Allow CyIpopt to solve problems without objectives

pyomo/contrib/pynumero/algorithms/solvers/cyipopt_solver.py

@@ -24,6 +24,8 @@
 from pyomo.common.deprecation import relocated_module_attribute
 from pyomo.common.dependencies import attempt_import, numpy as np, numpy_available
 from pyomo.common.tee import redirect_fd, TeeStream
+from pyomo.common.modeling import unique_component_name
+from pyomo.core.base.objective import Objective
 
 # Because pynumero.interfaces requires numpy, we will leverage deferred
 # imports here so that the solver can be registered even when numpy is
@@ -332,11 +334,22 @@ def solve(self, model, **kwds):
         grey_box_blocks = list(
             model.component_data_objects(egb.ExternalGreyBoxBlock, active=True)
         )
-        if grey_box_blocks:
-            # nlp = pyomo_nlp.PyomoGreyBoxNLP(model)
-            nlp = pyomo_grey_box.PyomoNLPWithGreyBoxBlocks(model)
-        else:
-            nlp = pyomo_nlp.PyomoNLP(model)
+        # if there is no objective, add one temporarily so we can construct an NLP
+        objectives = list(model.component_data_objects(Objective, active=True))
+        if not objectives:
+            objname = unique_component_name(model, "_obj")
+            objective = model.add_component(objname, Objective(expr=0.0))
+        try:
+            if grey_box_blocks:
+                # nlp = pyomo_nlp.PyomoGreyBoxNLP(model)
+                nlp = pyomo_grey_box.PyomoNLPWithGreyBoxBlocks(model)
+            else:
+                nlp = pyomo_nlp.PyomoNLP(model)
+        finally:
+            # We only need the objective to construct the NLP, so we delete
+            # it from the model ASAP
+            if not objectives:
+                model.del_component(objective)
 
         problem = cyipopt_interface.CyIpoptNLP(
             nlp,

pyomo/contrib/pynumero/algorithms/solvers/tests/test_cyipopt_solver.py

@@ -316,3 +316,13 @@ def test_hs071_evalerror_old_cyipopt(self):
         msg = "Error in AMPL evaluation"
         with self.assertRaisesRegex(PyNumeroEvaluationError, msg):
             res = solver.solve(m, tee=True)
+
+    def test_solve_without_objective(self):
+        m = create_model1()
+        m.o.deactivate()
+        m.x[2].fix(0.0)
+        m.x[3].fix(4.0)
+        solver = pyo.SolverFactory("cyipopt")
+        res = solver.solve(m, tee=True)
+        pyo.assert_optimal_termination(res)
+        self.assertAlmostEqual(m.x[1].value, 9.0)