Infeasible solution after addGenConstrPWL
AnsweredHi, I am working on the following entropy maximization problem
I use addGenConstrPWL to piecewise linearize x*(1-lnx), following are my codes to solve the model.
# generate points for PWL
ysh = zsh = np.linspace(0,1,num = 100)
hzs = [p*(1 - log(p)) if p != 0 else 0 for p in zsh]
hys = [p*(1 - log(p)) if p != 0 else 0 for p in ysh]
#build the model
msp = gb.Model('sp')
y = msp.addVars(n,m, ub = 1, name = 'y')
hy = msp.addVars(n,m,lb = -GRB.INFINITY, name = 'hy')
z = msp.addVars(n, ub = 1, name = 'z')
hz = msp.addVars(n,lb = -GRB.INFINITY, name = 'hz')
obj = gb.quicksum(q[i]*hy[i,j] for j in range(m) for i in range(n)) + gb.quicksum(q[i]*hz[i] for i in range(n))
msp.setObjective(obj,GRB.MAXIMIZE)
for i in range(n):
msp.addGenConstrPWL(z[i], hz[i], zsh, hzs)
for j in range(m):
msp.addGenConstrPWL(y[i,j], hy[i,j], ysh, hys)
msp.addConstrs(gb.quicksum(y[i,j] for j in range(m)) + z[i] == 1 for i in range(n))
msp.addConstr(gb.quicksum(q[i]*c[i][j]*y[i,j] for j in range(m) for i in range(n)) + gb.quicksum(q[i]*t[i]*z[i] for i in range(n)) <= B)
msp.optimize()
I solve the above model to get the objective value, which is very close to the true optimum. However, when I can check the solution (y,z) I find them infeasible, because the summation of them is greater than 1, e.g., 1.58, 2.36, etc.
Can anyone help me with this? Why infeasible solution occurs? How to get the right solution?
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Gurobi Staff
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Your example is not reproducible. Variables n,m, q, c are not defined. In addition, there are imported packages missing. Can you please update your code to be reproducible.
I solve the above model to get the objective value, which is very close to the true optimum. However, when I can check the solution (y,z) I find them infeasible, because the summation of them is greater than 1, e.g., 1.58, 2.36, etc.
Do you get any warning about violated constraints in the log file/output?
Best regards,
Jaromił0 -
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Hi Jaromił, I did not remember the specific parameter setting. These days I have tried many other parameter settings and have not found any infeasible solutions again. In case I find any infeasible solution in my following experiments, I will post it here. Thank you very much.
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