Fix _masterFunc2 fail flag caching and add fail flag identification…

… to IPOPT (#407)

* Cache fail flag

* Return NaN from IPOPT callback functions if evaluation failure

* Fixed typo in copy pasted code

* Fail flag tests with _masterFunc

* Undo accidental change of ipopt linear solver

* Simplify boolean evaluation

* Formatting

* A bit more test coverage

* A couple more unit tests to thoroughly test failure flag caching

* More thorough test parameterization

* Bounce the interpretation

* Upper (exclusive) bound of NumPy v2

* Last last unit test

* Formatting

* Upper numpy bound in conda environemnt

.github/environment.yml

@@ -1,7 +1,7 @@
 dependencies:
 # build
   - python >=3.9
-  - numpy >=1.21
+  - numpy >=1.21,<2
   - ipopt
   - swig
   - meson >=1.3.2

pyoptsparse/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.11.2"
+__version__ = "2.11.3"
 
 from .pyOpt_history import History
 from .pyOpt_variable import Variable

pyoptsparse/pyIPOPT/pyIPOPT.py

@@ -161,7 +161,7 @@ def __call__(
 
         if len(optProb.constraints) == 0:
             # If the user *actually* has an unconstrained problem,
-            # snopt sort of chokes with that....it has to have at
+            # IPOPT sort of chokes with that....it has to have at
             # least one constraint. So we will add one
             # automatically here:
             self.unconstrained = True
@@ -217,19 +217,25 @@ def __call__(
             # Define the 4 call back functions that ipopt needs:
             def eval_f(x, user_data=None):
                 fobj, fail = self._masterFunc(x, ["fobj"])
-                if fail == 2:
+                if fail == 1:
+                    fobj = np.array(np.NaN)
+                elif fail == 2:
                     self.userRequestedTermination = True
                 return fobj
 
             def eval_g(x, user_data=None):
                 fcon, fail = self._masterFunc(x, ["fcon"])
-                if fail == 2:
+                if fail == 1:
+                    fcon = np.array(np.NaN)
+                elif fail == 2:
                     self.userRequestedTermination = True
                 return fcon.copy()
 
             def eval_grad_f(x, user_data=None):
                 gobj, fail = self._masterFunc(x, ["gobj"])
-                if fail == 2:
+                if fail == 1:
+                    gobj = np.array(np.NaN)
+                elif fail == 2:
                     self.userRequestedTermination = True
                 return gobj.copy()
 
@@ -238,7 +244,9 @@ def eval_jac_g(x, flag, user_data=None):
                     return copy.deepcopy(matStruct)
                 else:
                     gcon, fail = self._masterFunc(x, ["gcon"])
-                    if fail == 2:
+                    if fail == 1:
+                        gcon = np.array(np.NaN)
+                    elif fail == 2:
                         self.userRequestedTermination = True
                     return gcon.copy()
 

pyoptsparse/pyOpt_optimizer.py

@@ -81,7 +81,7 @@ def __init__(
         self.storeSens: bool = True
 
         # Cache storage
-        self.cache: Dict[str, Any] = {"x": None, "fobj": None, "fcon": None, "gobj": None, "gcon": None}
+        self.cache: Dict[str, Any] = {"x": None, "fobj": None, "fcon": None, "gobj": None, "gcon": None, "fail": None}
 
         # A second-level cache for optimizers that require callbacks
         # for each constraint. (eg. PSQP etc)
@@ -388,6 +388,7 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
 
                 # Update fail flag
                 masterFail = max(masterFail, fail)
+                self.cache["fail"] = masterFail
 
             # fobj is now in cache
             returns.append(self.cache["fobj"])
@@ -437,6 +438,7 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
 
                 # Update fail flag
                 masterFail = max(masterFail, fail)
+                self.cache["fail"] = masterFail
 
             # fcon is now in cache
             returns.append(self.cache["fcon"])
@@ -447,10 +449,13 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
                 # The previous evaluated point is different than the point requested for the derivative
                 # OR this is the first call to _masterFunc2 in a hot started optimization
                 # Recursively call the routine with ['fobj', 'fcon']
-                self._masterFunc2(x, ["fobj", "fcon"], writeHist=False)
+                _, _, fail = self._masterFunc2(x, ["fobj", "fcon"], writeHist=False)
                 # We *don't* count that extra call, since that will
                 # screw up the numbering...so we subtract the last call.
                 self.callCounter -= 1
+                # Update fail flag
+                masterFail = max(masterFail, fail)
+                self.cache["fail"] = masterFail
             # Now, the point has been evaluated correctly so we
             # determine if we have to run the sens calc:
 
@@ -491,6 +496,7 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
 
                 # Update fail flag
                 masterFail = max(masterFail, fail)
+                self.cache["fail"] = masterFail
 
             # gobj is now in the cache
             returns.append(self.cache["gobj"])
@@ -502,10 +508,13 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
                 # The previous evaluated point is different than the point requested for the derivative
                 # OR this is the first call to _masterFunc2 in a hot started optimization
                 # Recursively call the routine with ['fobj', 'fcon']
-                self._masterFunc2(x, ["fobj", "fcon"], writeHist=False)
+                _, _, fail = self._masterFunc2(x, ["fobj", "fcon"], writeHist=False)
                 # We *don't* count that extra call, since that will
                 # screw up the numbering...so we subtract the last call.
                 self.callCounter -= 1
+                # Update fail flag
+                masterFail = max(masterFail, fail)
+                self.cache["fail"] = masterFail
             # Now, the point has been evaluated correctly so we
             # determine if we have to run the sens calc:
             if self.cache["gcon"] is None:
@@ -544,13 +553,15 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
 
                 # Update fail flag
                 masterFail = max(masterFail, fail)
+                self.cache["fail"] = masterFail
 
             # gcon is now in the cache
             returns.append(self.cache["gcon"])
             if self.storeSens:
                 hist["funcsSens"] = self.cache["funcsSens"]
 
-        # Put the fail flag in the history:
+        # Update the fail flag with any cached failure and put the fail flag in the history
+        masterFail = max(self.cache["fail"], masterFail)
         hist["fail"] = masterFail
 
         # Put the iteration counter in the history

setup.py

@@ -103,7 +103,7 @@ def copy_shared_libraries():
         keywords="optimization",
         install_requires=[
             "sqlitedict>=1.6",
-            "numpy>=1.21",
+            "numpy>=1.21,<2",
             "scipy>=1.7",
             "mdolab-baseclasses>=1.3.1",
         ],