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Class 12 · Mathematics · Part 1

Chapter 4: Determinants

Revision notes, worked problems, a question bank, assertion and reason questions and answers to the NCERT exercises. Tap a group to open it; opening one closes the others.

4.2The determinantOrder 1, 2 and 3

What a determinant is

To every square matrix A = [aij] we attach one number, its determinant, written |A|, det A or Δ. The vertical bars here do not mean 'modulus': a determinant can be negative. Only square matrices have determinants.

The determinant tells us whether a system of equations has a unique solution: for a₁x + b₁y = c₁, a₂x + b₂y = c₂ it is a₁b₂ − a₂b₁, and the solution is unique when this is not 0.

Orders 1 and 2

  • Order 1: if A = [a], then |A| = a.
  • Order 2:
|A| = a₁₁a₁₂a₂₁a₂₂ = a₁₁a₂₂ − a₁₂a₂₁

Example: 24−12 = 2(2) − 4(−1) = 4 + 4 = 8.

Order 3: expanding along a row

Expand along row 1 using second-order determinants, with alternating signs +, −, +:

|A| = a₁₁a₂₂a₂₃a₃₂a₃₃ − a₁₂a₂₁a₂₃a₃₁a₃₃ + a₁₃a₂₁a₂₂a₃₁a₃₂

Each small determinant is what is left after deleting the row and column of the entry in front of it.

Choose the best row or column

The value is the same along any row or column, so expand along the one with the most zeros, and use the sign (−1)^(i+j): + when i + j is even, − when it is odd.

Example: 124−130410. Column 3 has two zeros, so expand along it: 4−1341 = 4(−1 − 12) = −52.

Scalar multiples

If A = kB where A and B are square matrices of order n, then |A| = kⁿ|B|. For a 3 × 3 matrix, |2A| = 8|A|. Do not confuse this with multiplying one row by k, which multiplies the determinant by k only once.

The determinant of a triangular matrix (all zeros above or below the diagonal) is the product of the diagonal entries. For example |1 2 3; 0 4 5; 0 0 6| = 24.

Useful standard facts

  • |A′| = |A|: a matrix and its transpose have the same determinant.
  • Interchanging two rows (or two columns) changes the sign of the determinant.
  • A determinant with two identical rows, or two proportional rows, or a row of zeros, is 0.
  • |AB| = |A||B| for square matrices of the same order. In general |A + B| ≠ |A| + |B|.

The product rule |AB| = |A||B| is in your textbook as Theorem 3. The facts on transposes and row interchange are standard results that you can use in the questions here, though the textbook exercises do not ask you to prove them.

4.3Area of a triangleAnd the equation of a line

The formula

The triangle with vertices (x₁, y₁), (x₂, y₂), (x₃, y₃) has area

Δ = ½ x₁y₁1x₂y₂1x₃y₃1 = ½ [x₁(y₂ − y₃) + x₂(y₃ − y₁) + x₃(y₁ − y₂)]
(1, 0)(6, 0)(4, 3)3Area = ½ |x₁(y₂ − y₃) + x₂(y₃ − y₁)+ x₃(y₁ − y₂)|For (1, 0), (6, 0), (4, 3):½ |1(0 − 3) + 6(3 − 0) + 4(0 − 0)|= ½ × 15 = 15/2 square units
The same number can be written as ½ times the 3 × 3 determinant with rows (x, y, 1). Take the absolute value, because area is never negative.

Rules for using it

  • Area is positive, so take the absolute value of the determinant.
  • If the area is given, use both + and − values: ½ D = ±Area. This gives two answers for an unknown, such as k = 0 or 8.
  • Three points are collinear exactly when the determinant is 0.

Equation of a line through two points

For a point P(x, y) to lie on the line through A and B, the triangle ABP must have zero area. So the line through (1, 3) and (0, 0) is 001131xy1 = 0, which gives y − 3x = 0, that is y = 3x.

Check that the answer passes through both given points.

4.4Minors and cofactorsExpansion in compact form

Definitions

The minor Mij of the element aij is the determinant left after deleting row i and column j. For order n ≥ 2, it has order n − 1.

The cofactor is Aij = (−1)^(i+j) Mij.

+−+−+−+−+Sign of Aij: (−1)^(i+j)a₁₁a₁₂a₁₃a₂₁a₂₂a₂₃a₃₁a₃₂a₃₃Minor M₂₃ of a₂₃signs alternate like a chessboarddelete row 2 and column 3: keep the corners
The sign of the cofactor Aij is + when i + j is even and − when it is odd. The minor Mij is the determinant left after deleting row i and column j.

Example

In 123456789 the element 6 is in row 2, column 3. Delete them: M₂₃ = 1278 = 8 − 14 = −6, and A₂₃ = (−1)⁵(−6) = 6.

Expansion by cofactors

|A| = a₁₁A₁₁ + a₁₂A₁₂ + a₁₃A₁₃(the same holds for any row or any column)

That is, |A| is the sum of the products of the elements of any row (or column) with their own cofactors.

The 'wrong cofactor' rule

If the elements of one row (or column) are multiplied by the cofactors of the elements of another row (or column), the sum is 0. For example a₁₁A₂₁ + a₁₂A₂₂ + a₁₃A₂₃ = 0, because it is the expansion of a determinant with two identical rows.

Check with 2−3560415−7: a₁₁A₃₁ + a₁₂A₃₂ + a₁₃A₃₃ = 2(−12) + (−3)(22) + 5(18) = −24 − 66 + 90 = 0.

4.5Adjoint and inverseUsing cofactors to find A⁻¹

The adjoint

The adjoint of a square matrix A is the transpose of the matrix of cofactors: adj A = [Aij]′.

For a 2 × 2 matrix there is a shortcut: swap the diagonal entries and change the signs of the other two. For example if A = 2314 then adj A = 4−3−12.

Key theorem

A (adj A) = (adj A) A = |A| I

Reason: the sum of products of a row with its own cofactors is |A| (on the diagonal) and with another row's cofactors is 0 (everywhere else).

Singular and non-singular

  • A is singular if |A| = 0 and non-singular if |A| ≠ 0.
  • If A and B are non-singular of the same order, then so are AB and BA.
  • |adj A| = |A|ⁿ⁻¹ for a matrix of order n (so |A|² for 3 × 3 and |A| for 2 × 2).
  • A is invertible if and only if A is non-singular, and then A⁻¹ = (1/|A|) adj A.

Finding A⁻¹

Find |A|must not be 0CofactorsAij = (−1)^(i+j) MijTransposeadj A = [Aij]′Divideby |A|A⁻¹ = (1/|A|) adj Aand A A⁻¹ = A⁻¹ A = IIf |A| = 0 the matrix is singular and has no inverse.
Method for the inverse of a square matrix: determinant first (stop if it is 0), then cofactors, then transpose, then divide.

Example: A = 133143134. |A| = 1(16 − 9) − 3(4 − 3) + 3(3 − 4) = 1. The cofactors are A₁₁ = 7, A₁₂ = −1, A₁₃ = −1, A₂₁ = −3, A₂₂ = 1, A₂₃ = 0, A₃₁ = −3, A₃₂ = 0, A₃₃ = 1. So adj A = 7−3−3−110−101 and, since |A| = 1, A⁻¹ = adj A.

Reversal law

If A and B are non-singular of the same order, (AB)⁻¹ = B⁻¹A⁻¹. Also (A⁻¹)⁻¹ = A and |A⁻¹| = 1/|A|.

Inverse from a matrix equation

If A satisfies an equation such as A² − 4A + I = O and |A| ≠ 0, multiply by A⁻¹: A − 4I + A⁻¹ = O, so A⁻¹ = 4I − A. For A = 2312 this gives 2−3−12.

4.6Solving linear equationsMatrix method and consistency

Writing a system as AX = B

A system of three equations in x, y, z is written AX = B, where A is the matrix of coefficients, X = [x, y, z]ᵀ and B is the column of constants.

Case I: |A| ≠ 0

A⁻¹ exists, so X = A⁻¹B: the system is consistent with a unique solution. This is the matrix method.

Example: 2x + 5y = 1, 3x + 2y = 7. |A| = −11, A⁻¹ = -1112−5−32, and X = A⁻¹B = 3−1, so x = 3, y = −1.

Case II: |A| = 0

Find (adj A)B.

  • If (adj A)B ≠ O, there is no solution: the system is inconsistent.
  • If (adj A)B = O, the system may have infinitely many solutions or none (this textbook only studies unique solutions).
One solution|A| ≠ 0No solution|A| = 0, (adj A)B ≠ OInfinitely many|A| = 0, (adj A)B = O
Two equations in two unknowns are two lines. They cross once, never, or are the same line, which matches the three cases for AX = B.

Consistent and inconsistent

A system is consistent if it has at least one solution and inconsistent if it has none.

Setting up word problems

  • Name the unknowns and write one equation per condition.
  • Write AX = B, find |A|, then A⁻¹ and X = A⁻¹B.
  • Check the answer in every original equation.

Example: the sum of three numbers is 6; three times the third plus the second is 11; the first plus the third is twice the second. The equations are x + y + z = 6, y + 3z = 11, x − 2y + z = 0, and the solution is x = 1, y = 2, z = 3.

Reading the equations from a product

If you are given a matrix product that gives the identity, such as AB = I, then B = A⁻¹ and you can solve AX = C as X = BC without finding an inverse again. This is how Example 19 of the textbook solves a system.

RefQuick referenceFormulas and common slips

Formulas

ResultCondition
|A| for 2 × 2: a₁₁a₂₂ − a₁₂a₂₁order 2
Area = ½ |x₁ y₁ 1; x₂ y₂ 1; x₃ y₃ 1| (absolute value)vertices known
Aij = (−1)^(i+j) Mijany square matrix
A (adj A) = |A| Iany square matrix
|adj A| = |A|ⁿ⁻¹order n
A⁻¹ = (1/|A|) adj A|A| ≠ 0
(AB)⁻¹ = B⁻¹A⁻¹A, B invertible
X = A⁻¹B|A| ≠ 0
|kA| = kⁿ|A|order n

Common slips

  • Forgetting the sign (−1)^(i+j) in a cofactor.
  • Forgetting to transpose the cofactor matrix to get adj A.
  • Finding A⁻¹ without first checking |A| ≠ 0.
  • Writing (AB)⁻¹ = A⁻¹B⁻¹.
  • Leaving the area negative: take the absolute value.
  • Using only the positive value when the area is given.
  • Writing |2A| = 2|A| for a 3 × 3 matrix: it is 8|A|.
  • Multiplying A⁻¹B in the wrong order. It must be X = A⁻¹B, not BA⁻¹.