Is the Set C Nonempty and Unbounded for Given Linear Programming Constraints?

In summary, the set C is defined as all points in the first quadrant that satisfy certain inequalities. The feasible region is the entire first quadrant, making C nonempty and unbounded. For the LP problem with objective function M=2x+3y, there is no feasible, optimal solution for points in C. However, for the LP problem with objective function M=-3x-6y, there does exist a feasible, optimal solution for points in C. The proof for this statement may involve the simplex method.
  • #1
csc2iffy
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Homework Statement



Let C be the set of all points (x,y) in the plane satisfying x≥0, y≥0, -x-2y≤-8.

a. Show that C is nonempty and unbounded.
b. Prove that the LP problem: Max M=2x+3y subject to the constraint that (x,y) lie in C has no feasible, optimal solution.
c. Show that the LP problem: Max M=-3x-6y subject to the constraint that (x,y) lie in C does have a feasible, optimal solution.


Homework Equations





The Attempt at a Solution



a. I graphed the constraints and showed that the feasible region is the entire first quadrant, and therefore C is nonempty and unbounded (provided attachment of my work - is this enough?)

b. I could "show" this but I have no idea how to "prove" it. Does it involve the simplex method?

c. Same question as above
 

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  • #2
You plotted the wrong line in the figure.It should be -x-2y=-8.

As for b and c: It is a good start to show. Do it.

ehild
 

Related to Is the Set C Nonempty and Unbounded for Given Linear Programming Constraints?

1. What is linear programming?

Linear programming is a mathematical method for finding the optimal solution to a problem with linear constraints. It involves maximizing or minimizing an objective function, subject to a set of linear constraints.

2. How is linear programming used in proofs?

Linear programming is often used in mathematical proofs as a tool for finding the best solution to a given problem. It can help to simplify complex problems and provide a clear and logical approach to solving them.

3. What are the steps involved in a linear programming proof?

The first step in a linear programming proof is to define the objective function and the constraints. Then, use graphical or algebraic methods to find the optimal solution. Finally, verify the solution using the simplex method or other techniques.

4. Can linear programming be used for non-linear problems?

No, linear programming is specifically designed for solving problems with linear constraints. Non-linear problems require different mathematical methods for finding the optimal solution.

5. How is linear programming different from other optimization methods?

Linear programming is unique in that it deals with linear constraints and an objective function that is linear in the decision variables. Other optimization methods, such as dynamic programming or integer programming, may have different constraints or objective functions.

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