{-1}\)
  1. The inverse formula is
    \[A^{-1}=\frac{1}{|A|}\operatorname{adj}A\]
  2. Since \(|A|=1,\) we obtain
    \[\begin{aligned}A^{-1}&=\frac{1}{1}\begin{bmatrix}7&-3&-3\\-1&1&0\\-1&0&1\end{bmatrix}\\ &=\begin{bmatrix}7&-3&-3\\-1&1&0\\-1&0&1\end{bmatrix}\end{aligned}\]
  3. Therefore,
    \[A^{-1}=\begin{bmatrix}7&-3&-3\\-1&1&0\\-1&0&1\end{bmatrix}\]
⚡ Exam Tip
❌ Common Mistakes
  • Writing the cofactor matrix itself as \(\operatorname{adj}A\) without taking its transpose.
  • Forgetting the alternating cofactor signs
    \[ \begin{bmatrix} +&-&+\\ -&+&-\\ +&-&+ \end{bmatrix} \]
  • Writing
    \[ A^{-1}=|A|\operatorname{adj}A \]
    instead of
    \[ \boxed{A^{-1}=\frac{1}{|A|}\operatorname{adj}A} \]
  • Using the inverse formula without checking that
    \[ |A|\ne0 \]
  • Confusing the notation \(A_{ij}\) for a matrix element with \(A_{ij}\) for the cofactor. In determinant theory, the same uppercase notation is conventionally used for the cofactor of \(a_{ij}\), so context is important.

Example 12

❓ Question
If
\[A=\begin{bmatrix}2&3\\1&-4\end{bmatrix}\quad\text{and}\quad B=\begin{bmatrix}1&-2\\-1&3\end{bmatrix},\]
verify that
\[\boxed{(AB)^{-1}=B^{-1}A^{-1}}\]
💡 Concept
🗺️ Roadmap
  1. Calculate \(AB\).

  2. Check that \(A\) and \(B\) are non-singular.

  3. Find \((AB)^{-1}\).

  4. Find \(A^{-1}\) and \(B^{-1}\).

  5. Calculate \(B^{-1}A^{-1}\).

  6. Compare the two results.

🧩 Solution
Given:
\[A=\begin{bmatrix}2&3\\1&-4\end{bmatrix},\quad B=\begin{bmatrix}1&-2\\-1&3\end{bmatrix},\]
Step 1: Calculate \(AB\)
  1. we have
    \[\begin{aligned}AB&= \begin{bmatrix}2&3\\1&-4\end{bmatrix} \begin{bmatrix}1&-2\\-1&3\end{bmatrix}\\[8pt] &=\begin{bmatrix}2(1)+3(-1)&2(-2)+3(3)\\1(1)+(-4)(-1)&1(-2)+(-4)(3)\end{bmatrix}\\[8pt] &=\begin{bmatrix}2-3&-4+9\\1+4&-2-12\end{bmatrix}\\[8pt] &=\begin{bmatrix}-1&5\\5&-14\end{bmatrix}\end{aligned}\]
Step 2: Check Whether the Matrices are Invertible
  1. First,
    \[|A|=\begin{vmatrix}2&3\\1&-4\end{vmatrix}=2(-4)-3(1)=-8-3=-11\]
  2. Therefore,
    \[\boxed{|A|=-11\ne0}\]
    Hence, \(A\) is non-singular and \(A^{-1}\) exists.
  3. Next,
    \[|B|=\begin{vmatrix}1&-2\\-1&3\end{vmatrix}=1(3)-(-2)(-1)=3-2=1\]
  4. Thus,
    \[\boxed{|B|=1\ne0}\]
    Hence, \(B\) is also non-singular and \(B^{-1}\) exists.
  5. We can also verify the product determinant directly:
    \[|AB|=|A||B|=(-11)(1)=-11\ne0\]
    📝 Therefore, \(AB\) is invertible.
Step 3: Find \((AB)^{-1}\)
  1. We have
    \[AB=\begin{bmatrix}-1&5\\5&-14\end{bmatrix}\]
  2. Its determinant is
    \[\begin{aligned}|AB|&=\begin{vmatrix}-1&5\\5&-14\end{vmatrix}\\&=(-1)(-14)-5(5)\\&=14-25\\&=-11\end{aligned}\]
  3. For
    \[C=\begin{bmatrix}a&b\\c&d\end{bmatrix},\]
    the inverse is
    \[C^{-1}=\frac{1}{ad-bc}\begin{bmatrix}d&-b\\-c&a\end{bmatrix}\]
  4. Therefore,
    \[\begin{aligned}(AB)^{-1}&=\frac{1}{-11}\begin{bmatrix}-14&-5\\-5&-1\end{bmatrix}\\ &=\boxed{\frac{1}{11}\begin{bmatrix}14&5\\5&1\end{bmatrix}}\end{aligned}\]
Step 4: Find \(A^{-1}\)
  1. Since \(|A|=-11,\) We have
    \[\begin{aligned}A^{-1}&=\frac{1}{|A|}\begin{bmatrix}-4&-3\\-1&2\end{bmatrix}\\&=\frac{1}{-11} \begin{bmatrix}-4&-3\\-1&2\end{bmatrix}\end{aligned}\]
  2. Hence,
    \[\boxed{A^{-1}=\frac{1}{11}\begin{bmatrix}4&3\\1&-2\end{bmatrix}}\]
Step 5: Find \(B^{-1}\)
  1. Since \(|B|=1,\)
    We obtain
  2. \[\begin{aligned}B^{-1}&=\frac{1}{1}\begin{bmatrix}3&2\\1&1\end{bmatrix}\\&=\boxed{\begin{bmatrix}3&2\\1&1\end{bmatrix}}\end{aligned}\]
Step 6: Calculate \(B^{-1}A^{-1}\)
  1. Now,
    \[\begin{aligned}B^{-1}A^{-1}&=\begin{bmatrix}3&2\\1&1\end{bmatrix}\frac{1}{11} \begin{bmatrix}4&3\\1&-2\end{bmatrix}\\&=\frac{1}{11}\begin{bmatrix}3(4)+2(1)&3(3)+2(-2)\\1(4)+1(1)&1(3)+1(-2)\end{bmatrix}\\ &=\frac{1}{11}\begin{bmatrix}12+2&9-4\\4+1&3-2\end{bmatrix}\\&=\frac{1}{11}\begin{bmatrix}14&5\\5&1\end{bmatrix}\end{aligned}\]
Comparison
  1. From Step 3,
    \[(AB)^{-1}=\frac{1}{11}\begin{bmatrix}14&5\\5&1\end{bmatrix}\]
  2. From Step 6,
    \[B^{-1}A^{-1}=\frac{1}{11}\begin{bmatrix}14&5\\5&1\end{bmatrix}\]
  3. Hence,
    \[\boxed{(AB)^{-1}=B^{-1}A^{-1}}\]
🤔 Did You Know?
Why Does the Order Reverse?
This is a fundamental property of matrix inverses. Since matrix multiplication is generally non-commutative,
\[AB\ne BA,\]
the inverse must undo the operations in reverse order.

The product \(AB\) means that \(B\) acts first and \(A\) acts second when matrices are interpreted as linear transformations. To undo \(AB\), \(A\) must therefore be undone first by \(A^{-1}\), followed by \(B^{-1}\). This gives
\[\boxed{(AB)^{-1}=B^{-1}A^{-1}}\]
❌ Common Mistakes
  • Writing \(A^{-1}B^{-1}\) instead of \(B^{-1}A^{-1}\).
  • Omitting the negative sign in the \(2\times2\) inverse formula:
    \[\begin{bmatrix}a&b\\c&d\end{bmatrix}^{-1}=\frac{1}{ad-bc}\begin{bmatrix}d&-b\\-c&a\end{bmatrix}\]
  • Dividing by \(|AB|\) without checking that \(|AB|\ne0\).
  • Assuming that \(AB=BA\) because both products may have related determinants.

Example 13

❓ Question
Show that the matrix
\[A=\begin{bmatrix}2&3\\1&2\end{bmatrix}\]
satisfies the equation
\[A^2-4A+I=O,\]
where \(I\) is the \(2\times2\) identity matrix and \(O\) is the \(2\times2\) zero matrix. Hence, using this equation, find \(A^{-1}\).
💡 Concept
🗺️ Roadmap
  1. Calculate \(A^2\).

  2. Calculate \(4A\).

  3. Use \(A^2-4A+I\) to verify the given matrix equation.

  4. Rearrange the equation to isolate \(A^{-1}\).

  5. Substitute the given matrix \(A\) and simplify.

  6. Optionally verify the resulting inverse by multiplication.

🧩 Solution
Given:
\[A=\begin{bmatrix}2&3\\1&2\end{bmatrix}\]
Step 1: Calculate \(A^2\)
  1. we have
    \[\begin{aligned}A^2&=A\cdot A\\&=\begin{bmatrix}2&3\\1&2\end{bmatrix} \begin{bmatrix}2&3\\1&2\end{bmatrix}\\&=\begin{bmatrix}2(2)+3(1)&2(3)+3(2)\\1(2)+2(1)&1(3)+2(2)\end{bmatrix}\\ &=\begin{bmatrix}4+3&6+6\\2+2&3+4\end{bmatrix}\\&=\begin{bmatrix}7&12\\4&7\end{bmatrix}\end{aligned}\]
Step 2: Calculate \(4A\)
  1. Multiplying every element of \(A\) by \(4\),
    \[\begin{aligned}4A&=4\begin{bmatrix}2&3\\1&2\end{bmatrix}\\&=\begin{bmatrix}8&12\\4&8\end{bmatrix}\end{aligned}\]
Step 3: Verify \(A^2-4A+I=O\)
  1. The \(2\times2\) identity matrix is
    \[I=\begin{bmatrix}1&0\\0&1\end{bmatrix}\]
  2. Therefore,
    \[\begin{aligned}A^2-4A+I&=\begin{bmatrix}7&12\\4&7\end{bmatrix}-\begin{bmatrix}8&12\\4&8\end{bmatrix}+\begin{bmatrix}1&0\\0&1\end{bmatrix}\\ &=\begin{bmatrix}7-8+1&12-12+0\\4-4+0&7-8+1\end{bmatrix}\\&=\begin{bmatrix}0&0\\0&0\end{bmatrix}\\&=O\end{aligned}\]
  3. Hence,
    \[\boxed{A^2-4A+I=O}\]
Step 4: Derive \(A^{-1}\) from the Given Equation
  1. We have
    \[A^2-4A+I=O.\]
  2. Therefore,
    \[A^2-4A=-I\]
  3. Taking \(A\) common from the first two terms,
    \[A(A-4I)=-I\]
  4. To derive the inverse, multiply both sides on the left by \(A^{-1}\):
    \[A^{-1}A(A-4I)=A^{-1}(-I).\]
  5. Since
    \[A^{-1}A=I\]
  6. we get
    \[A-4I=-A^{-1}\]
    📝 Hence This is the most useful result obtained directly from the given matrix equation.
Step 5: Calculate \(A^{-1}\)
  1. Since
    \[I=\begin{bmatrix}1&0\\0&1\end{bmatrix},\]
  2. we have
    \[4I=\begin{bmatrix}4&0\\0&4\end{bmatrix}\]
  3. Therefore,
    \[\begin{aligned}A^{-1}&=4I-A\\&=\begin{bmatrix}4&0\\0&4\end{bmatrix}- \begin{bmatrix}2&3\\1&2\end{bmatrix}\\&=\begin{bmatrix}4-2&0-3\\0-1&4-2\end{bmatrix}\\ &=\begin{bmatrix}2&-3\\-1&2\end{bmatrix}\end{aligned}\]
⚡ Exam Tip
❌ Common Mistakes
  • Writing \(A^2-4A-I=O\) instead of the given equation
    \[ A^2-4A+I=O. \]
  • Using a determinant sign incorrectly while independently checking the inverse.
  • Forgetting that \(4I\), not \(4\), must be used when deriving
    \[ A^{-1}=4I-A. \]
  • Writing
    \[ A^{-1}=A-4I \]
    instead of
    \[ A^{-1}=4I-A. \]
  • Treating the entries of a matrix as independent scalars while multiplying matrices.

Consistent and Inconsistent Systems of Linear Equations

🗺️ Overview
Determinants and matrices provide a systematic method for representing and solving systems of linear equations. In Class 12 Mathematics, the matrix method is particularly important because it connects the concepts of determinants, inverse matrices, adjoint matrices and systems of equations. It is also useful in competitive examinations where a system may need to be classified as having a unique solution, infinitely many solutions or no solution.
📘 Definition of a Consistent System
📘 Definition of an Inconsistent System
🗂️ Types of Solutions of a System
A system of linear equations can therefore have one of the following three possibilities:
Unique solution
exactly one solution exists.
Infinitely many solutions
more than one solution exists without bound.
No solution
the equations are incompatible.
The first two cases are consistent, whereas the third case is inconsistent.
Number of solutions Classification
Exactly one Consistent
Infinitely many Consistent
None Inconsistent
🧩 Solution of a System of Linear Equations Using the Inverse of a Matrix
Consider the system of three linear equations in three unknowns:
\[ \begin{aligned} a_1x+b_1y+c_1z&=d_1,\\ a_2x+b_2y+c_2z&=d_2,\\ a_3x+b_3y+c_3z&=d_3 \end{aligned} \]
We can represent this system compactly in matrix form as
\[\boxed{AX=B}\]
🧰 Formation of the Coefficient Matrix
The matrix containing the coefficients of the unknowns is called the coefficient matrix:
\[A=\begin{bmatrix}a_1&b_1&c_1\\a_2&b_2&c_2\\a_3&b_3&c_3\end{bmatrix}\]
The column matrix of unknowns is
\[X=\begin{bmatrix}x\\y\\z\end{bmatrix}\]
The constant matrix is
\[B=\begin{bmatrix}d_1\\d_2\\d_3\end{bmatrix}\]
Therefore,
\[\begin{aligned}AX&=\begin{bmatrix}a_1&b_1&c_1\\a_2&b_2&c_2\\a_3&b_3&c_3\end{bmatrix} \begin{bmatrix}x\\y\\z\end{bmatrix}\\ &=\begin{bmatrix}a_1x+b_1y+c_1z\\a_2x+b_2y+c_2z\\a_3x+b_3y+c_3z\end{bmatrix}\end{aligned}\]
Consequently,
\[\begin{bmatrix}a_1&b_1&c_1\\a_2&b_2&c_2\\a_3&b_3&c_3\end{bmatrix} \begin{bmatrix}x\\y\\z\end{bmatrix}= \begin{bmatrix}d_1\\d_2\\d_3\end{bmatrix}\]
or simply
\[\boxed{AX=B}\]
  • Case I: \(A\) is Non-Singular
    Suppose the coefficient matrix \(A\) is non-singular. Then
    \[|A|\ne0\]
    Therefore, \(A^{-1}\) exists.

    Starting from
    \[AX=B,\]
    multiply both sides on the left by \(A^{-1}\):
    \[A^{-1}(AX)=A^{-1}B\]
    By associativity of matrix multiplication,
    \[ (A^{-1}A)X=A^{-1}B. \]
    Since
    \[ A^{-1}A=I, \]
    we get
    \[ IX=A^{-1}B. \]
    Since \(IX=X\),
    \[ \boxed{X=A^{-1}B}. \]
    Thus, when
    \[ \boxed{|A|\ne0}, \]
    the system has a unique solution.
    Why is the Solution Unique When \(|A|\ne0\)?
    Since \(|A|\ne0\), the inverse \(A^{-1}\) exists. Hence
    \[ X=A^{-1}B \]
    is completely determined by \(A\) and \(B\).

    Therefore, there cannot be two different solution matrices \(X_1\) and \(X_2\). If both satisfied the system, then
    \[ AX_1=B,\qquad AX_2=B. \]
    Hence,
    \[ AX_1=AX_2. \]
    Multiplying by \(A^{-1}\),
    \[ X_1=X_2. \]
    Thus,
    \[ \boxed{|A|\ne0\Rightarrow\text{unique solution}}. \]
    Explicit Formula for the Solution
    Since
    \[ A^{-1}=\frac{1}{|A|}\operatorname{adj}A, \]
    the solution can also be written as
    \[ \boxed{ X=\frac{1}{|A|}(\operatorname{adj}A)B }. \]
    This formula is useful when the inverse is obtained through the adjoint method.
  • Case II: \(A\) is Singular
    Now suppose that the coefficient matrix \(A\) is singular. Then
    \[ \boxed{|A|=0}. \]
    In this situation, \(A^{-1}\) does not exist. Therefore, the formula
    \[ X=A^{-1}B \]
    cannot be used.

    A more careful analysis is required to determine whether the system is inconsistent or has infinitely many solutions.

    When \(A\) is singular, we have
    \[ |A|=0. \]
    From the fundamental identity
    \[ A(\operatorname{adj}A)=|A|I, \]
    we obtain
    \[ A(\operatorname{adj}A)=O. \]
    Multiplying the system
    \[ AX=B \]
    by \(\operatorname{adj}A\), we get
    \[ (\operatorname{adj}A)AX=(\operatorname{adj}A)B. \]
    Since
    \[ (\operatorname{adj}A)A=|A|I=O, \]
    the left-hand side becomes
    \[ OX=O. \]
    Therefore,
    \[ \boxed{(\operatorname{adj}A)B=O} \]
    is a necessary condition for consistency when \(A\) is singular. Thus, if
    \[ \boxed{(\operatorname{adj}A)B\ne O}, \]
    the system is definitely inconsistent. However, if
    \[ \boxed{(\operatorname{adj}A)B=O}, \]
    this condition alone does not always distinguish between infinitely many solutions and no solution. The original equations must be examined further, or the rank criterion may be used.
🗒️ Complete Classification Using Rank
For the general matrix equation
\[ AX=B, \]
let
\[ [A|B] \]
denote the augmented matrix. The system is:
\[ \boxed{ \begin{aligned} \operatorname{rank}(A)&=\operatorname{rank}([A|B]) &&\Rightarrow\text{consistent},\\ \operatorname{rank}(A)&<\operatorname{rank}([A|B]) &&\Rightarrow\text{inconsistent}. \end{aligned} } \]
If the system is consistent and there are \(n\) unknowns, then:
\[ \boxed{ \operatorname{rank}(A)=n \Rightarrow\text{unique solution} } \]
and
\[ \boxed{ \operatorname{rank}(A)
Condition Conclusion
\(\operatorname{rank}(A)=\operatorname{rank}([A|B])=n\) Unique solution
\(\operatorname{rank}(A)=\operatorname{rank}([A|B]) Infinitely many solutions
\(\operatorname{rank}(A)<\operatorname{rank}([A|B])\) No solution; inconsistent
📌 Determinant-Based Classification for Three Linear Equations
✏️ Example
Consider
\[\begin{aligned}x+y+z&=6,\\2x+3y+z&=11,\\x+2y+3z&=14\end{aligned}\]
Write the system as
\[AX=B,\]
where
\[A=\begin{bmatrix}1&1&1\\2&3&1\\1&2&3\end{bmatrix},\quad X=\begin{bmatrix}x\\y\\z\end{bmatrix},\quad B=\begin{bmatrix}6\\11\\14\end{bmatrix}\]
Calculate the determinant:
\[\begin{aligned}|A|&=\begin{vmatrix}1&1&1\\2&3&1\\1&2&3\end{vmatrix}\\ &=1(9-2)-1(6-1)+1(4-3)\\&=7-5+1\\&=3\end{aligned}\]
Since
\[|A|=3\ne0,\]
\(A^{-1}\) exists and the system has a unique solution.

The solution is
\[\boxed{X=A^{-1}B}\]
In practice, one may calculate \(A^{-1}\) using
\[ A^{-1}=\frac{1}{|A|}\operatorname{adj}A \]
and then multiply it by \(B\).
✏️ Example of an Inconsistent System
Consider
\[\begin{aligned}x+y&=2,\\2x+2y&=5\end{aligned}\]
The first equation gives
\[ x+y=2. \]
Multiplying it by \(2\) gives
\[ 2x+2y=4, \]
which contradicts
\[ 2x+2y=5. \]
Therefore, no values of \(x\) and \(y\) can satisfy both equations. Hence the system is
\[\boxed{\text{inconsistent}}\]
✏️ Example of a Consistent System with Infinitely Many Solutions
Consider
\[\begin{aligned}x+y&=2,\\2x+2y&=4\end{aligned}\]
The second equation is simply twice the first equation. Therefore, both equations represent the same line.

The system has infinitely many solutions:
\[ \boxed{x+y=2}. \]
For example,
\[ (x,y)=(0,2),(1,1),(2,0) \]
are all solutions.

Hence the system is
\[\boxed{\text{consistent with infinitely many solutions}}\]
🔢 Formula
⚡ Exam Tip
❌ Common Mistakes
  • Calling every system with \(|A|=0\) inconsistent. This is incorrect.
  • Forgetting that a consistent system can have infinitely many solutions.
  • Applying \(A^{-1}B\) when \(A\) is singular.
  • Confusing the coefficient matrix \(A\) with the augmented matrix
    \[ [A|B]. \]
  • Treating
    \[ (\operatorname{adj}A)B=O \]
    as sufficient by itself to conclude infinitely many solutions.
  • Forgetting that the number of unknowns is important when using the rank criterion for distinguishing a unique solution from infinitely many solutions.
📝 Summary

Example 14

❓ Question
Solve the following system of linear equations using the inverse matrix method:
\[\begin{aligned}2x+5y&=1,\\3x+2y&=7\end{aligned}\]
💡 Concept
🗺️ Roadmap
  1. Write the given equations in the form \(AX=B\).

  2. Identify the coefficient matrix \(A\), variable matrix \(X\), and constant matrix \(B\).

  3. Calculate \(|A|\).

  4. Since \(|A|\ne0\), conclude that \(A^{-1}\) exists and the solution is unique.

  5. Find \(A^{-1}\).

  6. Use \(X=A^{-1}B\).

  7. Read the values of \(x\) and \(y\) from the resulting matrix.

🧩 Solution
Part (a)
Step 1: Write the System in Matrix Form
  1. The given equations are
    \[\begin{aligned}2x+5y&=1,\\3x+2y&=7\end{aligned}\]
  2. Comparing them with
    \[a_1x+b_1y=c_1,\quad a_2x+b_2y=c_2,\]
  3. we obtain
    \[A=\begin{bmatrix}2&5\\3&2\end{bmatrix},\qquad X=\begin{bmatrix}x\\y\end{bmatrix},\qquad B=\begin{bmatrix}1\\7\end{bmatrix}\]
  4. Hence,
    \[\begin{bmatrix}2&5\\3&2\end{bmatrix}\begin{bmatrix}x\\y\end{bmatrix}=\begin{bmatrix}1\\7\end{bmatrix}\]
    or
    \[\boxed{AX=B}\]
Part (b)
Step 2: Check Whether the Inverse of \(A\) Exists
  1. For
    \[A=\begin{bmatrix}2&5\\3&2\end{bmatrix},\]
    the determinant is
    \[\begin{aligned}|A|&=\begin{vmatrix}2&5\\3&2\end{vmatrix}\\&=2(2)-5(3)\\&=4-15\\&=-11\end{aligned}\]
  2. Therefore,
    \[\boxed{|A|=-11\ne0}\]
    📝 Hence \(A\) is a non-singular matrix, so \(A^{-1}\) exists. Consequently, the system has a unique solution.
Part (c)
Step 3: Find \(A^{-1}\)
  1. For a \(2\times2\) matrix
    \[A=\begin{bmatrix}a&b\\c&d\end{bmatrix},\]
    the inverse is
    \[\boxed{A^{-1}=\frac{1}{ad-bc}\begin{bmatrix}d&-b\\-c&a\end{bmatrix}}\]
    provided [ad-bc\ne0.\]
  2. Therefore,
    \[\begin{aligned}A^{-1}&=\frac{1}{-11}\begin{bmatrix}2&-5\\-3&2\end{bmatrix}\\&=-\frac{1}{11} \begin{bmatrix}2&-5\\-3&2\end{bmatrix}\end{aligned}\]
Part (d)
Step 4: Apply \(X=A^{-1}B\)
  1. Since \(AX=B\) multiplying by \(A^{-1}\) gives
    \[X=A^{-1}B\]
  2. Thus,
    \[\begin{aligned}X&=-\frac{1}{11}\begin{bmatrix}2&-5\\-3&2\end{bmatrix}\begin{bmatrix}1\\7\end{bmatrix}\\ &=-\frac{1}{11}\begin{bmatrix}2(1)-5(7)\\-3(1)+2(7)\end{bmatrix}\\&=-\frac{1}{11}\begin{bmatrix}2-35\\-3+14\end{bmatrix}\\ &=-\frac{1}{11}\begin{bmatrix}-33\\11\end{bmatrix}\\&=\begin{bmatrix}3\\-1\end{bmatrix}\end{aligned}\]
  3. Since
    \[ X= \begin{bmatrix} x\\ y \end{bmatrix}, \]
  4. we obtain
    \[ \boxed{ x=3,\quad y=-1 }. \]
⚡ Exam Tip
❌ Common Mistakes
  • Using \(A^{-1}\) without first checking that \(|A|\ne0\).
  • Forgetting the negative signs in
    \[ A^{-1}= \frac{1}{ad-bc} \begin{bmatrix} d&-b\\ -c&a \end{bmatrix}. \]
  • Writing \(BA^{-1}\) instead of \(A^{-1}B\). The order of matrix multiplication matters.
  • Confusing the variable matrix \(X\) with the coefficient matrix \(A\).
  • Failing to verify the final values in the original equations when a quick check is possible.
🔑 Key Takeaway
· Updated
NCERT · Class XII · Mathematics · Chapter 4
Determinants

A complete interactive companion — from first principles of a 2×2 array to solving systems of equations — built for deep, unhurried understanding.

Core Concepts

Six building blocks, arranged in the order they compound on each other. Click any card to expand it.

1  What Is a Determinant?

To every square matrix \(A = [a_{ij}]\) we associate a unique real number called its determinant, written \(\det(A)\), \(|A|\), or \(\Delta\). Only square matrices have determinants — a determinant is a number extracted from a matrix, it is not the matrix itself.

\( \text{If } A = \begin{bmatrix} a & b \\ c & d \end{bmatrix} \text{ then } |A| = \begin{vmatrix} a & b \\ c & d \end{vmatrix} \)

Order 1: for \(A=[a]\), \(|A| = a\) (just the number itself).

Order 2: \(\begin{vmatrix} a & b \\ c & d\end{vmatrix} = ad - bc\) — multiply the diagonals and subtract.

Order 3 is defined by expansion along any row or column, reducing it to a combination of three order-2 determinants (see Concept 4 for the mechanics).

2  Expansion of a 3×3 Determinant

Expanding along Row 1 of \(A=\begin{vmatrix}a_1&b_1&c_1\\a_2&b_2&c_2\\a_3&b_3&c_3\end{vmatrix}\):

\( |A| = a_1\begin{vmatrix}b_2&c_2\\b_3&c_3\end{vmatrix} - b_1\begin{vmatrix}a_2&c_2\\a_3&c_3\end{vmatrix} + c_1\begin{vmatrix}a_2&b_2\\a_3&b_3\end{vmatrix} \)

Each term is: (element) × (2×2 determinant obtained by deleting that element's row and column), with alternating signs \(+,-,+\). The remarkable fact NCERT asks you to verify is that expanding along any row or column — not just Row 1 — gives the exact same numeric value. Choosing the row/column with the most zeros makes the arithmetic much lighter.

3  Properties of Determinants

These properties let you simplify a determinant before expanding — often turning a messy calculation into a one-line answer.

P1 (Transpose): \(|A| = |A^{T}|\) — interchanging all rows and columns leaves the value unchanged.

P2 (Row/column swap): Interchanging any two rows (or columns) multiplies the determinant by \(-1\).

P3 (Repetition): If any two rows (or columns) are identical, \(|A| = 0\).

P4 (Scalar multiple): If every element of one row is multiplied by \(k\), the determinant is multiplied by \(k\). Consequently \(|kA| = k^{n}|A|\) for an \(n \times n\) matrix.

P5 (Sum splitting): If elements of a row are written as a sum, e.g. \(a_1+x_1,\,a_2+x_2,\,a_3+x_3\), the determinant splits into a sum of two determinants.

P6 (Invariance under row operations): \(|A|\) is unchanged by \(R_i \to R_i + kR_j\) (adding a multiple of one row/column to another). This is the single most useful property for hand computation — it lets you create zeros deliberately.

P7 (Triangular determinant): If all entries above or below the leading diagonal are zero, \(|A|\) equals the product of the diagonal entries.

4  Area of a Triangle & Collinearity

Given vertices \((x_1,y_1),(x_2,y_2),(x_3,y_3)\), the area is packaged neatly as a determinant:

\( \text{Area} = \dfrac{1}{2}\begin{vmatrix} x_1 & y_1 & 1 \\ x_2 & y_2 & 1 \\ x_3 & y_3 & 1 \end{vmatrix} \)

Since area cannot be negative, take the absolute value of the determinant before halving. This single idea instantly gives the collinearity test: three points are collinear exactly when this determinant equals zero (the "triangle" has zero area).

5  Minors, Cofactors & the Adjoint

Minor \(M_{ij}\): the determinant left after deleting row \(i\) and column \(j\) from \(A\).

Cofactor \(C_{ij} = (-1)^{i+j}M_{ij}\) — the minor with a sign attached based on position (a checkerboard of \(+\) and \(-\)).

A subtle but exam-favourite identity: if you multiply the elements of one row by the cofactors of a different row, the sum is always zero (they belong to different expansions).

Adjoint \(\operatorname{adj}(A)\): the transpose of the matrix of cofactors.

\( \operatorname{adj}(A) = \begin{bmatrix} C_{11} & C_{21} & C_{31} \\ C_{12} & C_{22} & C_{32} \\ C_{13} & C_{23} & C_{33} \end{bmatrix} \)

Notice the transpose — cofactors of row 1 become column 1 of the adjoint. This step is the one students skip most often; skipping it is the single biggest source of wrong inverses.

6  Inverse of a Matrix & Solving Linear Systems

A square matrix \(A\) is invertible (non-singular) if and only if \(|A| \neq 0\). When it is:

\( A^{-1} = \dfrac{1}{|A|}\,\operatorname{adj}(A) \)

This inverse is exactly what solves a linear system written as \(AX = B\): pre-multiplying both sides by \(A^{-1}\) gives \(X = A^{-1}B\), i.e. the matrix method.

Consistency check before solving:

• \(|A| \neq 0\) → unique solution (system is consistent).
• \(|A| = 0\) and \((\operatorname{adj} A)B = 0\) → infinitely many solutions or no solution (needs further check).
• \(|A| = 0\) and \((\operatorname{adj} A)B \neq 0\) → no solution (inconsistent).

Equivalently, Cramer's Rule for two/three variables solves each unknown as a ratio of determinants — see the AI Solver tab for a worked, adjustable version.

AI Step-by-Step Solver

A fully rule-based engine — pick a task, enter your own numbers, and get a complete worked solution. Runs entirely in your browser; nothing is sent anywhere.

Expansion row/column:

Enter \((x_1,y_1)\), \((x_2,y_2)\), \((x_3,y_3)\) — top row to bottom row.

System: \(a_1x+b_1y+c_1z=d_1\), \(a_2x+b_2y+c_2z=d_2\), \(a_3x+b_3y+c_3z=d_3\)

Formula Reference

Every result from this chapter, in one scannable grid.

Order 2
\(\begin{vmatrix}a&b\\c&d\end{vmatrix}=ad-bc\)
Order 3 (Row 1 expansion)
\(a_1\begin{vmatrix}b_2&c_2\\b_3&c_3\end{vmatrix}-b_1\begin{vmatrix}a_2&c_2\\a_3&c_3\end{vmatrix}+c_1\begin{vmatrix}a_2&b_2\\a_3&b_3\end{vmatrix}\)
Scalar multiple
\(|kA| = k^{n}|A|,\ \ n = \text{order of }A\)
Area of triangle
\(\Delta=\dfrac{1}{2}\begin{vmatrix}x_1&y_1&1\\x_2&y_2&1\\x_3&y_3&1\end{vmatrix}\)

Take the absolute value; equals zero ⟺ points are collinear.

Cofactor
\(C_{ij}=(-1)^{i+j}M_{ij}\)
Determinant via cofactors of any row
\(|A| = a_{i1}C_{i1}+a_{i2}C_{i2}+a_{i3}C_{i3}\)
Sum of products with wrong row's cofactors
\(a_{i1}C_{j1}+a_{i2}C_{j2}+a_{i3}C_{j3}=0\ \ (i\neq j)\)
Adjoint
\(\operatorname{adj}(A) = [C_{ij}]^{T}\)
A · adj(A)
\(A\cdot\operatorname{adj}(A)=\operatorname{adj}(A)\cdot A=|A|\,I\)
Inverse
\(A^{-1}=\dfrac{1}{|A|}\operatorname{adj}(A),\ \ |A|\neq0\)
Determinant of the inverse
\(|A^{-1}| = \dfrac{1}{|A|}\)
Product rule
\(|AB| = |A|\,|B|\)
Matrix method (linear system)
\(AX=B \implies X=A^{-1}B\)
Cramer's Rule (3 variables)
\(x=\dfrac{\Delta_1}{\Delta},\ y=\dfrac{\Delta_2}{\Delta},\ z=\dfrac{\Delta_3}{\Delta}\)

\(\Delta_1,\Delta_2,\Delta_3\) replace column 1, 2, 3 respectively of the coefficient determinant \(\Delta\) with the constants column.

Ticks & Tips

Small habits that consistently save time and marks in this chapter.

✦ Hunt for zeros first

Before expanding a 3×3 determinant, use \(R_i \to R_i + kR_j\) to create at least one zero in a row or column, then expand along that line. It turns a 6-term calculation into a 2-term one.

✦ Spot P3 instantly

Scan for two identical or proportional rows/columns before you touch a calculator — if found, the answer is 0 with zero arithmetic.

✦ Track the sign checkerboard

Memorise the cofactor sign pattern as a grid: \(\begin{smallmatrix}+&-&+\\-&+&-\\+&-&+\end{smallmatrix}\). Write it in the margin before computing any cofactor.

✦ Verify \(A\cdot\operatorname{adj}(A)=|A|I\)

After finding an adjoint, a 10-second multiplication check against \(|A|I\) catches almost every transpose or sign slip before you submit.

✦ Factor before expanding

If a row or column has a common factor, pull it out front using P4 before expanding — smaller numbers mean fewer mistakes.

✦ Collinearity shortcut

To check if three points are collinear, you don't need to halve anything — just test whether the raw 3×3 determinant is zero.

✦ Check consistency before solving

Always compute \(|A|\) first in a linear-system question. If it's zero, stop and analyse \((\operatorname{adj}A)B\) rather than trying to force a matrix-method answer.

✦ Row/column operations, one at a time

Never chain two row operations that both involve the row you just changed (e.g. \(R_1\to R_1+R_2\) then \(R_2\to R_2-R_1\)) in the same step — do them sequentially to avoid contaminating the result.

Common Mistakes

The errors examiners see most often — and the correct version, side by side.

⚠ Forgetting the adjoint transpose

Writing the cofactor matrix and calling it the adjoint directly.

✗ adj(A) = [C_ij]
✓ adj(A) = [C_ij]ᵀ (transpose the cofactor matrix)
⚠ Sign errors in cofactor expansion

Applying \((-1)^{i+j}\) inconsistently, especially for the middle term.

✗ +a₁M₁₁ + b₁M₁₂ + c₁M₁₃
✓ +a₁M₁₁ − b₁M₁₂ + c₁M₁₃
⚠ Skipping the absolute value in area

Submitting a negative "area" because the determinant came out negative.

✗ Area = −7 sq units
✓ Area = |−7| = 7 sq units
⚠ Confusing minor and cofactor

Using \(M_{ij}\) where \(C_{ij}\) was required (or vice-versa), especially in the adjoint.

✗ adj(A) uses minors directly
✓ adj(A) uses signed cofactors, then transposed
⚠ Assuming |A| ≠ 0 without checking

Jumping straight to \(A^{-1}=\frac{1}{|A|}\operatorname{adj}(A)\) without verifying the matrix is non-singular.

✗ "A⁻¹ exists" (unchecked)
✓ Compute |A| first; state "since |A| ≠ 0, A⁻¹ exists"
⚠ Misapplying the scalar-multiple rule

Treating \(|kA|\) the same as \(k|A|\), ignoring the matrix's order.

✗ |kA| = k|A|
✓ |kA| = kⁿ|A| for an n×n matrix
⚠ Row operation applied to the wrong side

Writing \(R_1 \to R_1 + kR_2\) but actually computing \(R_2 \to R_2 + kR_1\), silently changing a different row.

✗ Label says R₁, work shown modifies R₂
✓ Double-check the row you name matches the row you rewrite
⚠ Using cofactors from the wrong row/column in Cramer's Rule

Replacing the wrong column with the constants vector when forming \(\Delta_1,\Delta_2,\Delta_3\).

✗ Δ₂ formed by replacing column 1
✓ Δ₂ formed by replacing column 2 (the y-column) with constants

Concept-Building Practice Questions

Original questions, organised by concept, each with a full worked solution. Click "Show Solution" to reveal the steps.

Interactive Modules

Four hands-on modules for building intuition beyond static examples.

Start from a base determinant and apply one property at a time. Watch how — and whether — the value changes.

\(\begin{vmatrix}2&1\\3&4\end{vmatrix} = 5\)

This is the base matrix. Choose an operation above to see the property in action.

Click any cell of a 3×3 matrix to see its minor, its cofactor sign, and its cofactor value highlighted.

Click a cell above to compute its minor and cofactor.

Click three points on the grid (or drag existing ones) to instantly compute the area determinant and check collinearity.

Click anywhere on the grid to place your first vertex.

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    Determinants — Learning Resources

    🧠 Practice MCQs

    Frequently Asked Questions

    A determinant is a unique number associated with a square matrix. It is denoted by |A| or det(A) and is used to study matrix properties, solve equations and find the inverse of a matrix.

    For A = [[a,b],[c,d]], the determinant is |A| = ad - bc.

    A 3×3 determinant can be evaluated by expanding along any row or column using the corresponding elements, minors and cofactors.

    The minor Mij of an element aij is obtained by deleting its ith row and jth column. Its cofactor is Aij = (-1)^(i+j)Mij.

    A square matrix A is singular if |A| = 0 and non-singular if |A| ? 0.

    For a non-singular square matrix A, the inverse is A?¹ = (1/|A|) adj(A), where |A| ? 0.

    For vertices (x1,y1), (x2,y2), and (x3,y3), the area is 1/2 times the absolute value of the determinant formed using the three coordinates and a final column of 1s.

    If a system is written as AX = B and |A| ? 0, then A is invertible and the unique solution is X = A?¹B.

    Determinants are important for CBSE and JEE because questions commonly test determinant evaluation, properties, minors, cofactors, inverse matrices, area problems and systems of linear equations.

    Key results include |A| ? 0 for an invertible matrix, A?¹ = (1/|A|)adj(A), A(adj A) = |A|I, and X = A?¹B for AX = B when A is non-singular.

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