I find an interesting example about Gurobi’s LP solver. There are 2 linear constraints in my LP, named fixed and cut. The outcomes for the following 2 schemes are different: Add Both constraints, and solve only once Add fixed, solve; Then add cut, solve again In the 1st scheme, the solution y =…

The updated description should be more clearer. I guess it’s not a straightforward tolerance problem. I’m sleeping now. I’ll check the other issues tomorrow :slightly_smiling_face:.

Read Tolerances and numerical issues · JuMP

I read them. Although it is ideal to have the recommended setting, in real cases numerical problematic cuts are generated via algorithms, e.g. my case. And to update, I didn’t quite understand how Gurobi judges (the last line below) import JuMP, Gurobi function optimise(model) JuMP.optimize!(m…

Gurobi’s default feasibility tolerance is 1e-6: Parameter Reference - Gurobi Optimizer Reference Manual You cannot rely on specific behavior below this limit. It may find a solution that has a violation of 1e-10 or it may find a solution that has a tolerance of 1e-6. Gurobi doesn’t care, so long as…

[image] odow: You cannot rely on specific behavior below this limit. Therefore I set a tolerance myself, in my real-world application. e.g. this example .

Gurobi LP solver's cut-off behavior---an example

Specific Domains Optimization (Mathematical)

stevengj March 28, 2025, 1:16pm 2

Have you tried setting a tolerance? Setting tolerances in JuMP - is there an Optimizer-independent way? - #2 by odow

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Gurobi LP solver's cut-off behavior---an example

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