The Constraints of the Problem

Oct 13, 2025 by talynt.org 0 views

Introduction

In linear programming, the feasible region is the set of all possible solutions that satisfy the constraints of a problem. The vertices of the feasible region are the points where the constraints intersect, and they play a crucial role in determining the optimal solution. In this article, we will explore the constraints of a given problem and determine the vertices of the feasible region.

The Constraints of the Problem

The constraints of the problem are listed below:

$x + y \leq 7$
$x - 2y \leq -2$
$x \geq 0$
$y \geq 0$

Understanding the Constraints

Let's analyze each constraint individually:

The first constraint, $x + y \leq 7$ , represents a line in the coordinate plane with a slope of -1 and a y-intercept of 7. The points on or below this line satisfy the constraint.
The second constraint, $x - 2y \leq -2$ , represents a line in the coordinate plane with a slope of 1/2 and a y-intercept of -1. The points on or below this line satisfy the constraint.
The third constraint, $x \geq 0$ , represents the non-negative x-axis. The points to the right of this line satisfy the constraint.
The fourth constraint, $y \geq 0$ , represents the non-negative y-axis. The points above this line satisfy the constraint.

Finding the Vertices of the Feasible Region

To find the vertices of the feasible region, we need to find the points where the constraints intersect. Let's analyze the intersections of the constraints:

The intersection of the first and third constraints is the point (0,7), but this point does not satisfy the second constraint. Therefore, it is not a vertex of the feasible region.
The intersection of the first and fourth constraints is the point (7,0), which satisfies all the constraints. Therefore, it is a vertex of the feasible region.
The intersection of the second and third constraints is the point (0,-1), but this point does not satisfy the first constraint. Therefore, it is not a vertex of the feasible region.
The intersection of the second and fourth constraints is the point (2,0), which satisfies the first and third constraints, but not the second constraint. Therefore, it is not a vertex of the feasible region.
The intersection of the first and second constraints is the point (4,3), which satisfies all the constraints. Therefore, it is a vertex of the feasible region.

Conclusion

In conclusion, the vertices of the feasible region are the points (0,1), (4,3), and (7,0). These points are the intersection of the constraints and represent the possible solutions to the problem.

Discussion

The vertices of the feasible region are the points where the constraints intersect. In this problem, we have three constraints: $x + y \leq 7$ , $x - 2y \leq -2$ , $x \geq 0$ , and $y \geq 0$ . The vertices of the feasible region are the points (0,1), (4,3), and (7,0). These points are the intersection of the constraints and represent the possible solutions to the problem.

Final Answer

The final answer is $\boxed{(0,1), (4,3), (7,0)}$ .

Introduction

The Constraints of the Problem

The constraints of the problem are listed below:

$x + y \leq 7$
$x - 2y \leq -2$
$x \geq 0$
$y \geq 0$

Understanding the Constraints

Let's analyze each constraint individually:

The first constraint, $x + y \leq 7$ , represents a line in the coordinate plane with a slope of -1 and a y-intercept of 7. The points on or below this line satisfy the constraint.
The second constraint, $x - 2y \leq -2$ , represents a line in the coordinate plane with a slope of 1/2 and a y-intercept of -1. The points on or below this line satisfy the constraint.
The third constraint, $x \geq 0$ , represents the non-negative x-axis. The points to the right of this line satisfy the constraint.
The fourth constraint, $y \geq 0$ , represents the non-negative y-axis. The points above this line satisfy the constraint.

Finding the Vertices of the Feasible Region

To find the vertices of the feasible region, we need to find the points where the constraints intersect. Let's analyze the intersections of the constraints:

The intersection of the first and third constraints is the point (0,7), but this point does not satisfy the second constraint. Therefore, it is not a vertex of the feasible region.
The intersection of the first and fourth constraints is the point (7,0), which satisfies all the constraints. Therefore, it is a vertex of the feasible region.
The intersection of the second and third constraints is the point (0,-1), but this point does not satisfy the first constraint. Therefore, it is not a vertex of the feasible region.
The intersection of the second and fourth constraints is the point (2,0), which satisfies the first and third constraints, but not the second constraint. Therefore, it is not a vertex of the feasible region.
The intersection of the first and second constraints is the point (4,3), which satisfies all the constraints. Therefore, it is a vertex of the feasible region.

Conclusion

In conclusion, the vertices of the feasible region are the points (0,1), (4,3), and (7,0). These points are the intersection of the constraints and represent the possible solutions to the problem.

Discussion

Q&A

Q: What are the constraints of the problem?

A: The constraints of the problem are $x + y \leq 7$ , $x - 2y \leq -2$ , $x \geq 0$ , and $y \geq 0$ .

Q: What are the vertices of the feasible region?

A: The vertices of the feasible region are the points (0,1), (4,3), and (7,0).

Q: How do you find the vertices of the feasible region?

A: To find the vertices of the feasible region, you need to find the points where the constraints intersect.

Q: What is the significance of the vertices of the feasible region?

A: The vertices of the feasible region are the points where the constraints intersect, and they play a crucial role in determining the optimal solution.

Q: What is the feasible region?

A: The feasible region is the set of all possible solutions that satisfy the constraints of a problem.

Q: What is linear programming?

A: Linear programming is a method of finding the optimal solution to a problem by maximizing or minimizing a linear function subject to a set of linear constraints.

Final Answer

The final answer is $\boxed{(0,1), (4,3), (7,0)}$ .