Linear Programming Class 12 Notes

Linear Programming (LP) is an optimisation technique used to find the best outcome (maximum profit or minimum cost) given constraints. This chapter carries 5 marks in CBSE boards and is one of the most scoring chapters — once you master the graphical method, you can solve any LP problem!

Key Concepts

1. What is Linear Programming?

It is a method to achieve the best outcome (maximum or minimum) in a mathematical model whose requirements are represented by linear relationships.

TermMeaningExample
Objective FunctionThe function to be maximised or minimisedZ = 3x + 5y (profit function)
Decision VariablesVariables we controlx = units of A, y = units of B
ConstraintsLimitations/restrictions (linear inequalities)2x + y ≤ 100 (machine hours)
Non-negativityVariables can’t be negativex ≥ 0, y ≥ 0
Feasible RegionSet of all points satisfying all constraintsShaded region on graph
Feasible SolutionAny point in the feasible region(10, 20)
Optimal SolutionFeasible solution that optimises ZThe point giving max/min Z

2. Corner Point Method (Steps)

Step 1: Write the objective function and constraints from the problem
Step 2: Graph each constraint as an equation (line), shade the feasible region
Step 3: Find all corner points (vertices) of the feasible region
Step 4: Calculate Z at each corner point
Step 5: The corner point giving max/min Z is the optimal solution
💡 Fundamental Theorem: If the optimal solution exists, it occurs at a corner point (vertex) of the feasible region. You never need to check interior points!

3. Types of Feasible Regions

TypeDescriptionOptimal Solution
BoundedEnclosed region (finite area)Always exists (both max and min)
UnboundedRegion extends infinitelyMay or may not exist — need verification
💡 For unbounded regions: If you get Z = M at a corner point for maximisation, check if the half-plane Z > M has any point in common with the feasible region. If NO common point exists, then M is the maximum. Otherwise, no maximum exists.

4. Types of LP Problems in CBSE

TypeObjectiveCommon Scenario
ManufacturingMaximise profitUnits of products A and B
Diet ProblemMinimise costAmount of food items for nutrition
TransportationMinimise costShipping goods from factories to warehouses
AllocationMaximise outputDistributing resources

5. Graphing Tips

  • Convert inequality to equation to plot the line
  • Use test point (0,0) to determine which side to shade
  • Find intersection points by solving pairs of equations simultaneously
  • Don’t forget non-negativity constraints (x ≥ 0, y ≥ 0) — this restricts the region to the first quadrant

Important Definitions

TermDefinition
Linear ProgrammingOptimisation technique with linear objective function and linear constraints
Feasible RegionSet of all points satisfying all constraints simultaneously
Optimal SolutionA feasible solution at which the objective function has max or min value
Corner PointVertex of the feasible region where constraint boundaries intersect
Bounded RegionFeasible region that can be enclosed in a circle

Solved Examples — NCERT-Based

Example 1: Manufacturing Problem

A factory makes two products A and B. Each A requires 2 hrs on machine I and 1 hr on machine II. Each B requires 1 hr on machine I and 3 hrs on machine II. Machine I is available for 10 hrs, machine II for 12 hrs. Profit on A = ₹40, on B = ₹60. Find the maximum profit.

Solution:

Let x = units of A, y = units of B

Maximise Z = 40x + 60y

Subject to: 2x + y ≤ 10, x + 3y ≤ 12, x ≥ 0, y ≥ 0

Corner points: (0,0), (5,0), (0,4), and intersection of 2x+y=10 and x+3y=12

Solving: 2x+y=10 → y=10−2x → x+3(10−2x)=12 → x+30−6x=12 → −5x=−18 → x=18/5=3.6, y=2.8

Corner points and Z values:

Corner PointZ = 40x + 60y
(0, 0)0
(5, 0)200
(3.6, 2.8)144 + 168 = 312
(0, 4)240

Maximum profit = ₹312 at (3.6, 2.8)

Example 2: Diet Problem

A diet requires at least 10 units of vitamin A and 12 units of vitamin B. Food I costs ₹6/unit and provides 2 units of A, 1 unit of B. Food II costs ₹10/unit and provides 1 unit of A, 4 units of B. Find minimum cost.

Solution:

Let x = units of Food I, y = units of Food II

Minimise Z = 6x + 10y

Subject to: 2x + y ≥ 10, x + 4y ≥ 12, x ≥ 0, y ≥ 0

Corner points: (0, 10), (12, 0), intersection of 2x+y=10 and x+4y=12

Solving: y = 10−2x → x + 4(10−2x) = 12 → x + 40−8x = 12 → −7x = −28 → x=4, y=2

Corner PointZ = 6x + 10y
(0, 10)100
(4, 2)44
(12, 0)72

Minimum cost = ₹44 at x = 4 units of Food I, y = 2 units of Food II

Example 3: Maximise Z = 5x + 3y subject to 3x + 5y ≤ 15, 5x + 2y ≤ 10, x ≥ 0, y ≥ 0

Solution:

Corner points: (0,0), (2,0), (0,3)

Intersection: 3x+5y=15 and 5x+2y=10

From 5x+2y=10: y = (10−5x)/2. Substituting: 3x + 5(10−5x)/2 = 15 → 6x + 50−25x = 30 → −19x = −20 → x = 20/19, y = 45/19

Corner PointZ = 5x + 3y
(0, 0)0
(2, 0)10
(20/19, 45/19)100/19 + 135/19 = 235/19 ≈ 12.37
(0, 3)9

Maximum Z = 235/19 ≈ 12.37 at (20/19, 45/19)

Example 4: Minimise Z = x + 2y subject to 2x + y ≥ 3, x + 2y ≥ 6, x ≥ 0, y ≥ 0

Solution:

Corner points of unbounded feasible region: (6, 0), (0, 3)

Intersection: 2x+y=3 and x+2y=6 → y=3−2x → x+2(3−2x)=6 → x+6−4x=6 → x=0, y=3

Also check: (6, 0)

Corner PointZ = x + 2y
(6, 0)6
(0, 3)6

Minimum Z = 6 (achieved at both points and all points on the line segment joining them)

Important Questions for Board Exams

1 Mark Questions

  1. Define a feasible region in an LP problem.
  2. What is the corner point theorem?

2 Mark Questions

  1. Draw the feasible region for: x + y ≤ 4, x ≥ 0, y ≥ 0 and find its corner points.
  2. If the feasible region is bounded with vertices (0,0), (4,0), (0,3), find max Z = 7x + 4y.

5 Mark Questions

  1. Maximise Z = 3x + 2y subject to x + 2y ≤ 10, 3x + y ≤ 15, x ≥ 0, y ≥ 0. Graph the constraints and find optimal solution.
  2. A dealer deals in two products A and B. He has ₹30,000 to invest and space for 20 items. Product A costs ₹2,500, B costs ₹500. He can sell A at ₹500 profit and B at ₹150 profit. Formulate the LP problem and solve graphically for maximum profit.

Quick Revision Points

  • LP = optimise a linear function subject to linear constraints
  • Always check corner points — optimal solution is always at a vertex
  • Steps: Formulate → Graph → Find corners → Evaluate Z → Pick best
  • Bounded region → max and min both exist
  • Unbounded region → may need extra verification
  • Non-negativity (x ≥ 0, y ≥ 0) restricts to first quadrant
  • If Z has same value at two corners → all points on that edge are optimal
  • CBSE mostly asks 5-mark graphical problems — practice drawing accurate graphs!

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