What is bounded and unbounded solution in LPP?
The solutions of a linear programming problem which is feasible can be classified as a bounded solution and an unbounded solution. The unbounded solution is a situation when the optimum feasible solution cannot be determined, instead there are infinite many solutions.
What is bounded region in LPP?
A feasible region that can be enclosed in a circle. A bounded region will have both a maximum and minimum values. A feasible region that can not be enclosed in a circle.
What is bounded region?
A bounded region has either a boundary or some set of or constraints placed upon them. In other words, a bounded shape cannot be an infinitely large area—it’s defined by a set of measurements or parameters. For example, the surface area of a cylinder has constraints of length, height and circumference.
What is Z called in linear programming?
12.1. 4 Decision Variables In the objective function Z = ax + by, x and y are called decision variables. 12.1. 5 Constraints The linear inequalities or restrictions on the variables of an LPP are called constraints.
What is a bounded and unbounded solution region?
A solution region of a system of linear inequalities is bounded if it can be enclosed within a circle. If it cannot be enclosed within a circle, it is unbounded . The previous example had an unbounded solution region because it extended infinitely far to the left (and up and down
What if a feasible region is unbounded?
Linear Programming — If a Feasible Region is Unbounded If the feasible set is not bounded If the feasible set of a linear programming problem is not bounded (there is a direction in which you can travel indefinitely while staying in the feasible set) then a particular objective may or may not have an optimum:
What does unbounded mean in linear programming?
What does unbounded mean in linear programming? An unbounded solution of a linear programming problem is a situation where objective function is infinite. A linear programming problem is said to have unbounded solution if its solution can be made infinitely large without violating any of its constraints in the problem.
Does the feasible region have a common point with 3x+5Y <7?
For this, we draw the graph of the inequality, 3 x +5 y <7, and check whether the resulting half plane has points in common with the feasible region or not. It can be seen that the feasible region has no common point with 3 x +5 y <7.