Topics
Mathematical Logic
- Statements and Truth Values in Mathematical Logic
- Logical Connectives
- Tautology, Contradiction, and Contingency
- Quantifier, Quantified and Duality Statements in Logic
- Negations of Compound Statements
- Converse, Inverse, and Contrapositive
- Algebra of Statements
- Application of Logic to Switching Circuits
- Overview of Mathematical Logic
Matrices
Trigonometric Functions
Pair of Straight Lines
Vectors
Line and Plane
Linear Programming
Differentiation
- Introduction & Derivatives of Some Standard Functions
- Derivatives of Composite Functions
- Geometrical Meaning of Derivative
- Derivative of Inverse Function
- Logarithmic Differentiation
- Derivative of Implicit Functions
- Derivatives of Functions in Parametric Forms
- Higher Order Derivatives
- Overview of Differentiation
Applications of Derivatives
- Applications of Derivatives in Geometry
- Derivatives as a Rate Measure
- Approximations
- Rolle's Theorem
- Lagrange's Mean Value Theorem (LMVT)
- Increasing and Decreasing Functions
- Maxima and Minima
- Overview of Applications of Derivatives
Indefinite Integration
Definite Integration
- Definite Integral as Limit of Sum
- Integral Calculus
- Methods of Evaluation and Properties of Definite Integral
- Overview of Definite Integration
Application of Definite Integration
- Application of Definite Integration
- Area Bounded by Two Curves
- Overview of Application of Definite Integration
Differential Equations
- Basic Concepts of Differential Equations
- Order and Degree of a Differential Equation
- Formation of Differential Equations
- Methods of Solving Differential Equations> Homogeneous Differential Equations
- Methods of Solving Differential Equations>Linear Differential Equations
- Applications of Differential Equation
- Solution of a Differential Equation
- Overview of Differential Equations
Probability Distributions
- Random Variables
- Probability Distribution of Discrete Random Variables
- Probability Distribution of a Continuous Random Variable
- Variance of a Random Variable
- Expected Value and Variance of a Random Variable
- Overview of Probability Distributions
Binomial Distribution
Maharashtra State Board: Class 12
CISCE: Class 12
Key Points: Comparable and Equal Matrices
Comparable Matrices
-
Two matrices are said to be comparable if they have the same order
(same number of rows and columns).
Equal Matrices
Two matrices A= [aij] and B=[bij] are equal if:
-
They are comparable (same order), and
-
Their corresponding elements are equal.
Maharashtra State Board: Class 12
CISCE: Class 12
Definition: Negative of a Matrix
If A = [aij], then the negative of A, denoted by −A, is the matrix obtained by replacing each element aij by −aij
−A = [−aij]
- Order of −A = order of A
Maharashtra State Board: Class 12
CISCE: Class 12
Key Points: Powers of a Matrix
-
An is defined only when A is a square matrix.
-
AmAn = Am+n
-
In =
Maharashtra State Board: Class 12
CISCE: Class 12
Key Points: Elementary Operations on a Matrix
| Type | Transformation | Symbol |
|---|---|---|
| Interchange | Swap two rows/columns | Ri ↔ Rj |
| Multiplication | Multiply row/column by non-zero scalar k | Ri → kRi |
| Row addition | Add k times one row to another | Ri → Ri + kRj |
Maharashtra State Board: Class 12
CISCE: Class 12
Definition: Equivalent Matrices
Two matrices are equivalent if one can be obtained from the other by a finite number of elementary operations
-
Denoted by: A ∼ B
Maharashtra State Board: Class 12
CISCE: Class 12
Formula: Minor, Cofactor
Minor
Delete ith row and jth column: Mij
Cofactor of aij
Aij = (−1)i+j × (minor of aij)
Sign pattern:
\[\begin{bmatrix}
+ & - & + \\
- & + & - \\
+ & - & +
\end{bmatrix}\]
Maharashtra State Board: Class 12
CISCE: Class 12
Formula: Adjoint of a Matrix
Adjoint of A = transpose of the cofactor matrix \[\mathrm{adj}A=\left[A_{ij}\right]^T\]
-
\[\mathrm{adj}(kA)=k^{n-1}\mathrm{adj}(A)\]
-
A(adj A) = (adj A)A = ∣A∣I
-
∣adjA∣ = ∣A∣n−1(for an n×n non-singular matrix)
Maharashtra State Board: Class 12
CISCE: Class 12
Formula: Inverse of a Matrix of Order 2
\[A=
\begin{bmatrix}
a & b \\
c & d
\end{bmatrix}\]
\[A^{-1}=\frac{1}{ad-bc}
\begin{bmatrix}
d & -b \\
-c & a
\end{bmatrix}\] if ad − bc ≠ 0
\[A^{-1}=\frac{1}{|A|}(\operatorname{adj}A)\], if ∣A∣ ≠ 0
Properties:
-
\[(AB)^{-1}=B^{-1}A^{-1}\]
-
\[(A^{-1})^{-1}=A\]
-
\[(A^{\prime})^{-1}=(A^{-1})^{\prime}\]
- If inverse exists, it is unique.
Key Points: Method of Inversion
Matrix Form: AX = B
Condition:
-
A must be square
-
∣A∣ ≠ 0 (Non-singular)
Formula:
\[X=A^{-1}B\]
Key Points: Method of Reduction
-
Write AX = B
-
Apply row operations on A
(Same operations on B) -
Reduce A to triangular/identity form
-
Solve equations
