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JEE Maths
Class 12 Chapter 12

Must-Revise Concepts

Feasible and infeasible solutions

Graphical Method Linear Programming

Linear Programming

Marginal cost

Marginal Revenue

Non-trivial constraints

Terms of Linear Programming Problem

Class 12 Maths Chapters List:- JEE & NEET Revision Notes

Chapter 1 Relations and Functions

Chapter 2 Inverse Trigonometric Functions

Chapter 3 Matrices

Chapter 4 Determinants

Chapter 5 Continuity and Differentiability

Chapter 6 Application of Derivatives

Chapter 7 Integrals

Chapter 8 Application of Integrals

Chapter 9 Differential Equations

Chapter 10 Vector Algebra

Chapter 11 Three Dimensional Geometry

Chapter 12 Linear Programming

Chapter 13 Probability

Frequently Asked Questions

Linear Programming is widely used in production planning, transportation, scheduling, resource allocation, inventory management, finance, and business decision-making to achieve optimal outcomes.

The corner-point method is used because the optimal value of the objective function always occurs at one of the vertices (corner points) of the feasible region.

Linear Programming is a mathematical method used to maximize or minimize an objective function while satisfying a given set of linear constraints.

The feasible region is the common area that satisfies all the given linear inequalities and represents all possible solutions to the problem.

In a maximization problem, the objective function is optimized to obtain the highest value, whereas in a minimization problem, it is optimized to obtain the lowest value under the given constraints.

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Class 12 Maths Chapter 12: Linear Programming

Linear Programming (LP) is a mathematical technique used to determine the optimum value of an objective function subject to a set of linear constraints. It has extensive applications in business, economics, manufacturing, transportation, resource allocation, and operations research. This chapter introduces the graphical method for solving optimization problems involving two variables and helps students understand real-life decision-making using mathematics. It is an important and scoring chapter for the CBSE Board, while its concepts also strengthen analytical reasoning for competitive examinations like JEE Main and JEE Advanced.

At ALLEN, these revision notes simplify Linear Programming through concise theory, graphical interpretation, important formulas, solved examples, and exam-oriented strategies. These notes help students quickly revise key concepts and confidently solve optimization problems in board examinations.

1.0Chapter Snapshot

Parameter

Details

Difficulty Level

Easy–Moderate

Revision Time

2–3 Hours

Key Concepts Covered

Linear Constraints, Maximization and Minimization Problems, Corner Point Method

2.0Chapter 12 Smart Study Material by ALLEN Experts: Download PDFs

Resource

Status

NCERT Solutions

Available Soon

Important Questions

Available Soon

Formula Sheet PDF

Available Soon

Previous Years' Questions (PYQs)

Available Soon

Mock Test

Available Soon

Practice Worksheets

Available Soon

Quick Revision Notes PDF

Available Soon

Mind Maps

Available Soon

On this page


  • 1.0Chapter Snapshot
  • 2.0Chapter 12 Smart Study Material by ALLEN Experts: Download PDFs

Linear Programming

Linear Programming (LPP) is a powerful mathematical technique used for optimization, where the goal is to find the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. It plays a crucial role in various fields such as economics, business, engineering, and military applications.

1.0What is Linear Programming?

There are also some problems where we have to minimize a linear function subject to certain conditions determined by a set of linear inequalities with variables as non-negative. Such problems are called Linear Programming Problems.

Linear Programming Problems (LPPs) are optimization challenges where the goal is to minimize (or maximize) a linear function subject to a set of linear inequalities and conditions, with the variables constrained to be non-negative. These problems are fundamental in various fields, allowing for the efficient allocation of resources, cost reduction, and strategic planning within defined limits.

Thus, a Linear Programming Problem is one that is concerned with finding the optimal value (maximum or minimum value) of a linear function (called objective function) of several variables (say x and y), subject to the conditions that the variables are non-negative and satisfy a set of linear inequalities (called linear constraints). The term linear implies that all the mathematical relations used in the problem are linear relations while the term programming refers to the method of determining a particular programme or plan of action.

A Linear Programming Problem (LPP) involves the following components:

  • Objective: Finding the optimal value (maximum or minimum) of a linear function, known as the objective function.
  • Variables: The objective function is dependent on several variables (e.g., x and y).
  • Non-negativity: The variables are constrained to be non-negative.
  • Constraints: The variables must satisfy a set of linear inequalities, referred to as linear constraints.

2.0How to Solve Linear Programming Problems?

Solving Linear Programming Problems (LPP) involves several systematic steps to find the optimal solution, either to maximize or minimize the objective function subject to given constraints. Here is a step-by-step guide:

  1. Formulate the Problem
  • Define the Objective Function: Clearly state the linear function to be maximized or minimized.

Example: Maximize Z = 3x + 4y

  • List the Constraints: Identify all the linear inequalities or equations that constrain the variables.

​x+2y≤83x+y≤6x,y≥0​

  • Identify Non-negativity Constraints: Ensure that all variables are non-negative x≥0, y≥0.
  1. Graphical Method (for Two-Variable Problems)
  • Plot the Constraints: Draw the lines corresponding to each linear inequality on a graph.
  • Determine the Feasible Region: Identify the area where all constraints overlap; this is the feasible region.
  • Find the Vertices: Determine the corner points (vertices) of the feasible region.
  • Evaluate the Objective Function: Calculate the value of the objective function at each vertex.
  • Identify the Optimal Solution: Choose the vertex that gives the maximum or minimum value of the objective function.

Also Read : Graphical Method Linear Programming

  1. Simplex Method (for Larger Problems)
  • Set Up the Initial Simplex Tableau: Construct a tableau with the objective function and constraints.
  • Identify the Pivot Element: Choose the element to pivot based on specific rules (e.g., the most negative value in the bottom row for maximization).
  • Perform Pivot Operations: Adjust the tableau by performing row operations to change the pivot element to 1 and all other elements in the pivot column to 0.
  • Iterate: Repeat the pivot operations until there are no negative indicators in the bottom row (for maximization) or no positive indicators (for minimization).
  • Read the Solution: Extract the solution from the final tableau where the optimal values of the variables are found.
  1. Dual Simplex Method
  • Used when the initial solution is infeasible, but optimality conditions are satisfied.
  • Adjusts infeasibilities iteratively until a feasible and optimal solution is found.

On this page


  • 1.0What is Linear Programming?
  • 2.0How to Solve Linear Programming Problems?

1.0Linear Programming Solved Examples

Example 1: 

A furniture dealer specializes in selling only two products: tables and chairs. He has Rs. 5000 available to invest and can store up to 60 pieces in total. The cost to purchase a table is Rs. 250, while a chair costs Rs. 50. The dealer can make a profit of Rs. 50 on each table and Rs. 15 on each chair. Assuming that he can sell all the items he purchases, how should he allocate his investment to maximize his profit?

Solution:

This problem can be formulated as below.

Let x and y be the required numbers of tables and chairs respectively. Then, clearly, we have

x ≥ 0,

y ≥ 0; 

x + y ≤ 60;

250x + 50y ≤ 5000. i.e., 5x+ y ≤ 100

Let P be the profit function. Then, P = 50 + 15y.

Now, we have to maximize P

Now, x+y=60⇒60x​+60y​=1. 

This line meets the axes at (60, 0) and (0, 60). Plot these points and join them to get the line x + y = 60 

Linear programming practice problems

Also, 5x + y = 100⇒20x​+100y​=1 .  

This line meets the axes at (20, 0) and (0, 100). Plot these points and

join them to get the line 5x + y = 100.

Also, the line x = 0 is the y-axis and the line y = 0 is the x-axis.

These four straight lines enclose the quadrilateral OABC.

The coordinates of the points O, A, B, C are (0, 0), (20, 0), (10, 50) and (0, 60) respectively.

At these points, the corresponding values of P = 50x + 50 +15y are 0, 1000, 1250 and 900 respectively. Clearly, it is maximum at B (10, 50). 

In other words, we can say that

Maximize. P = 50x + 15y

X + y ≤ 6 0

250x + 50y ≤ 5000

x ≥ 0, y ≥ 0.

Points

Corner points

Corresponding Value of F

O

(0, 0)

0

A

(20,0)

1000

B

(10,50)

1250 → Maximize

C

(0,60)

900

So, for a maximum profit, the dealer should purchase 10 tables and 50 chairs


Example 2: 

A young man rides his motorcycle at two different speeds: 25 km per hour, costing him Rs. 2 per kilometer for petrol, and 40 km per hour, costing Rs. 5 per kilometer. With Rs. 100 available for petrol, he wants to determine the maximum distance he can cover within one hour. Frame this scenario as a linear programming problem and then solve it.

Solution:

Suppose that the young man rides x km at 25 km per hour and y km at 40 km per hour. Then, we have to maximize P = x + y.

Clearly:

x ≥ 0, 

y ≥ 0, 

2x + 5y ≤ 100.

Since the available time is at most one hour, we have

25x​+40y​≤1 or  8x + 5y ≤ 200

Sample solution for linear programming

Now, we solve the system of the inequations.

2x + 5y = 100 ⇒50x​+20y​=1 .

This line meets the axes at (50, 0) and (0, 20). Plot these points and join them to get the line 2x + 5y = 100.

In other words, 

Maximize Z = x + y

8x + 5y ≤ 200

2x + 5y ≤ 100

x ≥ 0, y ≥ 0

Points

Corner points

Corresponding Value of Z

O

(0, 0)

0

A

(25,0)

25

B

\left(\frac{50}{3}, \frac{40}{3}\right)

30 ← Maximize

C

(0,20)

20

So, Z = x + y is maximum when x=350​ and y=340​ . Thus, the young man can cover the maximum distance of 30 km, if he rides 350​  km at 25 km/h and 340​ km at 40 km/h 


Example 3: 

Suppose every gram of wheat provides 01. g of proteins and 0 25. g of carbohydrates, and the corresponding values for rice are 0 05. g and 0 5. g respectively. Wheat costs RS. 5 and rice RS. 20 per kilogram. The minimum daily requirements of proteins and carbohydrates for an average man are 50 g and 200 g respectively. In what quantities should wheat and rice be combined in the daily diet to provide the minimum daily requirements of proteins and carbohydrates at minimum cost, assuming that both wheat and rice are to be taken in the diet? 

Solution:

Let x g of wheat and y g of rice be mixed to fulfill the requirements. Then, we have to minimize the cost function.

Z=10005x​+100020y​, i.e., Z=200x​+50y​ …(i)

x g of wheat and y g of rice must give at least 50 g of proteins.

So, we must have

0.1 x + 0.05 y ≥ 50 or 2x + y ≥ 1000.

Similarly, x g of wheat and y g of rice must give at least 200 g of carbohydrates.

So, we must have

0.25x +0.5y ≥ 200 or x + 2y ≥ 800.

Thus, we have to minimize 

Z=200x​+50y​ , subject to the constraints

x > 0, 

y > 0, 

2x + y ≥ 1000 and 

x + 2y ≥ 800.

2x + y = 1000 ⇒ 500x​+1000y​=1 

Examples on linear programming

Minimize

Z=10005x​+100020y​

i.e. Z=200x​+50y​

2x +y ≥ 1000

x +2y ≥ 800

Points

Corner points

Corresponding Value of Z

C

(800,0)

4

E

(400, 200)

6

B

(1000, 0)

4

Clearly, at B we have x = 0, and at C we have y = 0. Also, the value of Z at E (400, 200) = 6. So, we must have 400 g of wheat and 200 g of rice


Example 4: 

A chemical industry manufactures two compounds, A and B. The table below shows the amount of ingredients C and D (in units per kg) required for compounds A and B, along with the minimum requirements for C and D, and the costs per kg of compounds A and B.


Compound (in units)

Minimum requirement

(in units)

Ingredient C (per kg)

1

2

80

Ingredient D (Per kg)

3

1

75

Cost per Kg (in (Rs)

4

6


Find the quantities of A and B which would minimize the cost.

Solution:

Let x kg of A and y kg of B be produced. Then,

x ≥ 0, y ≥ 0, x + 2y ≥ 80 and 3x +y ≥ 75.

Then the cost function is given by Z = 4x + 6y.

Minimize Z = 4x + 6y.

Subject to the constraints.

X +2y ≥ 80

3x +y ≥ 75

x ≥ 0. y ≥ 0. 

Example problems on linear inequalities

Points

Corner points

Corresponding Value of Z

P

(0,75)

450

Q

(14,33)

254 ← Minimum

R

(80, 0)

320

Thus Z is minimum at Q (14,33)

Hence, for a minimum cost, 14 kg of a and 33 kg of B must be taken.

2.0Linear Programming Practice Problems

1. A man has Rs. 1500 to spend on purchasing rice and wheat. A bag of rice costs Rs. 180, and a bag of wheat costs Rs. 120. With a storage capacity of only 10 bags, he earns a profit of Rs. 11 per bag of rice and Rs. 8 per bag of wheat. How many bags of each should he buy to maximize his profit? 

2. A manufacturer produces nuts and bolts for industrial machinery. To produce a packet of nuts, it requires 1 hour on machine A and 3 hours on machine B, while producing a packet of bolts requires 3 hours on machine A and 1 hour on machine B. The manufacturer earns a profit of Rs. 17.50 per packet of nuts and Rs. 7 per packet of bolts. If the machines can operate for a maximum of 12 hours each day, how many packets of each should be produced daily to maximize profit? Additionally, calculate the maximum profit achievable.

3. A dealer wishes to purchase a number of fans and sewing machines. He has only ` Rs.5760 to invest and has space for at most 20 items. A fan costs him Rs. 360 and a sewing machine, Rs. 240. He expects to gain Rs.22 on a fan and Rs.18 on a sewing machine. Assuming that he can sell all the items he can buy, how should he invest the money in order to maximize the profit?

Also Read : Linear Inequalities Questions

1.0Learning Outcomes

After completing this chapter, you will be able to:

  • Understand the concept of linear programming.
  • Formulate linear constraints from word problems.
  • Draw the feasible region graphically.
  • Identify the objective function.
  • Determine the optimal solution using the corner-point method.
  • Interpret optimization problems in real-life situations.
  • Solve CBSE Board and concept-based competitive exam questions confidently.

2.05-Minute Quick Revision

Must-Revise Concepts

  • Linear Programming Problem (LPP)
  • Objective Function
  • Linear Constraints
  • Feasible Region
  • Convex Region
  • Corner Point Method
  • Optimal Solution
  • Maximization Problems
  • Minimization Problems
  • Infeasible and Unbounded Regions

Important Formula Recall

  • Objective Function: Z=ax+by
  • General Linear Constraint: ax+by≤c,ax+by≥c,ax+by=c
  • Non-Negativity Constraints: x≥0,y≥0

Important Properties / Shortcut Tips

  • Convert every word problem into mathematical inequalities first.
  • Draw all constraint lines accurately before shading.
  • The feasible region is the common shaded region satisfying all constraints.
  • Evaluate the objective function only at the corner points.
  • The optimum solution always occurs at a corner point of the feasible region.

3.0High Weightage Topics

Topic

JEE Importance

Difficulty Level

Linear Constraints

High

Easy

Feasible Region

Very High

Easy–Moderate

Objective Function

High

Easy

Corner Point Method

Very High

Moderate

Maximization Problems

High

Moderate

Minimization Problems

High

Moderate

Real-Life Optimization Problems

Moderate

Moderate

4.0Formula Handbook

Available soon

5.0Common Mistakes & JEE Tips

Common Mistake

Correct Approach

Incorrect graph plotting

Draw all constraint lines accurately using intercepts.

Wrong shading of inequalities

Verify the correct half-plane using a test point.

Ignoring non-negativity constraints

Always consider (x \ge 0) and (y \ge 0).

Missing corner points

Identify every vertex of the feasible region before evaluation.

Evaluating objective function at random points

Evaluate only at the corner points of the feasible region.

Arithmetic errors during optimization

Double-check calculations for every corner point.

Misinterpreting the final answer

State both the optimum value and the corresponding values of the variables.

6.0ALLEN Faculty Tips

  • Practice graph plotting before attempting optimization problems.
  • Learn to convert word problems into mathematical constraints.
  • Identify feasible regions carefully.
  • Always evaluate the objective function at every corner point.
  • Revise different types of inequality graphs regularly.
  • Solve Previous Years' Questions (PYQs) to improve speed and accuracy.

7.0PYQ Trend Analysis

Linear Programming is a scoring chapter in the CBSE Board examination. Questions are generally based on graphical representation, feasible regions, and finding maximum or minimum values using the corner-point method.

Note: The trend below is based on the analysis of previous years' CBSE and competitive examination papers. The exact number of questions may vary each year.

Topic

Questions Asked (Last 5 Years)

Average Difficulty

Linear Constraints

1

Easy

Feasible Region

1

Easy–Moderate

Corner Point Method

1–2

Moderate

Maximization Problems

1

Moderate

Minimization Problems

1

Moderate

8.0Most Common Question Types

  • Formulating linear constraints from word problems.
  • Drawing the feasible region graphically.
  • Identifying the corner points of the feasible region.
  • Finding the maximum value of the objective function.
  • Finding the minimum value of the objective function.
  • Determining whether a feasible region exists.
  • Solving real-life optimization problems using linear programming.
  • Interpreting the optimal solution in practical contexts.

9.0Smart Revision Strategy

  1. Revise the concept of linear inequalities.
  2. Practice plotting constraint lines accurately.
  3. Learn how to identify feasible regions.
  4. Revise the corner-point method.
  5. Solve both maximization and minimization problems.
  6. Practice application-based word problems.
  7. Solve Previous Years' Questions (PYQs).
  8. Finish revision with a chapter-wise mock test.

On this page


  • 1.0Learning Outcomes
  • 2.05-Minute Quick Revision
  • 2.1Must-Revise Concepts
  • 2.2Important Formula Recall
  • 2.3Important Properties / Shortcut Tips
  • 3.0High Weightage Topics
  • 4.0Formula Handbook
  • 5.0Common Mistakes & JEE Tips
  • 6.0ALLEN Faculty Tips
  • 7.0PYQ Trend Analysis
  • 8.0Most Common Question Types
  • 9.0Smart Revision Strategy