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
- Derivative of Composite Functions
- Geometrical Meaning of Derivative
- Derivatives of Inverse Functions
- Logarithmic Differentiation
- Derivatives of Implicit Functions
- Derivatives of Parametric Functions
- 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
- Indefinite Integration with Standard Indefinite Integral Formulae
- Methods of Integration> Integration by Substitution
- Methods of Integration> Integration by Parts
- Methods of Integration> Integration Using Partial Fraction
- Overview of 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
- Differential Equations
- Order and Degree of a Differential Equation
- Formation of Differential Equations
- Homogeneous 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
- Bernoulli Trial
- Mean and Variance of Binomial Distribution
- Probability using Binomial Distribution
- Overview of Binomial Distribution
- Meaning of Linear Programming Problem
- Mathematical formulation of a linear programming problem
- Familiarize with terms related to Linear Programming Problem
Defintion: Linear Programming Problem (L.P.P.)
A linear programming problem (LPP) is one that is concerned with finding the optimal value (maximum or minimum) of a linear function of several variables, subject to constraints that the variables are non-negative and satisfy a set of linear inequalities.
Maximise / Minimise:
z = c₁x₁ + c₂x₂ + ... + cₙxₙ
Subject to constraints:
a₁₁x₁ + a₁₂x₂ + ... + a₁ₙxₙ (≤, =, ≥) b₁
a₂₁x₁ + a₂₂x₂ + ... + a₂ₙxₙ (≤, =, ≥) b₂
.
.
.
... aₘ₁x₁ + aₘ₂x₂ + ... + aₘₙxₙ (≤, =, ≥) bₘ
x₁, x₂, x₃, ..., xₙ ≥ 0
Objective function:
The function z = c₁x₁ + c₂x₂ + ... + cₙxₙ is called the objective function.
Key Points: Linear Programming Problem (L.P.P.)
| Term | Meaning |
|---|---|
| Decision Variables | Variables we need to find (like x, y) |
| Objective Function | Function to maximise or minimise (z = c₁x + c₂y) |
| Constraints | Conditions/restrictions given (inequalities like ax + by ≤ c) |
| Non-negativity Constraints | Variables cannot be negative (x ≥ 0, y ≥ 0) |
| Feasible Solution | Any solution that satisfies all constraints |
| Infeasible Solution | Does NOT satisfy constraints |
| Feasible Region | Area containing all feasible solutions |
| Optimal Solution | Best solution (max or min value) |
| Optimum Value | Value of the objective function at the optimal solution |
| Bounded Region | Region that is closed (limited area) |
| Unbounded Region | A region that extends infinitely |
| Corner Point (Extreme Point) | Intersection points of boundary lines |
| Optimal Feasible Solution | Feasible solution giving the best value of z |
Related QuestionsVIEW ALL [102]
A company produces mixers and food processors. Profit on selling one mixer and one food processor is Rs 2,000 and Rs 3,000 respectively. Both the products are processed through three machines A, B, C. The time required in hours for each product and total time available in hours per week on each machine arc as follows:
| Machine | Mixer | Food Processor | Available time |
| A | 3 | 3 | 36 |
| B | 5 | 2 | 50 |
| C | 2 | 6 | 60 |
How many mixers and food processors should be produced in order to maximize the profit?
