Binary Varibales get negative -0.0 values
AnsweredHello,
How is it possible to get -0.0 when they are binary variables?
Thanks.
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Gurobi Staff
Have you had a chance to look at our Knowledge Base article Why does Gurobi sometimes return non-integral values for integer variables?
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Yeah,
I set IntFeasTol to 1e-9 and IntegralityFocus to 1, but it didn't changed the outcome.
Thanks for the fast reply.
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Yes, things like this still can happen due to limited precision arithmetics.
We usually do a very rough test for binary variables in a solution: if x > 0.5 then x=1 else x=0.0 -
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for j in range(n):
for i in range(m):
model.addConstr(y_2022[j,i] >= 0.5 + eps - M*(1-B))
model.addConstr(y_2022[j,i] <= 0.5 + M*(B))
model.addConstr((B==1) >> (y_2022[j,i] == 1))
model.addConstr((B==0) >> (y_2022[j,i] == 0))
I added something like this, but this time i didn't have any outcome, are there
something wrong with this?
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I probably was not clear with my last comment. The idea is not to add further constraints to ensure that binary variables get exactly value 0 or 1. This is simply not possible on all our machines with limited precision arithmetics.
The test I proposed is meant for analyzing the solution after the optimization process has already finished. If the value for a binary variable is larger than 0.5, then it is actually 1 (or True) in the solution, 0 otherwise.
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Oh OK, i thought adding such constraint would delete the - sign on the 0.0's but it's the same thing.
Thanks for your support.
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