diff --git a/abopt/algs/tests/test_lbfgs.py b/abopt/algs/tests/test_lbfgs.py
index bf4b618..c491280 100644
--- a/abopt/algs/tests/test_lbfgs.py
+++ b/abopt/algs/tests/test_lbfgs.py
@@ -11,7 +11,7 @@
 from abopt.algs.lbfgs import pre_scaled_direct_bfgs, pre_scaled_inverse_dfp
 from abopt.algs.lbfgs import post_scaled_direct_bfgs, post_scaled_inverse_dfp
 
-from abopt.testing import RosenProblem, ChiSquareProblem, ChiSquareProblem_dual, ChiSquareProblem_dual2
+from abopt.testing import RosenProblem, ChiSquareProblem, ChiSquareProblemWithFG, ChiSquareProblemWithFandFG
 import numpy
 from numpy.testing import assert_allclose
 
@@ -65,7 +65,7 @@ def test_abopt_lbfgs_quad(precond):
 precond = Preconditioner(Pvp=diag_scaling, vPp=diag_scaling)
 @pytest.mark.parametrize("precond",
 [None, precond])
-def test_abopt_lbfgs_quad_dual(precond):
+def test_abopt_lbfgs_quad_fg(precond):
     lbfgs = LBFGS(linesearch=backtrace)
 
     J = numpy.array([ [0, 0,     2,  1],
@@ -73,7 +73,25 @@ def test_abopt_lbfgs_quad_dual(precond):
                       [40, 100,  0,  0],
                       [400, 0,   0,  0]])
 
-    problem = ChiSquareProblem_dual(J=J, precond=precond)
+    problem = ChiSquareProblemWithFG(J=J, precond=precond)
+
+    x0 = numpy.zeros(4)
+    r = lbfgs.minimize(problem, x0, monitor=print)
+    assert r.converged
+    assert_allclose(problem.f(r.x), 0.0, atol=1e-7)
+
+precond = Preconditioner(Pvp=diag_scaling, vPp=diag_scaling)
+@pytest.mark.parametrize("precond",
+[None, precond])
+def test_abopt_lbfgs_quad_f_fg(precond):
+    lbfgs = LBFGS(linesearch=backtrace)
+
+    J = numpy.array([ [0, 0,     2,  1],
+                      [0,  10,   2,  0],
+                      [40, 100,  0,  0],
+                      [400, 0,   0,  0]])
+
+    problem = ChiSquareProblemWithFandFG(J=J, precond=precond)
 
     x0 = numpy.zeros(4)
     r = lbfgs.minimize(problem, x0, monitor=print)
diff --git a/abopt/base.py b/abopt/base.py
index 69364a6..fe6e4e7 100644
--- a/abopt/base.py
+++ b/abopt/base.py
@@ -281,8 +281,6 @@ def __init__(self, objective=None, gradient=None,
         self._precond = precond
         self.vs = vs
 
-        self.problem_dual_eval = False
-
         if objective_gradient is None:
             if not (objective is not None and gradient is not None):
                 raise ValueError("if objective_gradient is None, gradient and objective cannot be None.")
diff --git a/abopt/testing/__init__.py b/abopt/testing/__init__.py
index c01f6a8..e444cd8 100644
--- a/abopt/testing/__init__.py
+++ b/abopt/testing/__init__.py
@@ -2,7 +2,7 @@
 from scipy.optimize import rosen, rosen_der, rosen_hess_prod, rosen_hess
 import numpy
 
-class ChiSquareProblem_dual(Problem):
+class ChiSquareProblemWithFG(Problem):
     """
         chisquare problem with
 
@@ -28,7 +28,7 @@ def hessian(x, v):
                       hessian_vector_product = hessian,
                       precond = precond)
 
-class ChiSquareProblem_dual2(Problem):
+class ChiSquareProblemWithFandFG(Problem):
     """
         chisquare problem with