Max of value and if negative, return 0 (gurobipy)
Answered
Hi,
I am trying to currently implement a lp with gurobipy, where some of the decision variables have a max function in it. The constraint I want to implement looks like this:
where e[j] and t[j] are my continuous decision variables and are defined as:
So far, I've tried to solve it like this according to my research of similar problems but it is still not working, as my lp now becomes infeasible although it shouldn't:
for j in range(n):
model.addConstr(e_sum[j] == d[j] - c[m-1,j,(s[j]) - 1])
model.addConstr(t_sum[j] == c[m-1,j,(s[j]) - 1] - d[j])
# Constraint
for j in range(n):
model.addGenConstrMax(e[j], [e_sum[j], 0.0])
model.addGenConstrMax(t[j], [t_sum[j], 0.0])
model.addConstr(c[m-1,j,(s[j]) - 1] - t[j] + e[j] == d[j], 'Constraint')
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Official comment
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Hi,
This can happen if the variables \(e_{sum}^j\) and \(t_{sum}^j\) are defined to be always \(\geq 0\). When adding a variable to the model, its lb is set to 0 by default. If these variables can take negative values, make sure to set the lb accordingly. For example, you can set it to \(-\mbox{float("inf")}\).
If this is not the cause of infeasibility in your model, you might want to check out the article How do I determine why my model is infeasible?
Best regards,
Maliheh
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