Address PR review comments on nlp_solving_chain extraction

- Add circular import comment in problem.py explaining the deferred import
- Move NLP_SOLVER_VARIANTS from nlp_solving_chain.py to defines.py
- Set BOUNDED_VARIABLES = True on NLPsolver base class and use it in
  _build_nlp_chain (matching the conic solver pattern in solving_chain.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: Transurgeon

cvxpy/problems/problem.py

@@ -1074,6 +1074,8 @@ def _solve(self,
                 return self.value
 
         if nlp and self.is_dnlp():
+            # Deferred import to avoid circular import:
+            # nlp_solving_chain → dnlp2smooth → cvxpy → problem
             from cvxpy.reductions.solvers.nlp_solving_chain import solve_nlp
             return solve_nlp(self, solver, warm_start, verbose, **kwargs)
         elif nlp and not self.is_dnlp():

cvxpy/reductions/solvers/defines.py

@@ -49,6 +49,7 @@
 # NLP interfaces
 from cvxpy.reductions.solvers.nlp_solvers.copt_nlpif import COPT as COPT_nlp
 from cvxpy.reductions.solvers.nlp_solvers.ipopt_nlpif import IPOPT as IPOPT_nlp
+from cvxpy.reductions.solvers.nlp_solvers.knitro_nlpif import KNITRO as KNITRO_nlp
 from cvxpy.reductions.solvers.nlp_solvers.uno_nlpif import UNO as UNO_nlp
 
 # QP interfaces
@@ -83,14 +84,23 @@
 ]}
 
 SOLVER_MAP_NLP = {inst.name(): inst for inst in [
-    IPOPT_nlp(), UNO_nlp(), COPT_nlp(),
+    IPOPT_nlp(), KNITRO_nlp(), UNO_nlp(), COPT_nlp(),
 ]}
 
 # Preference-ordered solver name lists, derived from the maps above.
 CONIC_SOLVERS = list(SOLVER_MAP_CONIC)
 QP_SOLVERS = list(SOLVER_MAP_QP)
 NLP_SOLVERS = list(SOLVER_MAP_NLP)
 
+# Solver variants: maps variant name → (base solver name, extra kwargs).
+NLP_SOLVER_VARIANTS = {
+    "knitro_ipm": ("KNITRO", {"algorithm": 1}),
+    "knitro_sqp": ("KNITRO", {"algorithm": 4}),
+    "knitro_alm": ("KNITRO", {"algorithm": 6}),
+    "uno_ipm": ("UNO", {"preset": "ipopt", "linear_solver": "MUMPS"}),
+    "uno_sqp": ("UNO", {"preset": "filtersqp"}),
+}
+
 # Mixed-integer solver lists, derived from solver class attributes.
 MI_SOLVERS = [
     name for name, slv in SOLVER_MAP_CONIC.items() if slv.MIP_CAPABLE

cvxpy/reductions/solvers/nlp_solvers/knitro_nlpif.py

@@ -27,7 +27,6 @@ class KNITRO(NLPsolver):
     NLP interface for the KNITRO solver
     """
 
-    BOUNDED_VARIABLES = True
     # Keys:
     CONTEXT_KEY = "context"
     X_INIT_KEY = "x_init"

cvxpy/reductions/solvers/nlp_solvers/nlp_solver.py

@@ -36,6 +36,7 @@ class NLPsolver(Solver):
     """
     REQUIRES_CONSTR = False
     MIP_CAPABLE = False
+    BOUNDED_VARIABLES = True
 
     def accepts(self, problem):
         """

cvxpy/reductions/solvers/nlp_solving_chain.py

@@ -20,52 +20,49 @@
 from cvxpy.reductions.cvx_attr2constr import CvxAttr2Constr
 from cvxpy.reductions.dnlp2smooth.dnlp2smooth import Dnlp2Smooth
 from cvxpy.reductions.flip_objective import FlipObjective
-from cvxpy.reductions.solvers.defines import INSTALLED_SOLVERS, SOLVER_MAP_NLP
+from cvxpy.reductions.solvers.defines import INSTALLED_SOLVERS, NLP_SOLVER_VARIANTS, SOLVER_MAP_NLP
 from cvxpy.reductions.solvers.solving_chain import SolvingChain
 
-# Solver variants: maps variant name → (base solver name, extra kwargs).
-NLP_SOLVER_VARIANTS = {
-    "knitro_ipm": ("KNITRO", {"algorithm": 1}),
-    "knitro_sqp": ("KNITRO", {"algorithm": 4}),
-    "knitro_alm": ("KNITRO", {"algorithm": 6}),
-    "uno_ipm": ("UNO", {"preset": "ipopt", "linear_solver": "MUMPS"}),
-    "uno_sqp": ("UNO", {"preset": "filtersqp"}),
-}
-
 
 def _build_nlp_chain(problem, solver, kwargs):
     """Build the NLP reduction chain and return (SolvingChain, kwargs).
 
     Solver selection may mutate kwargs (e.g., Knitro algorithm, Uno preset).
     """
-    if type(problem.objective) == Maximize:
-        reductions = [FlipObjective()]
-    else:
-        reductions = []
-    reductions = reductions + [CvxAttr2Constr(reduce_bounds=False), Dnlp2Smooth()]
-
+    # Resolve the solver instance.
     if solver is None:
         # Pick first installed NLP solver in preference order.
         for name, inst in SOLVER_MAP_NLP.items():
             if name in INSTALLED_SOLVERS:
-                reductions.append(inst)
+                solver_instance = inst
                 break
         else:
             raise error.SolverError(
                 "No NLP solver is installed. Install one of: %s"
                 % ", ".join(SOLVER_MAP_NLP)
             )
     elif solver in SOLVER_MAP_NLP:
-        reductions.append(SOLVER_MAP_NLP[solver])
+        solver_instance = SOLVER_MAP_NLP[solver]
     elif solver.lower() in NLP_SOLVER_VARIANTS:
         base_name, variant_kwargs = NLP_SOLVER_VARIANTS[solver.lower()]
         kwargs.update(variant_kwargs)
-        reductions.append(SOLVER_MAP_NLP[base_name])
+        solver_instance = SOLVER_MAP_NLP[base_name]
     else:
         raise error.SolverError(
             "Solver %s is not supported for NLP problems." % solver
         )
 
+    # Build the reduction chain.
+    if type(problem.objective) == Maximize:
+        reductions = [FlipObjective()]
+    else:
+        reductions = []
+    reductions += [
+        CvxAttr2Constr(reduce_bounds=not solver_instance.BOUNDED_VARIABLES),
+        Dnlp2Smooth(),
+        solver_instance,
+    ]
+
     return SolvingChain(reductions=reductions), kwargs