Programmatically determining warnings, e.g. large RHS
AnsweredHello,
I'm currently facing a model issue that's causing the following warning:
Warning: Model contains large rhs
While I've figured out the actual model issue already, I'm wondering if there is a programmatic way of determining whether your model has this or any other similar issue prior to actually solving (i.e. large coefficients). I'd like to add a sanity check into our build process such that we can immediately detect and flag these issues whenever they pop up.
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Gurobi Staff
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Hi Frederik,
Apart from checking your data before or while building the model, you can also run a quick sanity check before optimizing it.
This will print the model statistics:
model.update()
model.printStats()Please note that you must process pending model changes by calling update() before displaying the statistics.
I hope that helps!
Cheers,
Matthias0 -
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Hi Matthias,
thanks for the response. This approach would print the stats to the stdout which I would then have to read back in again. Is there a more direct of doing this? Also, what are the thresholds that gurobi applies to decide whether to throw a warning?
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You could also check the Model Attributes, for example, MaxRHS.
The threshold for the warning is 1e+09 for rhs, bounds, and obj coefficients. It is 1e+08 for matrix coefficients.
But we recommend to even have smaller values, see Recommended ranges for variables and constraints and Advanced user scaling.1 -
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