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Resolve R2 Allocation test #164

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MaxenceGollier
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@dpo @MohamedLaghdafHABIBOULLAH
I think this solves #161.
I added a function that constructs a GenericExecutionStats on a RegularizedNLPModel.
solver_specific entries are added during the construction, this removes the allocation in solve! caused by adding an uninitialized solver_specific entry in stats.

Also, for type stability, I had to specify that solver_specific entries are the same type as the one added in this new constructor, else there are allocations as well. This might cause issues if used without care.

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stats = GenericExecutionStats(reg_nlp.model, solver_specific = Dict{Symbol, T}())
set_solver_specific!(stats, :smooth_obj, T(Inf))
set_solver_specific!(stats, :nonsmooth_obj, T(Inf))
set_solver_specific!(stats, :xi, T(Inf))
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Wouldn’t $\xi$ be in dual_feas?

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I don't see how $$\xi$$ is related with dual feasibility of the unconstrained problem but perhaps I am missing something ?

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By dual feasibility, we mean the gradient of the Lagrangian (for smooth problems). Here, the corresponding concept is $$\sqrt{\xi / \nu}$$.

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