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Class 12 · Mathematics · Part 1

Chapter 3: Matrices

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.

3.2What a matrix isRows, columns, order and entries

Definition

A matrix is an ordered rectangular array of numbers (or functions) arranged in rows and columns and enclosed in brackets. The numbers are its elements or entries. Matrices are used to store and combine data such as sales figures, prices and coefficients of equations.

A matrix with m rows and n columns has order m × n (read 'm by n'). It has mn elements. The entry in row i and column j is written aij, and the matrix is A = [aij]m×n.

Reading a matrix

a₁₁a₁₂a₁₃a₁₄a₂₁a₂₂a₂₃a₂₄a₃₁a₃₂a₃₃a₃₄column 1column 2column 3column 4row 1row 2row 3a₂₃: row 2, column 33 × 4
A matrix with 3 rows and 4 columns has order 3 × 4. The entry aij sits in row i and column j, so the highlighted entry is a₂₃. The first subscript is the row, the second the column.
  • Order is always rows × columns. A 2 × 3 matrix is not the same shape as a 3 × 2 matrix.
  • The number of elements is the product of the order: 3 × 4 gives 12.
  • To write a matrix from a rule, such as aij = 2i − j, substitute each (i, j) in turn.

When are two matrices equal?

Two matrices A = [aij] and B = [bij] are equal if they have the same order and aij = bij for every i and j.

Example: x + y41x − y = 6412 gives x + y = 6 and x − y = 2, so x = 4 and y = 2.

Equality of matrices gives one equation for each entry. Matrices of different orders can never be equal.

3.3Types of matricesColumn, row, square, diagonal, scalar, identity and zero

By shape

TypeDescriptionExample
Column matrixOnly one column (order m × 1)147
Row matrixOnly one row (order 1 × n)2−53
Square matrixSame number of rows and columns (m = n)1234
Rectangular matrixRows and columns not equal123456

Special square matrices

TypeConditionExample
Diagonalaij = 0 whenever i ≠ j2000−10005
ScalarDiagonal, with all diagonal entries equal300030003
Identity (I)Diagonal, with every diagonal entry 11001
Zero (O)Every entry is 0 (any order)000000
Square matrixm = nany square matrix, such as 2 × 2 or 3 × 3Diagonal matrixaij = 0 for i ≠ je.g. diag(2, −1, 5)Scalar matrixdiagonal entries all equale.g. 3IIdentity matrixdiagonal entries all 1I
Every identity matrix is a scalar matrix, every scalar matrix is a diagonal matrix, and every diagonal matrix is square. The reverse is not true.

Things to remember

  • The main diagonal of a square matrix runs from top left to bottom right: a₁₁, a₂₂, a₃₃, …
  • Every scalar matrix is diagonal, but a diagonal matrix with different entries is not scalar.
  • Identity matrices exist only as square matrices. Zero matrices can have any order.
  • The diagonal entries of a matrix need not be non-zero; the zero matrix is also diagonal.
3.4Adding and scalingSum, difference and scalar multiples

Addition and subtraction

You can add or subtract matrices only when they have the same order. Do it entry by entry:

A + B = [aij + bij]A − B = [aij − bij]

Example: 1234 + 0110 = 1344.

Scalar multiplication

If k is a number, kA is found by multiplying every entry of A by k. The matrix −A = (−1)A, and A − B = A + (−B).

Example: 31234 = 36912.

Laws that hold

  • A + B = B + A (commutative) and (A + B) + C = A + (B + C) (associative).
  • A + O = A, and A + (−A) = O.
  • k(A + B) = kA + kB and (k + l)A = kA + lA, for scalars k and l.
3.4Multiplying matricesRow times column

When the product exists

AB is defined only when the number of columns of A equals the number of rows of B. If A is m × n and B is n × p, then AB is m × p.

a₁₁a₁₂a₁₃a₂₁a₂₂a₂₃b₁₁b₁₂b₂₁b₂₂b₃₁b₃₂c₁₁c₁₂c₂₁c₂₂A (2 × 3)B (3 × 2)AB (2 × 2)×=c₁₂ = a₁₁b₁₂ + a₁₂b₂₂ + a₁₃b₃₂row 1 of A times column 2 of B, multiplied entry by entry and added
To multiply, the columns of the first matrix must equal the rows of the second (3 and 3). The result takes the rows of A and the columns of B. Each entry is a row of A times a column of B.

The entry in row i and column j of AB is the sum of products of row i of A with column j of B:

(AB)ij = ai1 b1j + ai2 b2j + … + ain bnj

Example

12340110 = 1·0 + 2·11·1 + 2·03·0 + 4·13·1 + 4·0 = 2143.

But 01101234 = 3412, which is different.

What is different from numbers

  • AB ≠ BA in general. Often only one of AB and BA is even defined.
  • AB = O does not mean A = O or B = O. For example 11111−1−11 = 0000.
  • AB = AC does not allow us to cancel A and conclude B = C.
  • The usual algebra identities such as (A + B)² = A² + 2AB + B² fail, because AB and BA may differ. The correct expansion is A² + AB + BA + B².

Laws that hold

  • (AB)C = A(BC) (associative) and A(B + C) = AB + AC, (A + B)C = AC + BC (distributive).
  • IA = AI = A for a square matrix A with I of the same order.
  • A² = AA, A³ = AAA, and so on, for square matrices. Also A⁰ = I.
  • If A is a diagonal matrix, Aⁿ is found by raising each diagonal entry to the power n.

Checking the order first

Before you multiply, write the orders side by side, for example (3 × 2)(2 × 4). The two inner numbers must match, and the outer numbers give the order of the result (3 × 4).

3.5TransposeSwapping rows and columns

Definition

The transpose of A, written A′ (or Aᵀ), is obtained by interchanging its rows and columns. If A is m × n then A′ is n × m, and the (i, j) entry of A′ is the (j, i) entry of A.

Example: if A = 123456, then A′ = 135246.

Properties

  • (A′)′ = A
  • (A + B)′ = A′ + B′
  • (kA)′ = kA′
  • (AB)′ = B′A′, with the order reversed.

Check: with A = 123456 and B = 102310, (BA)′ and A′B′ are equal. The product reverses because the orders must still match: A′ is 2 × 3 and B′ is 3 × 2, so only A′B′ can be the transpose of BA.

3.6Symmetric and skew symmetric matricesA′ = A and A′ = −A

Definitions

A square matrix A is symmetric if A′ = A, that is aij = aji for all i, j. It is skew symmetric if A′ = −A, that is aij = −aji for all i, j.

abcbdecef0pq−p0r−q−r0Symmetric: A′ = ASkew symmetric: A′ = −Aaij = aji; mirror entries are equalaij = −aji; diagonal is 0
Fold a symmetric matrix along the main diagonal and the entries match (b with b, c with c, e with e). In a skew symmetric matrix they match with opposite signs, so every diagonal entry must be 0.

Key facts

  • In a skew symmetric matrix every diagonal entry is 0, because aii = −aii.
  • For any square matrix A, A + A′ is symmetric and A − A′ is skew symmetric.
  • A·A′ and A′·A are always symmetric.
  • If A and B are symmetric, AB is symmetric only when AB = BA. The matrix AB − BA is skew symmetric.
  • The only matrix that is both symmetric and skew symmetric is the zero matrix.

Theorem: every square matrix is a sum of a symmetric and a skew symmetric matrix

A = ½(A + A′) + ½(A − A′)

The first part P = ½(A + A′) is symmetric and the second part Q = ½(A − A′) is skew symmetric. The split is unique.

Example: A = 2375 gives P = 2555 and Q = 0−220.

3.7Invertible matricesInverse of a square matrix

Definition

A square matrix A of order n is invertible if there is a square matrix B of the same order with

AB = BA = I

B is called the inverse of A and is written A⁻¹. If no such B exists, A is not invertible.

Facts

  • Only square matrices can have an inverse.
  • If an inverse exists, it is unique.
  • If B is the inverse of A, then A is the inverse of B: (A⁻¹)⁻¹ = A.
  • (AB)⁻¹ = B⁻¹A⁻¹, with the order reversed, when A and B are both invertible.
  • The zero matrix and any matrix with a row or column of zeros has no inverse.

Example: A = 1−1−12 and B = 2111 give AB = 1001 = I (and BA = I too), so B = A⁻¹.

To check an inverse you must have AB = I and BA = I. For square matrices of the same order, either one implies the other.

Finding inverses later

This chapter only tests whether a given B is an inverse. In Chapter 4 you will find A⁻¹ directly with determinants and the adjoint, and in elementary row operations you will use A = IA.

RefQuick referenceFormulas and common slips

Formulas

ResultCondition
(AB)ij = Σ aik bkjcolumns of A = rows of B
(A′)′ = Aalways
(A + B)′ = A′ + B′same order
(AB)′ = B′A′AB defined
A = ½(A + A′) + ½(A − A′)A square
(AB)⁻¹ = B⁻¹A⁻¹A, B invertible, same order
AI = IA = AI of the same order

Common slips

  • Adding matrices of different orders.
  • Assuming AB = BA.
  • Concluding A = O or B = O from AB = O.
  • Using (A + B)² = A² + 2AB + B² or (AB)′ = A′B′.
  • Writing the order of a matrix as columns × rows.
  • Forgetting that the diagonal of a skew symmetric matrix is zero.
  • Calling a diagonal matrix with different entries a scalar matrix.