removing some differences between main and experimental

src/csnlp/nlps/objective.py

@@ -19,6 +19,12 @@ def _solve_and_get_stats(
     sol = solver(**kwargs)
     sol["p"] = kwargs["p"]  # add to solution the parameters for which it was computed
     sol["stats"] = solver.func.stats()  # add to solution the solver stats
+    # NOTE: in case of failure in retrieving the stats
+    # try:
+    #     stats = solver.func.stats()
+    # except RuntimeError:
+    #     stats = {"success": False}
+    # sol["stats"] = stats  # add to solution the solver stats
     return sol
 
 

src/csnlp/wrappers/__init__.py

@@ -3,7 +3,7 @@
 Motivation
 ==========
 
-The standard class :class:`csnlp.Mpc` provides a way to solve NLP problems; however, it
+The standard class :class:`csnlp.Nlp` provides a way to solve NLP problems; however, it
 lacks many features that are useful in practice in different fields. For instance, it
 does not provide a way to scale the problem, in cases where the primal variables have
 widely different orders of magnitude and convergence is difficult to numerically
@@ -12,8 +12,8 @@
 the optimization problems.
 
 To address this, inspired by the approach adopted by the
-`gymnasium <https://gymnasium.farama.org/>` package, we provide a way to wrap instances
-of the basic :class:`csnlp.Mpc` class with wrapper classes that can add desired
+`gymnasium <https://gymnasium.farama.org/>`_ package, we provide a way to wrap instances
+of the basic :class:`csnlp.Nlp` class with wrapper classes that can add desired
 features.
 
 Overview