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]
The optimal value of the objective function is attained at the ______ points of the feasible region.
A carpenter makes chairs and tables. Profits are ₹ 140 per chair and ₹ 210 per table. Both products are processed on three machines: Assembling, Finishing and Polishing. The time required for each product in hours and availability of each machine is given by the following table:
| Product → | Chair (x) | Table (y) | Available time (hours) |
| Machine ↓ | |||
| Assembling | 3 | 3 | 36 |
| Finishing | 5 | 2 | 50 |
| Polishing | 2 | 6 | 60 |
Formulate the above problem as LPP. Solve it graphically
