CBSE Class 12 Mathematics Chapter 12 Linear Programming NCERT Solutions
This chapter delves into the principles of Linear Programming (LP), a powerful mathematical technique used for optimizing a linear objective function subject to a set of linear constraints. The NCERT Solutions for Class 12 Mathematics, Chapter 12, provide a step-by-step approach to understanding and solving LP problems. Students will learn to identify the feasible region, determine corner points, and evaluate the objective function at these points to find the maximum or minimum values. The solutions cover graphical methods for solving LP problems with two variables, ensuring clarity and accuracy. These detailed explanations are crucial for students preparing for their board examinations, offering a clear path to mastering LP concepts and problem-solving techniques.
Quick info
| Board | CBSE |
|---|---|
| Class | Class 12 |
| Subject | Mathematics |
| Session | 2026 |
| Language | English |
| Type | NCERT Solutions |
| Chapter | Chapter 12 |
Chapter summary
Chapter 12 on Linear Programming introduces students to the optimization of linear objectives under linear constraints. The NCERT Solutions focus on the graphical method for solving problems involving two variables. Key concepts covered include defining the objective function, identifying constraints, determining the feasible region, finding corner points, and evaluating the objective function at these points to find maximum or minimum values. These solutions are designed to build a strong foundation in applying mathematical models to real-world optimization scenarios.
Learning outcomes
- Understand the concept of linear programming and its applications.
- Identify and graph linear inequalities representing constraints.
- Determine the feasible region for a system of linear inequalities.
- Find the corner points of the feasible region.
- Evaluate the objective function at corner points to find optimal solutions.
- Solve linear programming problems using the graphical method.
Topics covered
Paper topics
- Linear Programming
- Objective Function
- Constraints
- Feasible Region
- Corner Points
- Graphical Method
- Maximization
- Minimization
- Linear Inequalities
- Optimization
Important topics
- Graphical Method for Solving LP Problems
- Identifying the Feasible Region
- Finding Corner Points
- Evaluating Objective Function at Corner Points
- Distinguishing Maximization vs. Minimization
PDF preview
Read page by page below. PDF is streamed from the official NCERT website — no download button on this page.
Questions and Solutions
Question 1
We need to maximize the objective function subject to the constraints , , and . First, we identify the feasible region by graphing these inequalities.
The constraint represents the region on or below the line . The constraints and restrict the region to the first quadrant. The feasible region is a triangle with vertices (corner points) at the origin and the intercepts of the line with the axes.
The corner points of the feasible region are:
- O(0,0)
- A(4,0) (x-intercept, when y=0, x=4)
- B(0,4) (y-intercept, when x=0, y=4)
Now, we evaluate the objective function at each corner point:
- At O(0,0):
- At A(4,0):
- At B(0,4):
The maximum value of Z is 16, which occurs at the point B(0,4).
Answer: The maximum value of Z is 16 at the point (0,4).
Question 2
We need to minimize the objective function subject to the constraints , , , and . We will use the graphical method.
First, we graph the lines corresponding to the inequalities:
- Line 1: . Intercepts are (8,0) and (0,4).
- Line 2: . Intercepts are (4,0) and (0,6).
The constraints and restrict us to the first quadrant. The feasible region is the area that satisfies all inequalities. We find the intersection point of Line 1 and Line 2:
Subtracting from , we get , so . Substituting into , we get , which gives , so . The intersection point is (2,3).
The corner points of the feasible region are:
- O(0,0)
- A(4,0) (x-intercept of , since it's more restrictive than (8,0) from in the first quadrant)
- B(2,3) (intersection of the two lines)
- C(0,4) (y-intercept of , since it's more restrictive than (0,6) from in the first quadrant)
Now, we evaluate the objective function at each corner point:
- At O(0,0):
- At A(4,0):
- At B(2,3):
- At C(0,4):
The minimum value of Z is -12, which occurs at the point A(4,0).
Answer: The minimum value of Z is -12 at the point (4,0).
Question 3
We need to maximize the objective function subject to the constraints , , , and . We will use the graphical method.
First, we graph the lines corresponding to the inequalities:
- Line 1: . Intercepts are (5,0) and (0,3).
- Line 2: . Intercepts are (2,0) and (0,5).
The constraints and restrict the solution to the first quadrant. The feasible region is the area that satisfies all inequalities.
We find the intersection point of Line 1 and Line 2:
From , multiply by 2: .
From , multiply by 5: .
Subtracting the first modified equation from the second: , which simplifies to , so .
Substitute into : => => => .
The intersection point is (, ).
The corner points of the feasible region are:
- O(0,0)
- A(2,0) (x-intercept of , as it's more restrictive than (5,0))
- B(, ) (intersection of the two lines)
- C(0,3) (y-intercept of , as it's more restrictive than (0,5))
Now, we evaluate the objective function at each corner point:
- At O(0,0):
- At A(2,0):
- At B(, ):
- At C(0,3):
The maximum value of Z is , which occurs at the point (, ).
Answer: The maximum value of Z is at the point (, ).
Common mistakes
- Incorrectly graphing linear inequalities, especially with '<=' or '>=' signs.
- Errors in identifying the correct feasible region, leading to wrong corner points.
- Calculation mistakes when evaluating the objective function at corner points.
- Confusing maximization with minimization problems.
- Not considering all constraints simultaneously to define the feasible region.
Revision tips
- Practice graphing each constraint line and shading the correct region.
- Carefully find the intersection points of constraint lines to determine corner points.
- Double-check the evaluation of the objective function at each corner point.
- Understand the difference between maximizing and minimizing the objective function.
- Review the graphical representation of the feasible region to ensure accuracy.
Practice MCQs
Q1. What is the primary goal of linear programming?
Explanation: Linear programming is fundamentally about optimizing (maximizing or minimizing) a linear objective function subject to linear constraints.
Q2. In linear programming, the region satisfying all constraints is called the:
Explanation: The feasible region represents all possible combinations of variables that satisfy all the given constraints of the problem.
Q3. For a linear programming problem with two variables, the optimal solution is typically found at:
Explanation: According to the fundamental theorem of linear programming, if an optimal solution exists, it will occur at one of the corner points (vertices) of the feasible region.
Q4. What does Z = 3x + 4y represent in a linear programming problem?
Explanation: Z = 3x + 4y is the objective function that we aim to maximize or minimize in this specific linear programming problem.
Q5. If x >= 0 and y >= 0 are constraints, what part of the coordinate plane is considered?
Explanation: The constraints x >= 0 and y >= 0 restrict the solution to the first quadrant of the Cartesian coordinate system.
Frequently asked questions
What is Linear Programming?
Linear Programming (LP) 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 decision in a given situation.
What is the objective function in an LP problem?
The objective function is the linear expression (like Z = 3x + 4y) that needs to be maximized or minimized. It represents the quantity we want to optimize.
What are constraints in Linear Programming?
Constraints are the linear inequalities or equalities (like x + y <= 4, x >= 0, y >= 0) that limit the possible values of the decision variables.
How is the feasible region determined?
The feasible region is the area on the graph that satisfies all the given constraints simultaneously. It is typically a polygon.
Where is the optimal solution found in an LP problem?
For problems with two variables solved graphically, the optimal solution (maximum or minimum value of the objective function) is found at one of the corner points of the feasible region.
How do these NCERT Solutions help with exam preparation?
These solutions provide clear, step-by-step explanations for solving linear programming problems using the graphical method, helping students understand the concepts and practice problem-solving for exams.
Content reviewed by the NCERT Help team. Editorial Team and update policy
NCERT Solutions PDF PDF on NCERT Help. URL unchanged for search indexing.