├── README.md
├── linear_programming_sample.py
└── mixed_integer_linear_programming_sample.py


/README.md:
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 1 | **Cplex Python API Sample File**
 2 | 
 3 | In this repository, you can know how to use Cplex Python API to solve 
 4 | common mathematical programming problems. I create this repository
 5 |  because I have developed the way to import data into the 
 6 |  mathematical programming - by using matrices. 
 7 |  Further, you can also create the matrices via Database, 
 8 |  Excel file or any other file type which you prefer. On the other hand, it is
 9 |  not easy to find a good sample on Internet.
10 | 
11 | **CATALOG**
12 | 
13 | 1. Linear Programming
14 | 
15 | 2. Mixed Integer Linear Programming
16 | 
17 | New models are coming soon...


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/linear_programming_sample.py:
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 1 | import cplex
 2 | 
 3 | # ============================================================
 4 | # This file gives us a sample to use Cplex Python API to
 5 | # establish a Linear Programming model and then solve it.
 6 | # The Linear Programming problem displayed bellow is as:
 7 | #                  min z = cx
 8 | #    subject to:      Ax = b
 9 | # ============================================================
10 | 
11 | # ============================================================
12 | # Input all the data and parameters here
13 | num_decision_var = 3
14 | num_constraints = 3
15 | 
16 | A = [
17 |     [1.0, -2.0, 1.0],
18 |     [-4.0, 1.0, 2.0],
19 |     [-2.0, 0, 1.0],
20 | ]
21 | b = [11.0, 3.0, 1.0]
22 | c = [-3.0, 1.0, 1.0]
23 | 
24 | constraint_type = ["L", "G", "E"] # Less, Greater, Equal
25 | # ============================================================
26 | 
27 | # Establish the Linear Programming Model
28 | myProblem = cplex.Cplex()
29 | 
30 | # Add the decision variables and set their lower bound and upper bound (if necessary)
31 | myProblem.variables.add(names= ["x"+str(i) for i in range(num_decision_var)])
32 | for i in range(num_decision_var):
33 |     myProblem.variables.set_lower_bounds(i, 0.0)
34 | 
35 | # Add constraints
36 | for i in range(num_constraints):
37 |     myProblem.linear_constraints.add(
38 |         lin_expr= [cplex.SparsePair(ind= [j for j in range(num_decision_var)], val= A[i])],
39 |         rhs= [b[i]],
40 |         names = ["c"+str(i)],
41 |         senses = [constraint_type[i]]
42 |     )
43 | 
44 | # Add objective function and set its sense
45 | for i in range(num_decision_var):
46 |     myProblem.objective.set_linear([(i, c[i])])
47 | myProblem.objective.set_sense(myProblem.objective.sense.minimize)
48 | 
49 | # Solve the model and print the answer
50 | myProblem.solve()
51 | print(myProblem.solution.get_values())


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/mixed_integer_linear_programming_sample.py:
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 1 | import cplex
 2 | 
 3 | # ============================================================
 4 | # This file gives us a sample to use Cplex Python API to
 5 | # establish a Mixed Integer Linear Programming model and then solve it.
 6 | # The  problem displayed bellow is as:
 7 | #                  min z = cx
 8 | #    subject to:      Ax = b
 9 | #    and some of x is integer or binary
10 | # ============================================================
11 | 
12 | # ============================================================
13 | # Input all the data and parameters here
14 | num_decision_var = 2
15 | num_constraints = 2
16 | 
17 | A = [
18 |     [1, 9/14],
19 |     [-2, 1]
20 | ]
21 | b = [51/14, 1/3]
22 | c = [1, 1]
23 | 
24 | constraint_type = ["L", "L"] # Less, Greater, Equal
25 | # ============================================================
26 | 
27 | # Establish the Linear Programming Model
28 | myProblem = cplex.Cplex()
29 | 
30 | # Add the decision variables and set their lower bound and upper bound (if necessary)
31 | myProblem.variables.add(names= ["x"+str(i) for i in range(num_decision_var)])
32 | for i in range(num_decision_var):
33 |     myProblem.variables.set_lower_bounds(i, 0.0)
34 | 
35 | # Set the type of each variables
36 | myProblem.variables.set_types(0, myProblem.variables.type.integer)
37 | myProblem.variables.set_types(1, myProblem.variables.type.continuous)
38 | 
39 | # Add constraints
40 | for i in range(num_constraints):
41 |     myProblem.linear_constraints.add(
42 |         lin_expr= [cplex.SparsePair(ind= [j for j in range(num_decision_var)], val= A[i])],
43 |         rhs= [b[i]],
44 |         names = ["c"+str(i)],
45 |         senses = [constraint_type[i]]
46 |     )
47 | 
48 | # Add objective function and set its sense
49 | for i in range(num_decision_var):
50 |     myProblem.objective.set_linear([(i, c[i])])
51 | myProblem.objective.set_sense(myProblem.objective.sense.maximize)
52 | 
53 | # Solve the model and print the answer
54 | myProblem.solve()
55 | print(myProblem.solution.get_values())


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