Provide examples (using simple formulations with couple of constraints) of linear programming formulations for
1. Infeasible solution
2. Unbound solution
1.
An example of infeasible solutions
Max 5x + 2y
ST
2x + 3y >= 30
3x + 3y <= 20
x, y >= 0
Here the areas of the inequalities do not overlap in the positive quadrant. As a result it is infeasible
2.
An example of unbound solution
Max 5x + 2y
ST
2x + 3y >= 30
3x + 3y >= 20
x, y >= 0
Here, the inequalities are on the greater side and the objective function is also maximization. Thus the solution is unbound. It can be increased to indefinitely.
Provide examples (using simple formulations with couple of constraints) of linear programming formulations for 1. Infeasible...
Consider the following linear programming model: Max X1 + X2 Subject to: X1 + X2 ≤ 2 X1 ≥ 1 X2 ≥ 3 X1, X2 ≥ 0 This linear programming model has a(n). A. Unbound solution B. Infeasible solution C. Redundant constraint D. Alternate optimal solution
Multi-objective linear programming (MOLP) provide: a. a way to incorporate soft constraints b. a way to incorporate hard constraints c. a simple way to solve the problem as a relaxed LP d. a way to analyze LP problems with multiple conflicting objectives
Consider the following linear programming model Max 2X1 + 3X2 Subject to: X1 + X2 X1 ≥ 2 X1, X2 ≥ 0 This linear programming model has: A. Infeasible solution B. Unique solution C. Unbounded Solution D. Alternate optimal solution E. Redundant constraints
MATLAB Problem Create a code to plot 2-D linear programming problems. label all constraints and hatch the infeasible region for constraints and bounds. Use arrow to show objective function gradient and linprog function. The user should input a cost function vector, two vectors xmax and xmin for bounds, and various constraints like: equality and inequality.
5. Define a linear programming objective and constraints that is applicable to Facebook. Provide the necessary calculations (graphically and algebraically)
1. Solving the linear programming problem Maximize z 3r1 2r2 3, subject to the constraints using the simplex algorithm gave the final tableau T4 T5 #210 1-1/4 3/8-1/812 0 0 23/4 3/8 7/8 10 (a) (3 points) Add the constraint -221 to the final tableau and use the dual simplex algorithm to find a new optimal solution. (b) (3 points) After adding the constraint of Part (a), what happens to the optimal solution if we add the fourth constraint 2+...
1. True or False: Non-linear quality constraints can never be rewritten as linear constraints. 2. True or False: Every optimization problem with linear constraints is a linear programming problem.
Solve the following Integer Linear Programming Problem graphically using the method presented in class. Indicate whether problem is unbounded, infeasible and if an optimal solution exists, clearly state what the solution is. MAX Z = X1 + 2X2ST 4X1 + 6X2 ≤ 22 X1 + 5X2 ≤ 15 2X1 + X2 ≤ 9 X1, X2 ≥ 0 and X1 integer
3.4 EXERCISES In Exercises 1-5 the given tableau represents a solution to a linear programming problem that satisfies the optimality criterion, but is infeasible. Use the dual simplex method to restore feasibility 0x 0001 0 0 0x 1000-0 3'00-00 C. 0730 5 3.4 EXERCISES In Exercises 1-5 the given tableau represents a solution to a linear programming problem that satisfies the optimality criterion, but is infeasible. Use the dual simplex method to restore feasibility 0x 0001 0 0 0x 1000-0...
Which of the following represents valid constraints in linear programming? o 2X2 7XY 2X* 7Y 2 500 - 2X + 3Y = 100 2X^2 + 7Y 250 All of the above are valid linear programming constraints. A Moving to another question will save this response.