In this project we try to solve classical optimization tasks and realize famous optimization algorithms.
You can test our code in Live on replit platform: https://replit.com/@AndrewLevada2/Introduction-to-Optimization-Assignment-1
Objective:
- min
Number of Variables:
- 5
Number of Constraints:
- 3
Objective Function Coefficients - c:
- 0 0 0 0 0
Constraint Matrix - A:
- 3 2 1 0 0
- 2 1 0 1 0
- 5 3 0 0 1
Right-hand Side Vector - b:
- 30 40 50
Approximation accuracy - ε:
- 0.001
Output:
- The method is not applicable!
Objective:
- max
Number of Variables:
- 6
Number of Constraints:
- 3
Objective Function Coefficients - c:
- 2 3 0 -1 0 0
Constraint Matrix - A:
- 2 -1 0 -2 1 0
- 3 2 1 -3 0 0
- -1 3 0 4 0 1
Right-hand Side Vector - b:
- 16 18 24
Approximation accuracy - epsilon:
- 0.001
Output:
- A vector of decision variables - X* = [ 0.545 8.182 0.000 0.000 23.091 0.000 ]
- Maximum value of the objective function: 25.636
Objective:
- max
Number of Variables:
- 3
Number of Constraints:
- 1
Objective Function Coefficients - c:
- 25 40 0
Constraint Matrix - A:
- 1 -1 1
Right-hand Side Vector - b:
- 0
Approximation accuracy - epsilon:
- 0.001
Output:
- The method is not applicable!
Objective:
- max
Number of Variables:
- 6
Number of Constraints:
- 3
Objective Function Coefficients - c:
- 3 5 4 0 0 0
Constraint Matrix - A:
- 2 -3 0 1 0 0
- 0 2 5 0 1 0
- 3 2 4 0 0 1
Right-hand Side Vector - b:
- 8 10 15
Approximation accuracy - epsilon:
- 0.001
Output:
- A vector of decision variables - X* = [ 1.667 5.000 0.000 19.667 0.000 0.000 ]
- Maximum value of the objective function: 30.000
Objective:
- min
Number of Variables:
- 6
Number of Constraints:
- 3
Objective Function Coefficients - c:
- -2 3 -6 -1 0 0
Constraint Matrix - A:
- 2 1 -2 1 0 0
- 1 2 4 0 1 0
- 1 -1 2 0 0 1
Right-hand Side Vector - b:
- 24 22 10
Approximation accuracy - epsilon:
- 0.001
Output:
- A vector of decision variables - X* = [ 0.000 0.000 5.000 34.000 2.000 0.000 ]
- Minimum value of the objective function: -64.000