Revisit fixed variables treatment

[frapac]

• Change the behavior of MakeParameter, depending on the callback:
  • SparseCallback: remove the fixed variables and reduce problem's dimension in MadNLP
  • DenseCallback: keep the fixed variables frozen (as done previously). On the contrary to SparseCallback, we don't remove the fixed variables as this would require duplicating the Hessian and the Jacobian matrices to evaluate the model, which could be expensive if we are memory bounded.
• Add a new fixed_handler if the problem has no fixed variable: NoFixedVariable.
• Ensure we always interact to the nonlinear program nlp through the callback wrapper cb to avoid side effect.
• Remove the function get_index_constraint and stores indexes directly in the callback wrapper cb.
• Fix the computation of the bound's multipliers for fixed variables.

Solve #70, #229

Warning: this PR adds a small breaking change in create_kkt_system (we remove ind_cons from the arguments) but I think it's worth the change, as the previous situation was error prone. Now, we make sure we store all the problem's information inside the callback wrapper cb.

Preliminary results on space_shuttle_reentry shows that the current PR improves slightly the convergence (less iterations, less feasibility restoration), see the logs in attachment:

• master.txt
• this_pr.txt

Benchmark on CUTEst is under way.

[frapac]

Results on CUTEst are out (comparing this PR with MadNLP 0.8.4). It looks like we just improve very slightly the total number of iterations. In terms of computation time, we are slower. Also we are less robust now than before. pprof-iter.pdf pprof-cpu.pdf

Currently investigating what's going on.

[frapac]

Benchmark is improved if we use the fix in #361 in this PR: pprof-cpu.pdf