I have a simulation model in Python where I iteratively solve an optimization model using gurobipy. In one of the iterations, I encountered the problem discussed here, where I had a nonzero gap for the optimal solution. As the workaround proposed, I set the value of NumericFocus to 2 (NumericFocus = 1 did not help in my case). That solves the issue of the nonzero gap.
However, in another iteration (i.e., same model, different set of values), my model becomes infeasible. I set the NumericFocus back to the default value and the problem becomes feasible. For this particular iteration, the coefficient statistics are as follows:
Matrix range [1e-02, 9e+01]
Objective range [3e+01, 1e+02]
Bounds range [1e+00, 9e+01]
RHS range [1e+00, 2e+02]
Given the magnitude of the coefficients, I was not expecting any numerical issues. I know how to go around this issue in this specific case but given that increasing NumericFocus is supposed to "shift the focus towards being more careful in numerical computations," now I am concerned if my problem is infeasible to begin with. So now, my question is, is it possible increasing NumericFocus makes a feasible problem infeasible? or the problem is in fact infeasible?
I am using gurobipy 9.1.1. I have the mps of the problem here.
Any help is much appreciated.
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