
Matthias Miltenberger
Gurobi Optimization Support Manager - Berlin, Germany
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Activity overview
Latest activity by Matthias Miltenberger-
Matthias Miltenberger commented,
Hi Sanket, The only way I can think of to incorporate the results from tools like Porta or Polymake into Gurobi is via callbacks. But I don't think this is going to help you much because you only h...
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Matthias Miltenberger commented,
I don't have much to add to Jonasz' comment. His suggested tools are way more specialized for this purpose and should be much easier to use to solve that problem. Cheers,Matthias
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Matthias Miltenberger commented,
Hi Kamal, Exactly, non-basic constraints are binding or active. Their associated slack variable is 0. Cheers,Matthias
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Matthias Miltenberger commented,
Hi Vanny! I could not spot an obvious error in your formulation, except that you are using indicator constraints in (5), (7), and (8). I would also put the upper bounds on your capacity variables r...
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Matthias Miltenberger commented,
Hi Carol! It's not clear to me what the relationship is between the binary Service variable and the X and Y variables. Is there just a single Service variable? Maybe this article can help you model...
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Matthias Miltenberger commented,
That is interesting! Can you try updating your Gurobi version to 10.0.2? And can you also try running this on a different machine, if possible? Thanks,Matthias
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Matthias Miltenberger commented,
That is very hard to say upfront. It also depends on your model and whether the reformulation still makes sense and provides a meaningful solution. Cheers,Matthias
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Matthias Miltenberger commented,
Yes, linear constraints and objectives are almost always easier to optimize than quadratic or general nonlinear ones. Cheers,Matthias
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Matthias Miltenberger commented,
Hi! This is a pretty huge model with lots of nonlinearities (6 million general constraints, 1.4 million quadratic objective terms). It very much looks like you are running out of memory here. You s...
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Matthias Miltenberger commented,
Hi Helen, All variables in Gurobi have an implicit lower bound of 0 - it is customary to model with nonnegative variables. I imagine that the infeasibility comes from your continuous deviation vari...