Merge branch 'main' into restartDict-snstop

doc/guide.rst

@@ -22,13 +22,13 @@ The optimization class is created using the following call:
 
 .. code-block:: python
 
-  optProb = Optimization("name", objFun)
+  optProb = Optimization("name", objconFun)
 
-The general template of the objective function is as follows:
+The general template of the objective and constraint function is as follows:
 
 .. code-block:: python
 
-  def obj_fun(xdict):
+  def objconFun(xdict):
       funcs = {}
       funcs["obj_name"] = function(xdict)
       funcs["con_name"] = function(xdict)
@@ -196,17 +196,30 @@ This argument is a dictionary, and the keys must match the design variable sets
 Essentially what we have done is specified the which blocks of the constraint rows are non-zero,
 and provided the sparsity structure of ones that are sparse.
 
-For linear constraints the values in ``jac`` are meaningful:
-they must be the actual linear constraint Jacobian values (which do not change).
-For non-linear constraints, only the sparsity structure (i.e. which entries are nonzero) is significant.
-The values themselves will be determined by a call to the ``sens()`` function.
-
-Also note, that the ``wrt`` and ``jac`` keyword arguments are only supported when user-supplied sensitivity is used.
+Note that the ``wrt`` and ``jac`` keyword arguments are only supported when user-supplied sensitivity is used.
 If automatic gradients from pyOptSparse are used, the constraint Jacobian will necessarily be dense.
 
 .. note::
     Currently, only the optimizers SNOPT and IPOPT support sparse Jacobians.
 
+Linear Constraints
+~~~~~~~~~~~~~~~~~~
+Linear constraints in pyOptSparse are defined exclusively by ``jac``, ``lower``, and ``upper`` entries of the ``addConGroup`` method.
+For linear constraint :math:`g_L \leq Ax + b \leq g_U`, the constraint definition would look like:
+
+.. code-block:: python
+  
+  optProb.addConGroup("con", num_cons, linear=True, wrt=["xvars"], jac={"xvars": A}, lower=gL - b, upper=gU - b)
+
+Users should not provide the linear constraint values (i.e., :math:`g = Ax + b`) in a user-defined objective/constraint function.
+pyOptSparse will raise an error if you do so.
+
+For linear constraints, the values in ``jac`` are meaningful:
+they must be the actual linear constraint Jacobian values (which do not change).
+For non-linear constraints, only the sparsity structure (i.e. which entries are nonzero) is significant.
+The values themselves will be determined by a call to the ``sens()`` function.
+
+
 Objectives
 ++++++++++
 

pyoptsparse/pyOpt_optimizer.py

@@ -367,6 +367,10 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
                 self.userObjTime += time.time() - timeA
                 self.userObjCalls += 1
 
+                # Make sure the user-defined function does *not* return linear constraint values
+                if self.callCounter == 0:
+                    self._checkLinearConstraints(funcs)
+
                 # Discard zero imaginary components in funcs
                 for key, val in funcs.items():
                     funcs[key] = np.real(val)
@@ -417,6 +421,10 @@ def _masterFunc2(self, x, evaluate, writeHist=True):
                 self.userObjTime += time.time() - timeA
                 self.userObjCalls += 1
 
+                # Make sure the user-defined function does *not* return linear constraint values
+                if self.callCounter == 0:
+                    self._checkLinearConstraints(funcs)
+
                 # Discard zero imaginary components in funcs
                 for key, val in funcs.items():
                     funcs[key] = np.real(val)
@@ -867,6 +875,18 @@ def _on_setOption(self, name, value):
         """
         pass
 
+    def _checkLinearConstraints(self, funcs):
+        """
+        Makes sure that the user-defined obj/con function does not compute the linear constraint values
+        because the linear constraints are exclusively defined by jac and bounds in addConGroup.
+        """
+        for conName in self.optProb.constraints:
+            if self.optProb.constraints[conName].linear and conName in funcs:
+                raise Error(
+                    "Value for linear constraint returned from user obj function. Linear constraints "
+                    + "are evaluated internally and should not be returned from the user's function."
+                )
+
     def setOption(self, name, value=None):
         """
         Generic routine for all option setting. The routine does

tests/test_lincon_error.py

@@ -0,0 +1,47 @@
+"""
+Tests that pyOptSparse raises an error when a user-defined obj/con function returns a linear constraint value
+(which should not because linear constraint is defined exclusively by jac and bounds)
+"""
+
+# Standard Python modules
+import unittest
+
+# First party modules
+from pyoptsparse import SLSQP, Optimization
+from pyoptsparse.pyOpt_error import Error
+
+
+def objfunc(xdict):
+    """Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3"""
+    x = xdict["x"]
+    funcs = {}
+
+    funcs["obj"] = x**2
+    funcs["con"] = x - 1  # falsely return a linear constraint value
+
+    fail = False
+    return funcs, fail
+
+
+class TestLinearConstraintCheck(unittest.TestCase):
+    def test(self):
+        # define an optimization problem with a linear constraint
+        optProb = Optimization("test", objfunc)
+        optProb.addVarGroup("x", 1, value=1)
+        optProb.addObj("obj")
+        optProb.addConGroup("con", 1, lower=1.0, linear=True, wrt=["x"], jac={"x": [1.0]})
+
+        opt = SLSQP()
+        with self.assertRaises(Error) as context:
+            opt(optProb, sens="FD")
+
+        # check if we get the expected error message
+        err_msg = (
+            "Value for linear constraint returned from user obj function. Linear constraints "
+            + "are evaluated internally and should not be returned from the user's function."
+        )
+        self.assertEqual(err_msg, str(context.exception))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_snopt_bugfix.py

@@ -21,35 +21,6 @@ def objfunc(xdict):
     funcs = {}
 
     funcs["obj"] = (x - 3.0) ** 2 + x * y + (y + 4.0) ** 2 - 3.0
-    conval = -x + y
-    funcs["con"] = conval
-
-    fail = False
-    return funcs, fail
-
-
-def objfunc_no_con(xdict):
-    """Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3"""
-    x = xdict["x"]
-    y = xdict["y"]
-    funcs = {}
-
-    funcs["obj"] = (x - 3.0) ** 2 + x * y + (y + 4.0) ** 2 - 3.0
-
-    fail = False
-    return funcs, fail
-
-
-def objfunc_2con(xdict):
-    """Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3"""
-    x = xdict["x"]
-    y = xdict["y"]
-    funcs = {}
-
-    funcs["obj"] = (x - 3.0) ** 2 + x * y + (y + 4.0) ** 2 - 3.0
-    conval = -x + y
-    funcs["con"] = conval * np.ones(2)
-    funcs["con2"] = (conval + 1) * np.ones(3)
 
     fail = False
     return funcs, fail
@@ -115,7 +86,7 @@ def test_opt(self):
 
     def test_opt_bug1(self):
         # Due to a new feature, there is a TypeError when you optimize a model without a constraint.
-        optProb = Optimization("Paraboloid", objfunc_no_con)
+        optProb = Optimization("Paraboloid", objfunc)
 
         # Design Variables
         optProb.addVarGroup("x", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
@@ -141,7 +112,7 @@ def test_opt_bug1(self):
 
     def test_opt_bug_print_2con(self):
         # Optimization Object
-        optProb = Optimization("Paraboloid", objfunc_2con)
+        optProb = Optimization("Paraboloid", objfunc)
 
         # Design Variables
         optProb.addVarGroup("x", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)

tests/test_user_termination.py

@@ -30,8 +30,6 @@ def objfunc(self, xdict):
         funcs = {}
 
         funcs["obj"] = (x - 3.0) ** 2 + x * y + (y + 4.0) ** 2 - 3.0
-        conval = -x + y
-        funcs["con"] = conval
 
         if self.obj_count > self.max_obj:
             fail = 2