always return a scaling factor

Author: bknueven

pyomo/contrib/pynumero/interfaces/pyomo_nlp.py

@@ -299,32 +299,24 @@ def get_inequality_constraint_indices(self, constraints):
     # overloaded from NLP
     def get_obj_scaling(self):
         obj = self.get_pyomo_objective()
-        val = SuffixFinder('scaling_factor').find(obj)
+        val = SuffixFinder('scaling_factor', 1.0).find(obj)
         return val
 
     # overloaded from NLP
     def get_primals_scaling(self):
-        scaling_suffix_finder = SuffixFinder('scaling_factor')
+        scaling_suffix_finder = SuffixFinder('scaling_factor', 1.0)
         primals_scaling = np.ones(self.n_primals())
-        ret = None
         for i, v in enumerate(self.get_pyomo_variables()):
-            val = scaling_suffix_finder.find(v)
-            if val is not None:
-                primals_scaling[i] = val
-                ret = primals_scaling
-        return ret
+            primals_scaling[i] = scaling_suffix_finder.find(v)
+        return primals_scaling
 
     # overloaded from NLP
     def get_constraints_scaling(self):
-        scaling_suffix_finder = SuffixFinder('scaling_factor')
+        scaling_suffix_finder = SuffixFinder('scaling_factor', 1.0)
         constraints_scaling = np.ones(self.n_constraints())
-        ret = None
         for i, c in enumerate(self.get_pyomo_constraints()):
-            val = scaling_suffix_finder.find(c)
-            if val is not None:
-                constraints_scaling[i] = val
-                ret = constraints_scaling
-        return ret
+            constraints_scaling[i] = scaling_suffix_finder.find(c)
+        return constraints_scaling
 
     def extract_subvector_grad_objective(self, pyomo_variables):
         """Compute the gradient of the objective and return the entries