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This is needed to support efficient implicit differentiation, e.g. https://github.com/MadNLP/MadDiff.jl.
The idea is to have parameters be fully "implicit" in the eyes of NLPModels, instead of lifting them to "special variables" like in JuMP/DiffOpt. While the latter approach allows to reuse the existing AD APIs (slicing out the parametric parts), this approach should be more efficient. It also allows for variable/constraint bounds to be parametric.
cc @amontoison
closes #532