Read each statement below carefully and indicate whether it is True or False by marking T for True or F

for False. If you indicate false, please explain why it is false with a short sentence.

1. (T-F) The feasible region of a LP problem with two variables may be bounded or unbounded.

2. (T-F) If x is the amount of money a company invests in project A and y the amount invested in project B then the constraint the amount invested in B should be no more than 30 percent of the overall investment would indicate the inequality y 0.3(x + y).

3. (T-F) The set of solution points that satisfies all of a linear programming problems constraints simulta- neously is defined as the feasible region in linear programming.

4. (T-F) Adding a constraint to a linear programming problem only decreases the size of the feasible region.

5. (T-F) A constraint is a mathematical expression in linear programming that maximizes or minimizes some quantity.

6. (T-F) If the objective function coefficients are slightly changed within certain limits, the current optimal solution may remain optimal.

7. (T-F) The two objective functions (Maximize 3X + 7Y , and Minimize 3X 7Y ) will produce the same solution to a linear programming problem.

8. (T-F) The value of one additional unit of a resource in a linear programming model is the shadow price.

9. (T-F) For any linear programming problem with feasible solutions and a bounded feasible region, there is at least one optimal solution.

10. (T-F) The simplex method focuses solely on corner-point-feasible solutions.

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11. (T-F) Suppose you build an LP model and determine the values of the decision variables that yield an optimal solution. If one objective function coefficient is increased or decreased within its allowable range, the optimal amounts of the decision variables will not change.

12. (T-F) No LP problem with an unbounded feasible region has a solution.

13. (T-F) The graph of a linear inequality consists of a line and only some of the points on one side of the line.

14. (T-F) Every minimization problem can be converted into a maximization problem.

15. (T-F) If a constraint is binding, then its shadow price is non-positive in a maximization problem.