For each of the following programs: (1) Sketch the feasible region of the program and the direction of the objective function. (2) Use you sketch to find an optimal solution to the program. State the optimal solution give the objective value for this solution. If an optimal solution does not exist, state why. (a) (b) maximize subject to maximize subject to - 2x1 + x2 2₁-2₂ ≤ 1, 2x₁-₂ ≥ 1, 2x1 + 2x₂ ≥ 4, I1, I₂ 20 ₁ + 2x₂ -₁ + 2x₂ ≤ 6, x+3x₂ ≤ 12, I1, I2 20
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- -For this problem clearly derive the Linear program-Graph this problem and clearly indicate the feasible options- Clearly determine the solution to the problem using the method of points (be smart about which points you have to evaluate based on the graph of the objective functionYou are given the tableau shown in Table 74 for a maximization problem. Give conditions on the unknowns a1, a2, a3, b, and c that make the following statements true: a)The current solution is optimal. b)The current solution is optimal, and there are alternative optimal solutions. c)The LP is unbounded (in this part, assume that b 0).What is Optimization? How many methods are there to calculate it? Explain this? 2- What do we mean by function Objective? What do we mean by constraints? 3- Give three practical examples (physical or engineering) of a target function with a constraint
- Analyze algebraically what special case in simplex application is present in each of the LP model below. Give an explanation to support your answer. a) Maximize z = 4x1 + 2x2 Subject to: 2x1 - x2 ≤ 2 3x1 - 4x2 ≤ 8 x1, x2 ≥ 0b) Maximize z = 3x1 + 2x2 Subject to: 4x1 - x2 ≤ 8 4x1 + 3x2 ≤ 12 4x1 + x2 ≤ 8 x1, x2 ≥ 0b) Maximize Z = −40X1 −100X2s.t 10X1 + 5X2 ≤ 2502X1 + 5X2 ≤ 1002X1 + 3X2 ≤ 90X1, X2 ≥ 0Solve by simplex method, what are the solutions? Show that this problem hasmultiple solutions and find the solutions?Find solution using BigM (penalty) method.Maximize Z = x1 + 2x2 + 3x3 - x4subject to the constraintsx1 + 2x2 + 3x3 = 152x1 + x2 + 5x3 = 20x1 + 2x2 + x3 + x4 = 10and x1, x2, x3, x4 ≥ 0
- 1. A specific assignment of values to decision variables is called what? a. Constraint b. Feasible c. Solution d. None of the above 2. Which of the following must be true of a feasible solution a. All of what Solver calls changing variables must be greater than 0 b. It is optimal c. It violates no constraints d. None of the above4. Consider the following linear programming problem: Maximize Z=$15x + $5y, subject to (1) 2x + y ≤ 10 and (2) 4x + 3y ≤ 24 and (3) x, y ≥ 0. Will the optimal solution change if the objective function becomes Maximize Z=$15x + $20y (constraints remain the same)? Select one: a. Can't determine given the information. b. Yes, it will change. c. No, it remains the same.Consider the following LP problem: Min 6X+ 27Y Subject to : 2 X + 9Y => 25, and X + Y <= 75. Pick a suitable statement for this problem: a. X=37.5, Y=37.5 is the only optimal solution. b. Optimal Obj. function value is 75 c. X = 0, Y = 0 is the only optimal solution. d. Optimal Obj. function value is 0
- a) What is the optimal solution to this problem? Solve it graphically. b) If a technical breakthrough occurred that raised the profit per unit of X1 to $3, would this affect the optimal solution? c) Instead of an increase in the profit coefficient X1 to $3, suppose that profit was overestimated and should only have been $1.25. Does this change the optimal solution?Consider the following puzzle. You are to pick out 4three-letter “words” from the following list:DBA DEG ADI FFD GHI BCD FDF BAIFor each word, you earn a score equal to the position thatthe word’s third letter appears in the alphabet. For example,DBA earns a score of 1, DEG earns a score of 7, and so on.Your goal is to choose the four words that maximize yourtotal score, subject to the following constraint: The sum ofthe positions in the alphabet for the first letter of each wordchosen must be at least as large as the sum of the positionsin the alphabet for the second letter of each word chosen.Formulate an IP to solve this problem.Do you agree or disagree with the following statements? Match accordingly For a convex programming problem, a local optimum is not a global optimum KKT conditions can be used to determine the optimality of a potential solution for generally constrained problems For convex programming problems, if an objective function is being maximized, it is required to be convex and if it is being minimized it is required to be concave