Populate inform dict for all optimizers (#394)

* populate inform dict

* bugfix version bump

* revert version bump

* use empty string instead of None

* test print(sol)

pyoptsparse/pyALPSO/pyALPSO.py

@@ -2,6 +2,7 @@
 pyALPSO - A pyOptSparse interface to ALPSO
 work with sparse optimization problems.
 """
+
 # Standard Python modules
 import datetime
 import time
@@ -191,9 +192,7 @@ def objconfunc(x):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
-            sol_inform = {}
-            # sol_inform['value'] = inform
-            # sol_inform['text'] = self.informs[inform[0]]
+            sol_inform = {"value": "", "text": ""}
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, opt_f, opt_x)

pyoptsparse/pyCONMIN/pyCONMIN.py

@@ -2,6 +2,7 @@
 pyCONMIN - A variation of the pyCONMIN wrapper specificially designed to
 work with sparse optimization problems.
 """
+
 # Standard Python modules
 import datetime
 import os
@@ -240,9 +241,7 @@ def cnmngrad(n1, n2, x, f, g, ct, df, a, ic, nac):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
-            sol_inform = {}
-            # sol_inform['value'] = inform
-            # sol_inform['text'] = self.informs[inform[0]]
+            sol_inform = {"value": "", "text": ""}
 
             # Create the optimization solution
             sol = self._createSolution(optTime, sol_inform, ff, xs)

pyoptsparse/pyNSGA2/pyNSGA2.py

@@ -2,6 +2,7 @@
 pyNSGA2 - A variation of the pyNSGA2 wrapper specificially designed to
 work with sparse optimization problems.
 """
+
 # Standard Python modules
 import os
 import time
@@ -180,7 +181,7 @@ def objconfunc(nreal, nobj, ncon, x, f, g):
             self.optProb.comm.bcast(-1, root=0)
 
             # Store Results
-            sol_inform = {}
+            sol_inform = {"value": "", "text": ""}
 
             xstar = [0.0] * n
             for i in range(n):

pyoptsparse/pyParOpt/ParOpt.py

@@ -235,9 +235,7 @@ def evalObjConGradient(self, x, g, A):
             # Create the optimization solution. Note that the signs on the multipliers
             # are switch since ParOpt uses a formulation with c(x) >= 0, while pyOpt
             # uses g(x) = -c(x) <= 0. Therefore the multipliers are reversed.
-            sol_inform = {}
-            sol_inform["value"] = None
-            sol_inform["text"] = None
+            sol_inform = {"value": "", "text": ""}
 
             # If number of constraints is zero, ParOpt returns z as None.
             # Thus if there is no constraints, should pass an empty list

tests/testing_utils.py

@@ -141,6 +141,9 @@ def assert_solution_allclose(self, sol, tol, partial_x=False):
         ):
             assert_dict_allclose(sol.lambdaStar, self.lambdaStar[self.sol_index], atol=tol, rtol=tol)
 
+        # test printing solution
+        print(sol)
+
     def assert_inform_equal(self, sol, optInform=None):
         """
         Check that the optInform stored in the Solution object is as expected.