The solution returned by GUROBI is not optimal
AnsweredI try to solve my Optimization problem using Gurobi in Python but the answer does not make sense. I solve the problem using MATLAB CVX library with MSOEK solver to get an answer (solution should be around 60; it will vary around 60 as D is a random sample).
I am wondering what could be the reason behind that:
Please see my Python code below
import gurobipy as gp
from gurobipy import GRB
import numpy as np
from numpy.random import randint
# index
Ind=np.linspace(0, 999,num=1000, dtype = int)
DD=np.linspace(0, 100,num=101)
d = randint(0, 100, 1000)
D=DD[d]
eta=50
c=10
p=15
s=7
c_=c-s
p_=p-s
lamda=0.2
B=1/2
pro=1/1000
m=gp.Model()
x=m.addVar(vtype='c',lb=0, name='x')
w=m.addVars(Ind,vtype='c',lb=0, name='w')
v=m.addVars(Ind,vtype='c',lb=0, name='v')
Z=m.addVars(Ind,vtype='c', name='Z')
obj=pro*gp.quicksum( Z[k] for k in Ind)-lamda*pro*gp.quicksum((1-B)*w[k]+B*v[k] for k in Ind)
m.setObjective(obj, GRB.MAXIMIZE)
m.addConstrs(Z[k]== eta+v[k]-w[k] for k in Ind)
m.addConstrs(Z[k]<=-c_*x+p_*D[k] for k in Ind)
m.addConstrs(Z[k]<=-c_*x+p_*x for k in Ind)
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By default, variables added via Model.addVar() or Model.addVars() have a lower bound of \( 0 \). If the \( Z \) variables are free, you should define them as follows:
Z = m.addVars(Ind, lb=-GRB.INFINITY, vtype='C', name='Z')
With this change, the optimal value of \( x \) is around \( 60 \).
The only other difference I see between the mathematical formulation and your code is that the formulation includes \( p_k \) in the objective function summations, where your code instead uses a single \( \texttt{pro} \) value. Of course, this isn't an issue if all of the \( p_k \) are equal to \( \texttt{pro} \).
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Thank you so much. That fixes the problem!
Yes, pro is constant as they have a similar probability.
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