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
- Elementry Transformations
- Adjoint of a Matrix
- Application of Matrices
- Overview of Matrices
Trigonometric Functions
- Trigonometric Equations and Their Solutions
- Solutions of Triangle>Polar Co-Ordinates
- Solving a Triangle>Solving a Triangle
- Basics of Inverse Trigonometric Functions
- Graphs of Inverse Trigonometric Functions
- Domain, Range & Principal Value
- Properties of Inverse Trigonometric Functions > Self-adjusting Property
- Overview of Trigonometric Functions
- Properties of Inverse Trigonometric Functions > Reciprocal Property
- Properties of Inverse Trigonometric Functions > Complementary Property
- Properties of Inverse Trigonometric Functions > Addition & Subtraction Formula for Inverse Tangent
- Properties of Inverse Trigonometric Functions > Double-angle Property
- Properties of Inverse Trigonometric Functions > Triple-angle Property
- Properties of Inverse Trigonometric Functions > Addition–Subtraction Formula for Inverse Sine & Cosine
- Properties of Inverse Trigonometric Functions > Negative Argument Property
Pair of Straight Lines
Vectors
- Overview of Vectors
- Basic Concepts of Vector Algebra
- Types of Vectors in Algebra
- Algebra of Vectors > Scalar Multiplication
- Algebra of Vectors > Addition & Subtraction of Two Vectors
- Collinearity and Coplanarity of Vectors
- Vectors in Coordinate Geometry
- Components of Vector in Algebra
- Vector Joining Two Points in Algebra
- Section Formula in Vector Algebra
- Product of Two Vectors > Scalar (Dot) Product
- Product of Two Vectors > Vector (Cross) Product
- Direction Ratios, Direction Cosine & Direction Angles in Vector
- Scalar Triple Product
- Vector Triple Product
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
Definition: Adjoint of a Matrix
The adjoint of A is defined as the transpose (i.e. interchange rows and columns) of the cofactor matrix, and it is denoted by adj (A).
Maharashtra State Board: Class 12
Definition: Inverse of a Matrix
If A and B are non-singular square matrices of the same order such that AB = BA = I (where I is the identity matrix of the same order as A and B), then A and B are called inverses of each other.
We write A⁻¹ = B and B⁻¹ = A.
i.e. AA⁻¹ = A⁻¹A = I.
- If |A| ≠ 0, then A⁻¹ exists.
- If the inverse of a square matrix exists, then it is unique. A matrix can not have more than one distinct inverse.
Maharashtra State Board: Class 12
Formula: Inverse of a Square Matrix
By Adjoint Method: \[A^{-1}=\frac{\mathrm{adj}A}{|A|}\]
By Using Algebraic Equation: A matrix A and an algebraic equation in matrix A is in the form of A² + bA + C = O.
\[A^{-1}=\frac{1}{C}\left[-aA-bI\right]\]
Real Life Examples
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Inverse matrices are used in solving systems of equations, which appear in economics, statistics, and engineering applications.
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They help in reversing matrix operations, just as division reverses multiplication in ordinary numbers.
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In computer graphics and data transformations, inverse matrices help recover original positions after rotation or scaling operations.
Analogy:
An inverse matrix works like an “undo” operation for a matrix transformation, provided the matrix is non-singular.
Maharashtra State Board: Class 12
Key Points: Adjoint of a Matrix
- adj (AB) = (adj B) (adj A)
- (adj A)A = A (adj A) = |A| Iₙ
- (a) |adj A| = |A|ⁿ⁻¹, if |A| ≠ 0
(b) |adj A| = 0, if |A| = 0 - If |A| = 0, then (adj A) A = A (adj A) = O
- adj (Aᵐ) = (adj A)ᵐ, m ∈ N
- adj (kA) = kⁿ⁻¹ (adj A), k ∈ R
- adj (adj A) = |A|ⁿ⁻² A, A is non-singular matrix
- adj (adj A) = |A|ⁿ⁻² A, A is non-singular matrix
