Update ROLObjective to be dualspace-aware (#118)

* Move Riesz representation responsibility to ReducedFunctional

ROLObjective.gradient was calling ReducedFunctional.derivative,
then performing its own Riesz mapping according to the ROLVector's
inner product. With the recent dualspace update, this doubles up the
conversion and causes a type error. Instead, we just defer the type
conversion responsibility to ReducedFunctional.derivative.

* Update computation of ROLObjective hessVec

pyadjoint/optimization/rol_solver.py

@@ -20,12 +20,14 @@ def value(self, x, tol):
             return self._val
 
         def gradient(self, g, x, tol):
-            self.deriv = self.rf.derivative()
-            g.dat = g.riesz_map(self.deriv)
+            opts = {"riesz_representation": x.inner_product}
+            self.deriv = self.rf.derivative(options=opts)
+            g.dat = Enlist(self.deriv)
 
         def hessVec(self, hv, v, x, tol):
-            hessian_action = self.rf.hessian(v.dat)
-            hv.dat = hv.riesz_map(hessian_action)
+            opts = {"riesz_representation": x.inner_product}
+            hessian_action = self.rf.hessian(v.dat, options=opts)
+            hv.dat = Enlist(hessian_action)
 
         def update(self, x, flag, iteration):
             if hasattr(ROL, "UpdateType") and isinstance(flag, ROL.UpdateType):