CBSE Class 12 Mathematics: Linear Programming NCERT Solutions

NCERT Solutions PDF Class 12 PDF

CBSE Class 12 Mathematics, Chapter 12: Linear Programming, delves into optimizing linear objective functions under given linear constraints. This chapter introduces the core concepts of defining an objective function, which represents the quantity to be maximized or minimized, and constraints, which are the limitations expressed as linear inequalities. Students will master the graphical method to visualize these constraints, thereby identifying the feasible region – the area representing all possible solutions. The solutions focus on finding the corner points (vertices) of this feasible region and evaluating the objective function at each vertex. This process allows for the determination of the maximum or minimum value of the objective function, providing the optimal solution to the problem. Understanding these techniques is vital for developing problem-solving skills in optimization and for performing well in examinations.

Quick info

BoardCBSE
ClassClass 12
SubjectMathematics
Session2026
LanguageEnglish
TypeNCERT Solutions
ChapterLinear Programming

Chapter summary

This chapter focuses on the graphical method for solving linear programming problems. It covers how to define an objective function and constraints, determine the feasible region by plotting the inequalities, identify the corner points of this region, and evaluate the objective function at these points to find the maximum or minimum value. The exercises provide practice in applying these steps to various maximization and minimization problems.

Learning outcomes

  • Understand the concept of linear programming and its applications.
  • Identify and formulate linear programming problems.
  • Graphically determine the feasible region for a system of linear inequalities.
  • Find the corner points of the feasible region.
  • Calculate the maximum and minimum values of a linear objective function over a feasible region.
  • Apply the graphical method to solve optimization problems.

Topics covered

Paper topics

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

Important topics

  • Graphical determination of the feasible region
  • Identification of corner points
  • Evaluating the objective function at corner points
  • Finding maximum and minimum values
  • Formulating linear programming problems

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Questions and Solutions

Question 1

Maximise Z = 3x + 4y

Subject to the constraints:

x + y \le 4, x \ge 0, y \ge 0
Solution:

We are asked to maximise the objective function Z = 3x + 4y subject to the constraints x + y \le 4, x \ge 0, and y \ge 0.

First, we identify the feasible region defined by these constraints. The constraint x \ge 0 and y \ge 0 restrict our attention to the first quadrant of the coordinate plane.

The line x + y = 4 passes through the points (4, 0) and (0, 4). Since the inequality is x + y \le 4, the feasible region lies below or on this line.

The feasible region is a triangle with vertices at the origin O (0, 0), point A on the x-axis, and point B on the y-axis.

To find the coordinates of A, we set y = 0 in x + y = 4, which gives x = 4. So, A is (4, 0).

To find the coordinates of B, we set x = 0 in x + y = 4, which gives y = 4. So, B is (0, 4).

The corner points of the feasible region are O(0, 0), A(4, 0), and B(0, 4).

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(4, 0): Z = 3(4) + 4(0) = 12
  • At B(0, 4): Z = 3(0) + 4(4) = 16

Comparing the values of Z, the maximum value is 16, which occurs at the point B(0, 4).

Therefore, the maximum value of Z is 16 at the point (0, 4).

Question 2

Minimise Z = -3x + 4y

subject to the constraints:

x+2y \le 8 3x+2y \le 12 x \ge 0 y \ge 0
Solution:

We need to minimise the objective function Z = -3x + 4y subject to the constraints x+2y \le 8, 3x+2y \le 12, x \ge 0, and y \ge 0.

The constraints x \ge 0 and y \ge 0 restrict the feasible region to the first quadrant.

Consider the boundary lines:

  1. x + 2y = 8: This line passes through (8, 0) and (0, 4).
  2. 3x + 2y = 12: This line passes through (4, 0) and (0, 6).

The feasible region is the area that satisfies all inequalities. We find the intersection points of these lines.

The corner points are:

  • The origin O(0, 0).
  • The x-intercept of 3x + 2y = 12 is when y=0, so 3x = 12 \Rightarrow x = 4. This point is A(4, 0).
  • The y-intercept of x + 2y = 8 is when x=0, so 2y = 8 \Rightarrow y = 4. This point is C(0, 4). Note that (0,4) satisfies 3x+2y \le 12 as 3(0)+2(4)=8 \le 12.
  • The intersection of x + 2y = 8 and 3x + 2y = 12. Subtracting the first equation from the second gives (3x + 2y) - (x + 2y) = 12 - 8, which simplifies to 2x = 4, so x = 2. Substituting x = 2 into x + 2y = 8 gives 2 + 2y = 8, so 2y = 6, and y = 3. This intersection point is B(2, 3).

The corner points of the feasible region are O(0, 0), A(4, 0), B(2, 3), and C(0, 4).

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(4, 0): Z = -3(4) + 4(0) = -12
  • At B(2, 3): Z = -3(2) + 4(3) = -6 + 12 = 6
  • At C(0, 4): Z = -3(0) + 4(4) = 16

Comparing these values, the minimum value of Z is -12, which occurs at the point A(4, 0).

Therefore, the minimum value of Z is -12 at the point (4, 0).

Question 3

Maximise Z = 5x + 3y

subject to the constraints:

3x + 5y \le 15 5x + 2y \le 10 x \ge 0 y \ge 0
Solution:

We are asked to maximise the objective function Z = 5x + 3y subject to the constraints 3x + 5y \le 15, 5x + 2y \le 10, x \ge 0, and y \ge 0.

The constraints x \ge 0 and y \ge 0 restrict the feasible region to the first quadrant.

We consider the boundary lines for the inequalities:

  1. 3x + 5y = 15: This line passes through (5, 0) and (0, 3).
  2. 5x + 2y = 10: This line passes through (2, 0) and (0, 5).

The feasible region is the area in the first quadrant that lies below or on both these lines.

The corner points of the feasible region are:

  • The origin O(0, 0).
  • The x-intercept of 5x + 2y = 10 is when y=0, so 5x = 10 \Rightarrow x = 2. This point is A(2, 0). Note that (2,0) satisfies 3x+5y \le 15 as 3(2)+5(0)=6 \le 15.
  • The y-intercept of 3x + 5y = 15 is when x=0, so 5y = 15 \Rightarrow y = 3. This point is C(0, 3). Note that (0,3) satisfies 5x+2y \le 10 as 5(0)+2(3)=6 \le 10.
  • The intersection of 3x + 5y = 15 and 5x + 2y = 10. To find this point, we can use elimination or substitution. Let's use elimination: Multiply the first equation by 2 and the second by 5: 2(3x + 5y) = 2(15) \Rightarrow 6x + 10y = 30 5(5x + 2y) = 5(10) \Rightarrow 25x + 10y = 50 Subtracting the first new equation from the second: (25x + 10y) - (6x + 10y) = 50 - 30 19x = 20 \Rightarrow x = \frac{20}{19} Substitute x = \frac{20}{19} into 5x + 2y = 10: 5\left(\frac{20}{19}\right) + 2y = 10 \frac{100}{19} + 2y = 10 2y = 10 - \frac{100}{19} = \frac{190 - 100}{19} = \frac{90}{19} y = \frac{45}{19} So, the intersection point is B \left(\frac{20}{19}, \frac{45}{19}\right).

The corner points of the feasible region are O(0, 0), A(2, 0), B

\left(\frac{20}{19}, \frac{45}{19}\right), and C(0, 3).

Now, we evaluate the objective function Z = 5x + 3y at each corner point:

  • At O(0, 0): Z = 5(0) + 3(0) = 0
  • At A(2, 0): Z = 5(2) + 3(0) = 10
  • At B \left(\frac{20}{19}, \frac{45}{19}\right): Z = 5\left(\frac{20}{19}\right) + 3\left(\frac{45}{19}\right) = \frac{100}{19} + \frac{135}{19} = \frac{235}{19} \approx 12.37
  • At C(0, 3): Z = 5(0) + 3(3) = 9

Comparing these values, the maximum value of Z is \frac{235}{19}, which occurs at the point B

\left(\frac{20}{19}, \frac{45}{19}\right).

Therefore, the maximum value of Z is \frac{235}{19} at the point \left(\frac{20}{19}, \frac{45}{19}\right).

Common mistakes

  • Incorrectly plotting the boundary lines of inequalities.
  • Shading the wrong region for inequalities (e.g., not considering the direction of the inequality).
  • Failing to identify all corner points of the feasible region.
  • Making calculation errors when evaluating the objective function at corner points.
  • Confusing maximization and minimization objectives.

Revision tips

  • Practice plotting all types of linear inequalities accurately.
  • Ensure you correctly identify all vertices of the feasible region, including intersection points.
  • Double-check your calculations when substituting corner point coordinates into the objective function.
  • Review the definitions of objective function and constraints before starting problems.
  • Understand the graphical interpretation of maximizing or minimizing a function.

Practice MCQs

Q1. What is the primary goal in a linear programming problem?

Q2. The region representing all possible solutions to the constraints is called the:

Q3. Where does the optimal solution (maximum or minimum value) of a linear programming problem typically occur?

Q4. If Z = 3x + 4y, and the corner points are (0,0), (4,0), and (0,4), what is the maximum value of Z?

Q5. For the objective function Z = -3x + 4y, which corner point yields the minimum value in the given example?

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 making the best possible decisions in situations with limited resources.

How is the feasible region determined in Linear Programming?

The feasible region is determined by graphically plotting all the linear inequalities (constraints) given in the problem. The region where all shaded areas overlap represents the feasible region.

What are corner points in Linear Programming?

Corner points, also known as vertices, are the points where the boundary lines of the feasible region intersect. These points are crucial because the optimal solution often lies at one of these points.

How do you find the maximum or minimum value of the objective function?

After identifying the corner points of the feasible region, you substitute the coordinates of each corner point into the objective function. The largest value obtained is the maximum, and the smallest value is the minimum.

Are there other methods to solve Linear Programming problems besides the graphical method?

Yes, for problems with more than two variables or complex constraints, other methods like the Simplex method are used. However, the graphical method is suitable for problems with two variables, as typically covered in Class 12.

How do these NCERT Solutions help with exam preparation?

These solutions provide clear, step-by-step explanations for solving typical linear programming problems, helping students understand the concepts and practice the graphical method effectively for their exams.

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