Matrix

Basics

1. Introduction to Matrices

Definition: A matrix is a rectangular array of numbers, symbols, or expressions arranged in rows and columns.

General Form:

$A = \begin{bmatrix} a_{11} & a_{12} & a_{13} & \cdots & a_{1n} \\ a_{21} & a_{22} & a_{23} & \cdots & a_{2n} \\ a_{31} & a_{32} & a_{33} & \cdots & a_{3n} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ a_{m1} & a_{m2} & a_{m3} & \cdots & a_{mn} \end{bmatrix}$

Notation:

• Matrix $A$ of order $m \times n$ (m rows, n columns)
• Element in $i^{th}$ row and $j^{th}$ column: $a_{ij}$
• $A = [a_{ij}]_{m \times n}$

Order/Dimension: $m \times n$ where $m$ = number of rows, $n$ = number of columns

2. Types of Matrices

Based on Order:

• a) Row Matrix: Matrix with only one row ($1 \times n$)

$A = \begin{bmatrix} 2 & 5 & 7 & 9 \end{bmatrix}$

• b) Column Matrix: Matrix with only one column ($m \times 1$)

$B = \begin{bmatrix} 3 \\ 7 \\ 2 \end{bmatrix}$

• c) Square Matrix: Matrix where $m = n$ (equal rows and columns)

$C = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} \quad (2 \times 2 \text{ square matrix})$

• d) Rectangular Matrix: Matrix where $m \neq n$

Based on Elements:

• e) Zero/Null Matrix: All elements are zero. Notation: $O$ or $0$

$O = \begin{bmatrix} 0 & 0 \\ 0 & 0 \end{bmatrix}$

• f) Diagonal Matrix: Square matrix where all non-diagonal elements are zero $(a_{ij} = 0 \text{ for } i \neq j)$

$D = \begin{bmatrix} 5 & 0 & 0 \\ 0 & 3 & 0 \\ 0 & 0 & 7 \end{bmatrix}$

• g) Scalar Matrix: Diagonal matrix where all diagonal elements are equal

$S = \begin{bmatrix} 4 & 0 & 0 \\ 0 & 4 & 0 \\ 0 & 0 & 4 \end{bmatrix}$

• h) Identity/Unit Matrix: Diagonal matrix where all diagonal elements are 1. Notation: $I$ or $I_n$

$I_3 = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}$

• i) Upper Triangular Matrix: Square matrix where all elements below the diagonal are zero $(a_{ij} = 0 \text{ for } i > j)$

$U = \begin{bmatrix} 2 & 5 & 7 \\ 0 & 3 & 8 \\ 0 & 0 & 4 \end{bmatrix}$

• j) Lower Triangular Matrix: Square matrix where all elements above the diagonal are zero $(a_{ij} = 0 \text{ for } i < j)$

$L = \begin{bmatrix} 2 & 0 & 0 \\ 5 & 3 & 0 \\ 7 & 8 & 4 \end{bmatrix}$

3. Matrix Operations

A. Equality of Matrices

• Two matrices $A$ and $B$ are equal if:
  1. They have the same order
  2. Corresponding elements are equal: $a_{ij} = b_{ij}$ for all $i, j$

B. Addition and Subtraction

• Conditions: Matrices must have the same order

• Addition: $A + B = [a_{ij} + b_{ij}]$

• Subtraction: $A - B = [a_{ij} - b_{ij}]$

• Properties:

  • Commutative: $A + B = B + A$
  • Associative: $(A + B) + C = A + (B + C)$
  • Additive Identity: $A + O = A$
  • Additive Inverse: $A + (-A) = O$

• Example:

$\begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} + \begin{bmatrix} 5 & 6 \\ 7 & 8 \end{bmatrix} = \begin{bmatrix} 6 & 8 \\ 10 & 12 \end{bmatrix}$

C. Scalar Multiplication

• Definition: Multiplying every element by a scalar $k$

$kA = [k \cdot a_{ij}]$

• Properties:

  • $k(A + B) = kA + kB$
  • $(k + m)A = kA + mA$
  • $(km)A = k(mA)$
  • $1 \cdot A = A$
  • $0 \cdot A = O$

• Example:

$3\begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} = \begin{bmatrix} 3 & 6 \\ 9 & 12 \end{bmatrix}$

D. Matrix Multiplication

• Condition: Number of columns in first matrix = number of rows in second matrix
• If $A$ is $m \times n$ and $B$ is $n \times p$, then $AB$ is $m \times p$
• Formula:

$(AB)_{ij} = \sum_{k=1}^{n} a_{ik} \cdot b_{kj}$

• Properties:

  • Generally NOT commutative: $AB \neq BA$
  • Associative: $(AB)C = A(BC)$
  • Distributive: $A(B + C) = AB + AC$
  • Identity: $AI = IA = A$
  • $(AB)^T = B^T A^T$

• Example:

$\begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} \begin{bmatrix} 5 & 6 \\ 7 & 8 \end{bmatrix} = \begin{bmatrix} 1 \times 5 + 2 \times 7 & 1 \times 6 + 2 \times 8 \\ 3 \times 5 + 4 \times 7 & 3 \times 6 + 4 \times 8 \end{bmatrix} = \begin{bmatrix} 19 & 22 \\ 43 & 50 \end{bmatrix}$

4. Transpose of a Matrix

• Definition: Matrix obtained by interchanging rows and columns

• If $A = [a_{ij}]_{m \times n}$, then $A^T = [a_{ji}]_{n \times m}$

• Properties:

  • $(A^T)^T = A$
  • $(A + B)^T = A^T + B^T$
  • $(kA)^T = kA^T$
  • $(AB)^T = B^T A^T$

• Example:

$A = \begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \end{bmatrix} \quad \Rightarrow \quad A^T = \begin{bmatrix} 1 & 4 \\ 2 & 5 \\ 3 & 6 \end{bmatrix}$

Special Matrices Related to Transpose:

• a) Symmetric Matrix: A square matrix where $A = A^T$ (i.e., $a_{ij} = a_{ji}$)

$S = \begin{bmatrix} 1 & 2 & 3 \\ 2 & 5 & 6 \\ 3 & 6 & 9 \end{bmatrix}$

• b) Skew-Symmetric Matrix: A square matrix where $A = -A^T$ (i.e., $a_{ij} = -a_{ji}$)

  • All diagonal elements must be zero.

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

• Important Property: Any square matrix can be expressed as the sum of a symmetric and skew-symmetric matrix:

$\boxed{A = \frac{1}{2}(A + A^T) + \frac{1}{2}(A - A^T)}$

• Symmetric part: $\boxed{\frac{1}{2}(A + A^T)}$
• Skew-symmetric part: $\boxed{\frac{1}{2}(A - A^T)}$

**5 Minor & Cofactor **

a) Minor of an element

• Definition: The minor of an element $a_{ij}$ in a matrix $A$, denoted by $M_{ij}$, is the determinant of the submatrix obtained by deleting the $i$-th row and $j$-th column from $A$.

• Notation: $M_{ij}$ or $|M_{ij}|$

• For a 2×2 Matrix: $A = \begin{bmatrix} a & b \ c & d \end{bmatrix}$ $M_{11}$ = determinant after removing 1st row and 1st column = $d$ $M_{12}$ = determinant after removing 1st row and 2nd column = $c$ $M_{21}$ = determinant after removing 2nd row and 1st column = $b$ $M_{22}$ = determinant after removing 2nd row and 2nd column = $a$

• For a 3×3 Matrix: $A = \begin{bmatrix} a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a_{31} & a_{32} & a_{33} \end{bmatrix}$

  $M_{11} = \begin{vmatrix} a_{22} & a_{23} \\ a_{32} & a_{33} \end{vmatrix} = a_{22}a_{33} - a_{23}a_{32}$

  $M_{12} = \begin{vmatrix} a_{21} & a_{23} \\ a_{31} & a_{33} \end{vmatrix} = a_{21}a_{33} - a_{23}a_{31}$

  $M_{13} = \begin{vmatrix} a_{21} & a_{22} \\ a_{31} & a_{32} \end{vmatrix} = a_{21}a_{32} - a_{22}a_{31}$

b) Cofactor of an Element

• Definition: The cofactor of an element $a_{ij}$, denoted by $C_{ij}$ or $A_{ij}$, is given by:

$\boxed{C_{ij} = (-1)^{i+j} \cdot M_{ij}}$

• where $M_{ij}$ is the minor of element $a_{ij}$.

• Sign Pattern: The factor $(-1)^{i+j}$ creates a checkerboard pattern of signs:

For any matrix: $$\begin{bmatrix}

• & - & + & - & \cdots \

• & + & - & + & \cdots \

• & - & + & - & \cdots \

• & + & - & + & \cdots \ \vdots & \vdots & \vdots & \vdots & \ddots \end{bmatrix}$$

For 2×2 Matrix: $\begin{bmatrix} + & - \\ - & + \end{bmatrix}$

For 3×3 Matrix: $\begin{bmatrix} + & - & + \\ - & + & - \\ + & - & + \end{bmatrix}$ Properties of Minors and Cofactors

• Property 1: The minor $M_{ij}$ is always a determinant value (scalar).
• Property 2: The cofactor $C_{ij}$ differs from minor $M_{ij}$ only by sign $(-1)^{i+j}$.
• Property 3: When $i + j$ is even, $C_{ij} = M_{ij}$ (positive sign)
• Property 4: When $i + j$ is odd, $C_{ij} = -M_{ij}$ (negative sign)
• Property 5: For a diagonal matrix, all cofactors on the diagonal are products of the other diagonal elements.

8. Adjoint of a Matrix

• Definition: Transpose of the cofactor matrix

• Steps to Find Adjoint:

  1. Find the cofactor matrix $C = [C_{ij}]$
  2. Take transpose: $\text{adj}(A) = C^T$

• Properties:

  • $A \cdot \text{adj}(A) = \text{adj}(A) \cdot A = \det(A) \cdot I$
  • $\text{adj}(A^T) = (\text{adj }A)^T$
  • $\text{adj}(AB) = \text{adj}(B) \cdot \text{adj}(A)$
  • $\text{adj}(kA) = k^{n-1} \text{adj}(A)$ for $n \times n$ matrix
  • $\det(\text{adj }A) = [\det(A)]^{n-1}$ for $n \times n$ matrix

• Example for 2×2 Matrix:

  $\text{If } A = \begin{bmatrix} a & b \\ c & d \end{bmatrix}$

  then $\boxed{\text{adj}(A) = \begin{bmatrix} d & -b \\ -c & a \end{bmatrix}}$

7. Determinant of a Matrix

• Definition: A scalar value that can be computed from a square matrix

• Notation: $\det(A)$ or $|A|$

1. For 2×2 Matrix:

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

2. For 3×3 Matrix:

$\begin{vmatrix} a & b & c \\ d & e & f \\ g & h & i \end{vmatrix} = a(ei - fh) - b(di - fg) + c(dh - eg)$

• Expansion by Cofactors:

$\det(A) = \sum_{j=1}^{n} a_{ij} C_{ij} \quad \text{(along any row or column)}$

• where $C_{ij} = (-1)^{i+j} M_{ij}$ (cofactor)

• and $M_{ij}$ = minor (determinant of submatrix after removing $i^{th}$ row and $j^{th}$ column)

• Properties of Determinants:

  • $\det(I) = 1$
  • $\det(O) = 0$
  • $\det(A^T) = \det(A)$
  • $\det(kA) = k^n \det(A)$ for $n \times n$ matrix
  • $\det(AB) = \det(A) \cdot \det(B)$
  • If two rows/columns are identical, $\det(A) = 0$
  • If we interchange two rows/columns, determinant changes sign
  • Multiplying a row/column by $k$ multiplies determinant by $k$
  • Adding a multiple of one row to another doesn’t change determinant

• Singular Matrix: $\det(A) = 0$

• Non-singular Matrix: $\det(A) \neq 0$

7. Inverse of a Matrix

• Definition: $A^{-1}$ is the inverse of $A$ if $A \cdot A^{-1} = A^{-1} \cdot A = I$

• Formula:

$\boxed{A^{-1} = \frac{1}{\det(A)} \cdot \text{adj}(A)}$

• Conditions for Inverse to Exist:

  • Matrix must be square
  • $\det(A) \neq 0$ (non-singular)

• Properties:

  • $(A^{-1})^{-1} = A$
  • $(AB)^{-1} = B^{-1} A^{-1}$
  • $(A^T)^{-1} = (A^{-1})^T$
  • $\det(A^{-1}) = \frac{1}{\det(A)}$
  • $(kA)^{-1} = \frac{1}{k} A^{-1}$

• Example:

$A = \begin{bmatrix} 2 & 3 \\ 1 & 4 \end{bmatrix}$

$\det(A) = 2 \times 4 - 3 \times 1 = 8 - 3 = 5$

$\text{adj}(A) = \begin{bmatrix} 4 & -3 \ -1 & 2 \end{bmatrix}$

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

8. Elementary Row Operations

Used to solve systems of equations and find inverse:

1. Row Interchange: $R_i \leftrightarrow R_j$ (swap two rows)
2. Row Scaling: $R_i \to kR_i$ (multiply a row by non-zero scalar)
3. Row Addition: $R_i \to R_i + kR_j$ (add multiple of one row to another)

Elementary Matrices: Matrices obtained by performing one elementary operation on the identity matrix

9. Rank of a Matrix

• Definition: The rank of a matrix is the maximum number of linearly independent rows (or columns)

• Methods to Find Rank:

  1. Convert to row echelon form using elementary row operations
  2. Count the number of non-zero rows==

• Properties:

  • $\text{rank}(A) \leq \min(m, n)$ for $m \times n$ matrix
  • $\text{rank}(A) = \text{rank}(A^T)$
  • $\text{rank}(AB) \leq \min(\text{rank}(A), \text{rank}(B))$
  • If $A$ is invertible $n \times n$ matrix, $\text{rank}(A) = n$ (full rank)

• Row Echelon Form: ⭐

  • All non-zero rows are above zero rows
  • Leading entry of each non-zero row is to the right of the leading entry of the row above

• Reduced Row Echelon Form:

  • Row echelon form
  • Leading entry in each non-zero row is 1
  • All entries above and below each leading 1 are zero

10. System of Linear Equations (❌ Not all important for GATE)

• General Form:

$\begin{cases} a_{11}x_1 + a_{12}x_2 + \cdots + a_{1n}x_n = b_1 \\ a_{21}x_1 + a_{22}x_2 + \cdots + a_{2n}x_n = b_2 \\ \vdots \\ a_{m1}x_1 + a_{m2}x_2 + \cdots + a_{mn}x_n = b_m \end{cases}$

• Matrix Form: $\boxed{AX = B}$
• where $A$ is coefficient matrix, $X$ is variable matrix, $B$ is constant matrix

Methods of Solution

• a) Matrix Inversion Method (when $A$ is square and non-singular): ⭐

$\boxed{X = A^{-1}B}$

• b) Cramer’s Rule (for square systems with $\det(A) \neq 0$):

$x_i = \frac{\det(A_i)}{\det(A)}$ - where $A_i$ is $A$ with $i$-th column replaced by $B$

• c) Gaussian Elimination:

  • Convert augmented matrix $[A|B]$ to row echelon form
  • Use back substitution

• d) Gauss-Jordan Elimination:

  • Convert to reduced row echelon form
  • Read off solutions directly

Consistency of System: ( ⭐GATE 2025 )

• Augmented Matrix: $[A|B]$
• Using rank:
  • Consistent (has solution): $\text{rank}(A) = \text{rank}([A|B])$
    1. Unique solution: $\text{rank}(A) = \text{rank}([A|B]) = n$ (number of variables)
    2. Infinite solutions: $\text{rank}(A) = \text{rank}([A|B]) < n$
  • Inconsistent (no solution): $\text{rank}(A) < \text{rank}([A|B])$

11. Special Products and Matrices ( ❌Not important for GATE)

a) Idempotent Matrix: $A^2 = A$

• Example: Identity matrix

b) Nilpotent Matrix: $A^k = O$ for some positive integer $k$

• Smallest such $k$ is called index of nilpotency

c) Involutory Matrix: $A^2 = I$

• $A$ is its own inverse: $A^{-1} = A$

d) Orthogonal Matrix: $AA^T = A^T A = I$

• $A^{-1} = A^T$
• $\det(A) = \pm 1$
• Columns (and rows) form orthonormal sets

e) Hermitian Matrix (for complex matrices): $A = \bar{A}^T$ (conjugate transpose)

f) Unitary Matrix: $A\bar{A}^T = I$

12. Trace of a Matrix

Definition: Sum of diagonal elements of a square matrix

$\boxed{\text{tr}(A) = \sum_{i=1}^{n} a_{ii}}$

Trace–Eigenvalue Relation: (⭐GATE 2023)

• Summation of the eigenvalue is equal to the trace of the matrix $\boxed{\text{tr}(A)=\sum_{i=1}^{n}\lambda_i}$

• Holds for any square matrix (diagonalizable or not)

• Follows from characteristic polynomial

• Works over real or complex field

• Frequently used to find unknown eigenvalues using trace shortcut

Properties:

• $\text{tr}(A + B) = \text{tr}(A) + \text{tr}(B)$
• $\text{tr}(kA) = k \cdot \text{tr}(A)$
• $\text{tr}(AB) = \text{tr}(BA)$
• $\text{tr}(A^T) = \text{tr}(A)$
• $\text{tr}(ABC) = \text{tr}(CAB) = \text{tr}(BCA)$ (cyclic property)

13. Applications of Matrices ( ❌Not important for GATE)

1. Solving Linear Equations: Systems of equations in science, engineering, economics
2. Computer Graphics: Transformations (rotation, scaling, translation)
3. Cryptography: Encoding and decoding messages
4. Economics: Input-output models, Leontief models
5. Network Analysis: Graph theory, connectivity
6. Quantum Mechanics: State vectors, operators
7. Statistics: Correlation matrices, covariance matrices
8. Machine Learning: Data representation, neural networks

14. Summary of Key Formulas ⭐⭐

Determinant (2×2):

$\boxed{ |A| = \begin{vmatrix} a & b \\ c & d \end{vmatrix} = ad - bc}$

Inverse:

$\boxed{A^{-1} = \frac{1}{|A|} \cdot \text{adj}(A)}$

Adjoint (2×2):

$\boxed{\text{adj}\begin{bmatrix} a & b \\ c & d \end{bmatrix} = \begin{bmatrix} d & -b \\ -c & a \end{bmatrix}}$

Product Rules:

$\boxed{(AB)^T = B^T A^T}$

$\boxed{(AB)^{-1} = B^{-1} A^{-1}}$

Rank Property:

$\boxed{\text{rank}(A) = \text{rank}(A^T)}$

Solution of $AX = B$:

$\boxed{X = A^{-1}B \quad \text{(when } A \text{ is invertible)}}$

---

Eigenvalues & Eigenvectors

1. Eigenvalues and Eigenvectors ⭐

Eigenvector: A non-zero vector $\vec{v}$ is called an eigenvector of a square matrix $A$ if there exists a scalar $\lambda$ such that:

$\boxed{A\vec{v} = \lambda\vec{v}}$

Eigenvalue: The scalar $\lambda$ is called the eigenvalue corresponding to the eigenvector $\vec{v}$.

• $\vec{v}$: Eigen Vector
• $\lambda$: Eigen Value

Geometric Interpretation: When matrix $A$ acts on eigenvector $\vec{v}$, the result is a vector in the same direction (or opposite direction) as $\vec{v}$, scaled by factor $\lambda$.

2. Characteristic Equation ⭐

• To find eigenvalues, we solve:

$A\vec{v} = \lambda\vec{v}$ $A\vec{v} - \lambda\vec{v} = \vec{0}$ $(A - \lambda I)\vec{v} = \vec{0}$

• For non-trivial solution (where $\vec{v} \neq \vec{0}$):

$\boxed{\det(A - \lambda I) = 0}$

• This is called the Characteristic Equation of matrix $A$.

• Characteristic Polynomial: $\boxed{p(\lambda) = \det(A - \lambda I)}$

3. Finding Eigenvalues and Eigenvectors ⭐⭐⭐

Step-by-Step Process:

1. Step 1: Find eigenvalues by solving $\det(A - \lambda I) = 0$
2. Step 2: For each eigenvalue $\lambda_i$, find eigenvectors by solving $(A - \lambda_i I)\vec{v} = \vec{0}$

Example: Find eigenvalues and eigenvectors of $A = \begin{bmatrix} 4 & 2 \\ 1 & 3 \end{bmatrix}$

Solution:

1. Step 1: Find eigenvalues

   $A - \lambda I = \begin{bmatrix} 4-\lambda & 2 \\ 1 & 3-\lambda \end{bmatrix}$

   $\det(A - \lambda I) = (4-\lambda)(3-\lambda) - 2 = \lambda^2 - 7\lambda + 10 = 0$

   $(\lambda - 5)(\lambda - 2) = 0 \quad$, Eigenvalues: $\lambda_1 = 5, \lambda_2 = 2$

2. Step 2: Find eigenvectors

   • For $\lambda_1 = 5$: $(A - 5I)\vec{v} = \begin{bmatrix} -1 & 2 \\ 1 & -2 \end{bmatrix} \begin{bmatrix} v_1 \\ v_2 \end{bmatrix} = \begin{bmatrix} 0 \\ 0 \end{bmatrix} \quad$, Solution: $\vec{v}_1 = \begin{bmatrix} 2 \\ 1 \end{bmatrix}$ (or any scalar multiple)

   • For $\lambda_2 = 2$: $(A - 2I)\vec{v} = \begin{bmatrix} 2 & 2 \ 1 & 1 \end{bmatrix}\begin{bmatrix} v_1 \ v_2 \end{bmatrix} = \begin{bmatrix} 0 \ 0 \end{bmatrix} \quad$, Solution: $\vec{v}_2 = \begin{bmatrix} 1 \ -1 \end{bmatrix}$ (or any scalar multiple)

4. Properties of Eigenvalues ⭐

1. Property 1: Sum of eigenvalues = Trace of matrix

$\sum_{i=1}^{n} \lambda_i = \text{tr}(A) = \sum_{i=1}^{n} a_{ii}$

2. Property 2: Product of eigenvalues = Determinant of matrix

$\prod_{i=1}^{n} \lambda_i = \det(A)$

3. Property 3: If $\lambda$ is an eigenvalue of $A$, then:

• $\lambda^k$ is an eigenvalue of $A^k$ (⭐GATE 2023)
• $\lambda + c$ is an eigenvalue of $A + cI$
• $k\lambda$ is an eigenvalue of $kA$
• $\frac{1}{\lambda}$ is an eigenvalue of $A^{-1}$ (if $A$ is invertible and $\lambda \neq 0$)

3. Property 4: Eigenvalues of $A$ and $A^T$ are the same

4. Property 5: For triangular (upper or lower) and diagonal matrices, eigenvalues are the diagonal elements

5. Property 6: If $A$ is singular, then $\lambda = 0$ is an eigenvalue

6. Property 7: Eigenvalues of a real symmetric matrix are always real

7. Property 8: Eigenvalues of an orthogonal matrix have absolute value 1

8. Property 9: Eigenvalues of a Hermitian matrix are real

9. Property 10: Eigenvalues of a skew-Hermitian matrix are purely imaginary or zero

5. Properties of Eigenvectors

1. Property 1: Eigenvectors corresponding to distinct eigenvalues are linearly independent

2. Property 2: If $\vec{v}$ is an eigenvector, then $k\vec{v}$ (for any non-zero scalar $k$) is also an eigenvector with the same eigenvalue

3. Property 3: For a real symmetric matrix, eigenvectors corresponding to distinct eigenvalues are orthogonal

4. Property 4: The eigenvectors corresponding to an eigenvalue with algebraic multiplicity $m$ span a subspace of dimension $\leq m$

6. Algebraic and Geometric Multiplicity ( ❌Not important for GATE)

Algebraic Multiplicity: The number of times an eigenvalue appears as a root of the characteristic polynomial

Geometric Multiplicity: The dimension of the eigenspace (number of linearly independent eigenvectors) corresponding to an eigenvalue

$\text{Geometric Multiplicity} \leq \text{Algebraic Multiplicity}$

Example:

$A = \begin{bmatrix} 5 & 0 & 0 \\ 0 & 5 & 0 \\ 0 & 0 & 3 \end{bmatrix}$

• Characteristic equation: $(5-\lambda)^2(3-\lambda) = 0$
• Eigenvalues: $\lambda_1 = 5$ (algebraic multiplicity 2), $\lambda_2 = 3$ (algebraic multiplicity 1)
• For $\lambda_1 = 5$: geometric multiplicity = 2 (two independent eigenvectors)

7. Diagonalization

A matrix $A$ is diagonalizable if there exists an invertible matrix $P$ such that:

$\boxed{P^{-1}AP = D}$

where $D$ is a diagonal matrix containing eigenvalues of $A$, and columns of $P$ are the corresponding eigenvectors.

$D = \begin{bmatrix} \lambda_1 & 0 & \cdots & 0 \\ 0 & \lambda_2 & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \cdots & \lambda_n \end{bmatrix}$

Condition for Diagonalization: An $n \times n$ matrix is diagonalizable if and only if it has $n$ linearly independent eigenvectors.

Guaranteed Diagonalizable:

• Matrices with $n$ distinct eigenvalues
• Real symmetric matrices
• Hermitian matrices
• Orthogonal matrices
• Unitary matrices

Applications of Diagonalization:

1. Computing Powers: $A^k = PD^kP^{-1}$
2. Matrix Functions: $f(A) = Pf(D)P^{-1}$
3. Solving Differential Equations

8. Similarity of Matrices ( ❌Not important for GATE)

Two matrices $A$ and $B$ are similar if there exists an invertible matrix $P$ such that:

$\boxed{B = P^{-1}AP}$

Properties of Similar Matrices:

• Same eigenvalues
• Same determinant
• Same trace
• Same rank
• Same characteristic polynomial

9. Cayley-Hamilton Theorem ( ❌Not important for GATE)

Theorem: Every square matrix satisfies its own characteristic equation.

If $p(\lambda) = \det(A - \lambda I)$ is the characteristic polynomial, then:

$p(A) = O$

Example: If $A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix}$

• Characteristic equation: $\lambda^2 - 5\lambda - 2 = 0$

• Then: $A^2 - 5A - 2I = O$

• Applications:

  • Finding matrix inverse
  • Computing higher powers of matrices
  • Simplifying matrix polynomials

Finding Inverse using Cayley-Hamilton:

• If characteristic equation is $\lambda^n + c_{n-1}\lambda^{n-1} + \cdots + c_1\lambda + c_0 = 0$

• Then: $A^n + c_{n-1}A^{n-1} + \cdots + c_1A + c_0I = O$

• If $c_0 \neq 0$ (i.e., $\det(A) \neq 0$):

$A^{-1} = -\frac{1}{c_0}(A^{n-1} + c_{n-1}A^{n-2} + \cdots + c_1I)$

10. Special Eigenvalue Problems ( ❌Not important for GATE)

Symmetric Matrices:

• All eigenvalues are real
• Eigenvectors corresponding to distinct eigenvalues are orthogonal
• Always diagonalizable

Orthogonal Matrices ($A^TA = I$):

• All eigenvalues have absolute value 1
• $|\lambda| = 1$ or $\lambda = e^{i\theta}$

Positive Definite Matrices:

• All eigenvalues are positive
• All leading principal minors are positive

Idempotent Matrices ($A^2 = A$):

• Eigenvalues are either 0 or 1

Nilpotent Matrices ($A^k = O$):

• All eigenvalues are 0

Involutory Matrices ($A^2 = I$):

• Eigenvalues are either 1 or -1

11. Power Method (Finding Dominant Eigenvalue) ( ❌Not important for GATE)

The power method is an iterative technique to find the largest (dominant) eigenvalue and its corresponding eigenvector.

• Algorithm:

  1. Start with initial guess $\vec{x}_0$ (non-zero)
  2. Iterate: $\vec{x}_{k+1} = \frac{A\vec{x}_k}{|A\vec{x}_k|}$
  3. Eigenvalue: $\lambda \approx \frac{\vec{x}_k^T A \vec{x}_k}{\vec{x}_k^T \vec{x}_k}$

• Converges to: Dominant eigenvalue (eigenvalue with largest absolute value)

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System of Linear Equations (Advanced Concepts)

1. Matrix Representation

System of equations:

$\begin{cases} a_{11}x_1 + a_{12}x_2 + \cdots + a_{1n}x_n = b_1 \\ a_{21}x_1 + a_{22}x_2 + \cdots + a_{2n}x_n = b_2 \\ \vdots \\ a_{m1}x_1 + a_{m2}x_2 + \cdots + a_{mn}x_n = b_m \end{cases}$

Matrix form: $AX = B$

where:

$A = \begin{bmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ a_{21} & a_{22} & \cdots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{m1} & a_{m2} & \cdots & a_{mn} \end{bmatrix}, \quad X = \begin{bmatrix} x_1 \\ x_2 \\ \vdots \\ x_n \end{bmatrix}, \quad B = \begin{bmatrix} b_1 \\ b_2 \\ \vdots \\ b_m \end{bmatrix}$

Augmented Matrix: $[A | B]$ ⭐⭐

2. Types of Linear Systems

1. Homogeneous System: $AX = 0$ (where $B = 0$) ⭐

   • Always has at least the trivial solution $X = 0$
   • Non-trivial solutions exist if and only if $\det(A) = 0$ (for square matrices) ⭐

2. Non-homogeneous System: $AX = B$ (where $B \neq 0$)

3. Consistency and Solution Types ⭐⭐

For Square Systems ($m = n$):

Determinant Condition	Determinant	Solution Type
$\det(A) \neq 0$	Non-zero	Unique solution: $X = A^{-1}B$
$\det(A) = 0$ and $\text{rank}(A) = \text{rank}([A\|B])$	Zero	Infinite solutions
$\det(A) = 0$ and $\text{rank}(A) < \text{rank}([A\|B])$	Zero	No solution (inconsistent)

For General Systems ($m \times n$):

Let $r = \text{rank}(A)$ and $r' = \text{rank}([A|B])$

Rank Condition	Solution Type
$r < r'$	Inconsistent (no solution)
$r = r' = n$	Unique solution
$r = r' < n$	Infinite solutions (with $n - r$ free parameters)

4. Rouché-Capelli Theorem ( ❌Not important for GATE)

A system $AX = B$ is consistent if and only if:

$\boxed{\text{rank}(A) = \text{rank}([A|B])}$

Number of free variables = $n - \text{rank}(A)$

5. Homogeneous Systems

System: $AX = 0$

Properties:

• Always has trivial solution $X = 0$
• Has non-trivial solution if and only if $\det(A) = 0$ (for square $A$)
• For $m \times n$ system with $m < n$: always has infinite solutions
• Solution space forms a vector space (null space or kernel of $A$)

Dimension of solution space: $n - \text{rank}(A)$ (called nullity of $A$)

Rank-Nullity Theorem: For $m \times n$ matrix $A$:

$\text{rank}(A) + \text{nullity}(A) = n$

6. Solution Methods ( ❌ Not all important for GATE)

A. Cramer’s Rule

• For square system with $\det(A) \neq 0$:

$x_i = \frac{\det(A_i)}{\det(A)}$

• where $A_i$ is the matrix obtained by replacing the $i$-th column of $A$ with $B$.

• Example: Solve $\begin{cases} 2x + 3y = 8 \ x + 4y = 9 \end{cases}$

  $A = \begin{bmatrix} 2 & 3 \ 1 & 4 \end{bmatrix}, \quad B = \begin{bmatrix} 8 \ 9 \end{bmatrix}$

  $\det(A) = 8 - 3 = 5$

  $A_1 = \begin{bmatrix} 8 & 3 \ 9 & 4 \end{bmatrix}, \quad \det(A_1) = 32 - 27 = 5$

  $x = \frac{5}{5} = 1$

  $A_2 = \begin{bmatrix} 2 & 8 \ 1 & 9 \end{bmatrix}, \quad \det(A_2) = 18 - 8 = 10$

  $y = \frac{10}{5} = 2$

• Limitation: Computationally expensive for large systems

B. Matrix Inversion Method ⭐

• For square system with $\det(A) \neq 0$:

$\boxed{X = A^{-1}B}$

• Steps:
  1. Find $A^{-1}$ using $A^{-1} = \frac{1}{\det(A)} \text{adj}(A)$
  2. Compute $X = A^{-1}B$

C. Gaussian Elimination

• Process:

  1. Form augmented matrix $[A|B]$
  2. Use elementary row operations to convert to row echelon form
  3. Use back substitution to find solutions

• Elementary Row Operations:

  • $R_i \leftrightarrow R_j$ (interchange rows)
  • $R_i \to kR_i$ (multiply row by non-zero constant)
  • $R_i \to R_i + kR_j$ (add multiple of one row to another)

• Row Echelon Form:

  • All zero rows are at the bottom
  • Leading coefficient (pivot) of each non-zero row is to the right of the pivot above it

• Example: $\begin{cases} x + 2y + z = 9 \ 2x - y + 3z = 8 \ 3x + y + 2z = 13 \end{cases}$

  Augmented matrix: $\left[\begin{array}{ccc|c} 1 & 2 & 1 & 9 \\ 2 & -1 & 3 & 8 \\ 3 & 1 & 2 & 13 \end{array}\right]$

  After row operations: $\left[\begin{array}{ccc|c} 1 & 2 & 1 & 9 \\ 0 & -5 & 1 & -10 \\ 0 & 0 & -2 & -4 \end{array}\right]$

  Back substitution: $z = 2, y = 2, x = 1$

D. Gauss-Jordan Elimination

• Process:

  1. Form augmented matrix $[A|B]$
  2. Convert to reduced row echelon form (RREF)
  3. Read solutions directly

• Reduced Row Echelon Form:

  • Row echelon form
  • Leading coefficient (pivot) in each row is 1
  • Each pivot is the only non-zero entry in its column

• Example: Same system as above

  After Gauss-Jordan: $\left[\begin{array}{ccc|c} 1 & 0 & 0 & 1 \\ 0 & 1 & 0 & 2 \\ 0 & 0 & 1 & 2 \end{array}\right]$

  Solution: $x = 1, y = 2, z = 2$

E. LU Decomposition

• Decompose $A$ into product of lower triangular $L$ and upper triangular $U$:

$A = LU$

• Process:

  1. Find $L$ and $U$ such that $A = LU$
  2. Solve $LY = B$ (forward substitution)
  3. Solve $UX = Y$ (backward substitution)

• Advantage: Efficient for solving multiple systems with same $A$ but different $B$

7. Particular and General Solutions ( ❌ Not important for GATE)

For non-homogeneous system $AX = B$:

General Solution = Particular Solution + Homogeneous Solution

$X = X_p + X_h$

where:

• $X_p$ is any particular solution of $AX = B$
• $X_h$ is the general solution of $AX = 0$

Example: If $AX = B$ has particular solution $X_p = \begin{bmatrix} 1 \\ 2 \\ 0 \end{bmatrix}$

and homogeneous solution $X_h = t\begin{bmatrix} 1 \\ -1 \\ 1 \end{bmatrix}$ (where $t$ is any real number)

Then general solution: $X = \begin{bmatrix} 1 \\ 2 \\ 0 \end{bmatrix} + t\begin{bmatrix} 1 \\ -1 \\ 1 \end{bmatrix} = \begin{bmatrix} 1+t \\ 2-t \\ t \end{bmatrix}$

8. Vector Space Interpretation ( ❌ Not important for GATE)

Column Space (Range/Image of $A$): $C(A) = {A\vec{x} : \vec{x} \in \mathbb{R}^n}$

• Dimension = $\text{rank}(A)$

Null Space (Kernel of $A$): $N(A) = {\vec{x} : A\vec{x} = \vec{0}}$

• Dimension = $\text{nullity}(A) = n - \text{rank}(A)$

Row Space: Space spanned by rows of $A$

• Dimension = $\text{rank}(A)$

Left Null Space: Null space of $A^T$

Fundamental Theorem:

$\text{rank}(A) + \text{nullity}(A) = n$

$\dim(C(A)) + \dim(N(A^T)) = m$

9. Least Squares Solution ( ❌ Not important for GATE)

For overdetermined systems ($m > n$, more equations than unknowns) with no exact solution:

Normal Equation:

$A^TAX = A^TB$

Least Squares Solution:

$X = (A^TA)^{-1}A^TB$

This minimizes $|AX - B|^2$

Application: Best fit line, curve fitting, data regression

10. Condition Number and Stability ( ❌ Not important for GATE)

Condition Number: Measures sensitivity of solution to changes in $A$ or $B$

$\kappa(A) = |A| \cdot |A^{-1}|$

For eigenvalue-based definition:

$\kappa(A) = \frac{\lambda_{\max}}{\lambda_{\min}}$

Well-conditioned: $\kappa(A)$ is small (close to 1)

Ill-conditioned: $\kappa(A)$ is large

Singular matrix: $\kappa(A) = \infty$

11. Important GATE Concepts Summary ( ❌ Not important for GATE)

For Homogeneous Systems ($AX = 0$):

• Always consistent (at least trivial solution)
• Non-trivial solution exists $\iff$ $\det(A) = 0$ (for square $A$)
• Number of free variables = $n - \text{rank}(A)$

For Non-homogeneous Systems ($AX = B$):

• Consistent $\iff$ $\text{rank}(A) = \text{rank}([A|B])$
• Unique solution $\iff$ $\text{rank}(A) = \text{rank}([A|B]) = n$
• Infinite solutions $\iff$ $\text{rank}(A) = \text{rank}([A|B]) < n$

Key Formulas:

• Cramer’s Rule: $x_i = \frac{\det(A_i)}{\det(A)}$
• Matrix Inversion: $X = A^{-1}B$
• Rank-Nullity: $\text{rank}(A) + \text{nullity}(A) = n$

Solution Methods Priority (for GATE):

1. Cramer’s Rule (for 2×2 or 3×3 with unique solution)
2. Matrix Inversion (for small square non-singular systems)
3. Gaussian Elimination (for general systems)
4. Rank method (for consistency check)