lin_prog_sample.py

import cplex

# ============================================================
# This file gives us a sample to use Cplex Python API to
# establish a Linear Programming model and then solve it.
# The Linear Programming problem displayed bellow is as:
#                  min z = cx
#    subject to:      Ax = b
# ============================================================

# ============================================================
# Input all the data and parameters here
num_decision_var = 3
num_constraints = 3

A = [
    [1.0, -2.0, 1.0],
    [-4.0, 1.0, 2.0],
    [-2.0, 0, 1.0],
]
b = [11.0, 3.0, 1.0]
c = [-3.0, 1.0, 1.0]

constraint_type = ["L", "G", "E"] # Less, Greater, Equal
# ============================================================

# Establish the Linear Programming Model
myProblem = cplex.Cplex()

# Add the decision variables and set their lower bound and upper bound (if necessary)
myProblem.variables.add(names= ["x"+str(i) for i in range(num_decision_var)])
for i in range(num_decision_var):
    myProblem.variables.set_lower_bounds(i, 0.0)

# Add constraints
for i in range(num_constraints):
    myProblem.linear_constraints.add(
        lin_expr= [cplex.SparsePair(ind= [j for j in range(num_decision_var)], val= A[i])],
        rhs= [b[i]],
        names = ["c"+str(i)],
        senses = [constraint_type[i]]
    )

# Add objective function and set its sense
for i in range(num_decision_var):
    myProblem.objective.set_linear([(i, c[i])])
myProblem.objective.set_sense(myProblem.objective.sense.minimize)

# Solve the model and print the answer
myProblem.solve()
print(myProblem.solution.get_values())