CBSE Class 12 Maths Exemplar Chapter 12: Linear Programming NCERT Solutions

NCERT Solutions PDF Class 12 PDF

CBSE Class 12 Maths Exemplar Chapter 12 introduces Linear Programming, a crucial topic for optimization problems. This chapter's NCERT Solutions guide students through identifying feasible regions and corner points, essential steps for solving linear programming problems. You'll learn to optimize objective functions, such as Z = 11x + 7y and Z = 3x + 4y, subject to a variety of linear constraints like 2x + y ≤ 6, x ≤ 2, x + y ≤ 1, x ≤ 3, and y ≤ 2, always considering the non-negativity constraints x ≥ 0 and y ≥ 0. The solutions emphasize the graphical method and the corner point method, providing clear, step-by-step explanations. This approach ensures a solid understanding of how to find maximum or minimum values for given functions within defined boundaries, making it an excellent resource for exam preparation and reinforcing core concepts.

Quick info

BoardCBSE
ClassClass 12
SubjectMaths Exemplar
Session2026
LanguageEnglish
TypeNCERT Solutions
ChapterChapter 12

Chapter summary

Chapter 12 on Linear Programming for CBSE Class 12 Maths Exemplar focuses on solving optimization problems. The NCERT Solutions provided here guide students through identifying the feasible region defined by linear inequalities, finding the coordinates of the corner points of this region, and evaluating the objective function at these points to determine the maximum or minimum value. The solutions emphasize the graphical method and the corner point method, crucial for tackling problems involving maximizing or minimizing linear functions under given constraints.

Learning outcomes

  • Understand the concept of linear programming and its applications.
  • Identify and graph linear inequalities to determine the feasible region.
  • Determine the corner points of a bounded feasible region.
  • Apply the corner point method to find the maximum or minimum value of a linear objective function.
  • Solve optimization problems with given linear constraints.

Topics covered

Paper topics

  • Linear Programming
  • Objective Function
  • Constraints
  • Feasible Region
  • Corner Points
  • Graphical Method
  • Optimization
  • Maximization
  • Minimization
  • Linear Inequalities

Important topics

  • Identifying the Feasible Region
  • Finding Corner Points
  • Evaluating Objective Function at Corner Points
  • Determining Maximum/Minimum Value

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Questions and Solutions

Question 1

Determine the maximum value of the objective function Z = 11x + 7y subject to the following constraints: 2x + y \le 6, x \le 2, x \ge 0, and y \ge 0.
Solution:

We are asked to maximize the objective function Z = 11x + 7y subject to the given constraints:

  1. 2x + y \le 6
  2. x \le 2
  3. x \ge 0
  4. y \ge 0

First, we identify the feasible region by graphing these inequalities. The region is bounded by the lines 2x + y = 6, x = 2, x = 0 (y-axis), and y = 0 (x-axis). The corner points of this feasible region (OABC) are found by the intersection of these lines.

The corner points are:

  • O: Intersection of x = 0 and y = 0, which is (0, 0).
  • A: Intersection of y = 0 and x = 2, which is (2, 0).
  • B: Intersection of x = 2 and 2x + y = 6. Substituting x = 2 into 2x + y = 6 gives 2(2) + y = 6, so 4 + y = 6, which means y = 2. Thus, point B is (2, 2).
  • C: Intersection of x = 0 and 2x + y = 6. Substituting x = 0 gives 2(0) + y = 6, so y = 6. Thus, point C is (0, 6).

Now, we evaluate the objective function Z = 11x + 7y at each corner point:

  • At O(0, 0): Z = 11(0) + 7(0) = 0
  • At A(2, 0): Z = 11(2) + 7(0) = 22
  • At B(2, 2): Z = 11(2) + 7(2) = 22 + 14 = 36
  • At C(0, 6): Z = 11(0) + 7(6) = 42

Comparing these values, the maximum value of Z is 42, which occurs at the point (0, 6).

Hence, the maximum value of Z is 42.

Question 2

Maximise the objective function Z = 3x + 4y, subject to the constraints x + y \le 1, x \ge 0, and y \ge 0.
Solution:

We need to maximize Z = 3x + 4y subject to the constraints:

  1. x + y \le 1
  2. x \ge 0
  3. y \ge 0

The feasible region is determined by these inequalities. The boundary lines are x + y = 1, x = 0 (y-axis), and y = 0 (x-axis). This forms a bounded region OAB.

The corner points of the feasible region are:

  • O: Intersection of x = 0 and y = 0, which is (0, 0).
  • A: Intersection of y = 0 and x + y = 1. Substituting y = 0 gives x + 0 = 1, so x = 1. Thus, point A is (1, 0).
  • B: Intersection of x = 0 and x + y = 1. Substituting x = 0 gives 0 + y = 1, so y = 1. Thus, point B is (0, 1).

Now, we evaluate the objective function Z = 3x + 4y at each corner point:

  • At O(0, 0): Z = 3(0) + 4(0) = 0
  • At A(1, 0): Z = 3(1) + 4(0) = 3
  • At B(0, 1): Z = 3(0) + 4(1) = 4

Comparing these values, the maximum value of Z is 4, which occurs at the point (0, 1).

Hence, the maximum value of Z is 4.

Question 3

Maximise the objective function Z = 11x + 7y, subject to the constraints x \le 3, y \le 2, x \ge 0, and y \ge 0.
Solution:

We need to maximize Z = 11x + 7y subject to the constraints:

  1. x \le 3
  2. y \le 2
  3. x \ge 0
  4. y \ge 0

The feasible region is defined by these inequalities. The boundary lines are x = 3, y = 2, x = 0 (y-axis), and y = 0 (x-axis). This forms a rectangle, which is a bounded region OABC.

The corner points of the feasible region are:

  • O: Intersection of x = 0 and y = 0, which is (0, 0).
  • A: Intersection of y = 0 and x = 3, which is (3, 0).
  • B: Intersection of x = 3 and y = 2, which is (3, 2).
  • C: Intersection of x = 0 and y = 2, which is (0, 2).

Now, we evaluate the objective function Z = 11x + 7y at each corner point:

  • At O(0, 0): Z = 11(0) + 7(0) = 0
  • At A(3, 0): Z = 11(3) + 7(0) = 33
  • At B(3, 2): Z = 11(3) + 7(2) = 33 + 14 = 47
  • At C(0, 2): Z = 11(0) + 7(2) = 14

Comparing these values, the maximum value of Z is 47, which occurs at the point (3, 2).

Hence, the maximum value of Z is 47.

Common mistakes

  • Incorrectly graphing the linear inequalities.
  • Failing to identify all corner points of the feasible region.
  • Errors in calculating the objective function's value at corner points.
  • Confusing maximization with minimization problems.

Revision tips

  • Practice graphing each inequality carefully to accurately define the feasible region.
  • Always list all corner points of the feasible region before evaluating the objective function.
  • Double-check your calculations for the objective function at each corner point.
  • Understand the difference between the constraints and the objective function.

Practice MCQs

Q1. What is the objective of linear programming?

Q2. Which method is commonly used to find the optimal solution in linear programming problems with a bounded feasible region?

Q3. If a feasible region is bounded, where does the optimal solution (maximum or minimum) of the objective function occur?

Q4. What does the region defined by the constraints in a linear programming problem represent?

Frequently asked questions

What is Linear Programming?

Linear Programming is a mathematical technique used to optimize a linear objective function subject to a set of linear constraints. It helps in finding the best possible outcome (maximum or minimum) under given conditions.

How do you find the feasible region in Linear Programming?

The feasible region is found by graphing all the linear inequalities (constraints) on a coordinate plane. The region where all shaded areas overlap represents the feasible region.

What is the role of corner points in Linear Programming?

For a bounded feasible region, the maximum or minimum value of the objective function occurs at one of the corner points (vertices) of the feasible region. These points are evaluated to find the optimal solution.

Are these solutions suitable for CBSE Class 12 Maths Exemplar?

Yes, these solutions are specifically designed for CBSE Class 12 Maths Exemplar, Chapter 12 on Linear Programming, providing clear explanations for the problems.

How can these NCERT Solutions help in exam preparation?

These solutions offer step-by-step guidance and accurate answers, helping students understand the concepts and methods required for solving linear programming problems, thereby aiding in effective exam revision.

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