Solution Provided by Gurobi violates Model Constraints
I am currently using the Gurobi Optimizer to create an ILP formulation for a variation of the Metric TSP problem. However, the solution returned by the Gurobi Optimizer violates constraints that I have included in the model. I have variables for the edges, named e, and vertices, named v, in the graph, and I have included the constraint that e.sum() >= v.sum(). However, the solution that is being provided by the Optimizer completely violates this constraint. Am I not properly adding this constraint to the ILP formulation? I am using model.addVars() for each of my variables, with model.addConstrs(). I have also added a couple of lazy constraints, so I have set model.params.LazyConstraints to 1. Thanks for any and all help!
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Gurobi Staff
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Maybe you could provide your model formulation?
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