in linear programming, a solution that does not simultaneously satisfy all constraints is called an part 2 a. intermediate solu
1 answer:
A solution that does not simultaneously satisfy all constraints is called an infeasible solution.
Option (C) is correct.
What is linear programming?
Linear programming, also known as linear optimization, is a method for achieving the best result in a mathematical model with requirements represented by linear relationships. Linear programming is a special case of mathematical programming.
In linear programming, a solution that does not simultaneously satisfy all constraints is called an infeasible solution.
In some cases, there is no feasible solution area, i.e., there are no points that satisfy all constraints of the problem. An infeasible LP problem with two decision variables can be identified through its graph. For example, let us consider the following linear programming problem.
Minimize z = 200x1 + 300x2
subject to
2x1 + 3x2 ≥ 1200
x1 + x2 ≤ 400
2x1 + 1.5x2 ≥ 900
x1, x2 ≥ 0
The region located on the right of PQR includes all solutions, which satisfy the first and the third constraints. The region located on the left of ST includes all solutions, which satisfy the second constraint. Thus, the problem is infeasible because there is no set of points that satisfies all three constraints.
Hence, a solution that does not simultaneously satisfy all constraints is called an infeasible solution.
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This is a graph of the two equations. This would then prove that the answer is d. None of the above. I’m unsure of this but Let me know if I’m correct
