MathOptInterface.jl
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[Nonlinear] consider passing Nonlinear.Model to solvers
@kaarthiksundar and I talked about how it'd be good to pass the MOI.Nonlinear.Model
straight to the solver (e.g., Alpine).
We kinda do at the moment, in the NLPBlockData
you can access data.evaluator.model
, but this might not be the most robust approach. We could add a new AbstractModelAttribute
to pass the model directly, and the update JuMP https://github.com/jump-dev/JuMP.jl/blob/6a358d193d5d6e168afdaf104f5a5972add59bae/src/optimizer_interface.jl#L163-L170 to pass the model instead of the evaluator. I'm a bit worried that these are half-baked ideas though. We probably should wait until we understand the NLP expression representation at the JuMP level first.