more brief changes

Need to try some other numerical things to encourage convergence before attacking Hessian problem...

Author: djlaky

Diff for: pyomo/contrib/doe/doe.py

@@ -211,7 +211,7 @@ def __init__(
             grey_box_solver.config.options['hessian_approximation'] = 'limited-memory'
             #grey_box_solver.config.options["linear_solver"] = "ma57" 
             grey_box_solver.config.options['max_iter'] = 3000
-            grey_box_solver.config.options['tol'] = 1e-4
+            grey_box_solver.config.options['tol'] = 2e-3
             #grey_box_solver.config.options['mu_strategy'] = "monotone"
 
             self.grey_box_solver = grey_box_solver
@@ -342,6 +342,9 @@ def FIM_egb_cons(m, p1, p2):
         #     # The solver was unsuccessful, might want to warn the user or terminate gracefully, etc.
         model.dummy_obj = pyo.Objective(expr=0, sense=pyo.minimize)
         self.solver.solve(model, tee=self.tee)
+        from idaes.core.util import DiagnosticsToolbox
+        dt = DiagnosticsToolbox(model)
+        dt.display_extreme_jacobian_entries()
 
         # Track time to initialize the DoE model
         initialization_time = sp_timer.toc(msg=None)
@@ -387,6 +390,9 @@ def FIM_egb_cons(m, p1, p2):
         # Solve the full model, which has now been initialized with the square solve
         if self.use_grey_box:
             res = self.grey_box_solver.solve(model, tee=self.tee)
+            # from idaes.core.util import DiagnosticsToolbox
+            # dt = DiagnosticsToolbox(model)
+            # dt.report_numerical_issues()
         else:
             res = self.solver.solve(model, tee=self.tee)
 

Diff for: pyomo/contrib/doe/grey_box_utilities.py

@@ -38,20 +38,12 @@
 
 import pyomo.environ as pyo
 
-# Remove this and utilize pyomo.contrib.doe 
-# but resolve the circular import issue.
-class ObjectiveLib(Enum):
-    determinant = "determinant"
-    trace = "trace"
-    minimum_eigenvalue = "minimum_eigenvalue"
-    zero = "zero"
-
 
 class FIMExternalGreyBox(ExternalGreyBoxModel):
     def __init__(
             self,
             doe_object,
-            objective_option=ObjectiveLib.determinant,
+            objective_option="determinant",
             logger_level=None,
     ):
         """
@@ -99,11 +91,13 @@ def __init__(
 
         # Set initial values for inputs
         self._input_values = np.asarray(self.doe_object.fim_initial.flatten(), dtype=np.float64)
-        print(self._input_values)
+        #print(self._input_values)
 
 
     def input_names(self):
         # Cartesian product gives us matrix indicies flattened in row-first format
+        # Can use itertools.combinations(self._param_names, 2) with added
+        # diagonal elements, or do double for loops if we switch to upper triangular
         input_names_list = list(itertools.product(self._param_names, self._param_names))
         return input_names_list
 
@@ -186,6 +180,8 @@ def evaluate_jacobian_outputs(self):
         # within the eigenvalue-dependent
         # objective options...
         eig_vals, eig_vecs = np.linalg.eig(M)
+        #print("Conditon number:")
+        #print(np.linalg.cond(M))
         if min(eig_vals) <= 1:
             pass
             print("Warning: {:0.6f}".format(min(eig_vals)))