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example.py
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example.py
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import pyomo.environ as pyo
import numpy as np
import pyomo_utility as util
i_num = 3
j_num = 4
m = pyo.ConcreteModel()
m.i = pyo.RangeSet(0, i_num - 1)
m.j = pyo.RangeSet(0, j_num - 1)
# variable
m.x = pyo.Var(m.i, m.j, bounds=(0, 1))
# parameter
m.A = pyo.Param(m.i, m.j, default=0, mutable=True)
# constraint
m.row_sum_eq_one_con = pyo.Constraint(
m.i, rule=lambda p, i:
pyo.quicksum(p.x[i, :]) == 1
)
# objective
m.obj = pyo.Objective(expr=pyo.sum_product(m.A, m.x))
# suffix for dual
m.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)
A_0 = np.random.rand(i_num, j_num)
# fill parameter A with A_0
util.fill_value(m.A, A_0)
solver = pyo.SolverFactory('gurobi_direct')
solver.solve(m)
# get value of variable x
x = util.value_to_numpy(m.x)
print(f'x = {x}')
# x = [[0. 0. 0. 1.]
# [0. 0. 1. 0.]
# [0. 0. 0. 1.]]
# get dual of constraint con
dual = util.suffix_to_numpy(m.dual, m.row_sum_eq_one_con)
print(f'dual = {dual}')
# dual = [0.06459462 0.27276094 0.07508348]
# fill variable x with x_0
x_0 = np.random.rand(i_num, j_num)
util.fill_value(m.x, x_0)