diff --git a/algopy/tracer/tracer.py b/algopy/tracer/tracer.py
index b84651b..8128de5 100644
--- a/algopy/tracer/tracer.py
+++ b/algopy/tracer/tracer.py
@@ -246,7 +246,10 @@ def f(x1, x2):
         else:
             x_list = [x]
 
-        utpm_x_list = [algopy.UTPM(numpy.asarray(xi).reshape((1,1) + numpy.shape(xi))) for xi in x_list]
+        utpm_x_list = []
+        for xi in x_list:
+            element = numpy.asarray(xi).reshape((1,1) + numpy.shape(xi))
+            utpm_x_list.append(algopy.UTPM(element))
 
         self.pushforward(utpm_x_list)
 
@@ -1017,6 +1020,9 @@ def __rdiv__(self, lhs):
         lhs = self.__class__.totype(lhs)
         return lhs/self
 
+    __truediv__ = __div__
+    __rtruediv__ = __rdiv__
+
 
     # #########################################################
     # numpy functions
diff --git a/algopy/utpm/tests/test_utpm.py b/algopy/utpm/tests/test_utpm.py
index 810a8f7..f263825 100644
--- a/algopy/utpm/tests/test_utpm.py
+++ b/algopy/utpm/tests/test_utpm.py
@@ -653,6 +653,19 @@ def test_gammaln(self):
         b = UTPM.gammaln(x + 1) - UTPM.log(x)
         assert_allclose(a.data, b.data)
 
+    def test_gammaln_pullback(self):
+        D,P = 2,1
+
+        # forward
+        x = UTPM(numpy.random.random((D,P)))
+        y = UTPM.gammaln(x)
+
+        # reverse
+        ybar = UTPM(numpy.random.random((D,P)))
+        xbar = UTPM.pb_gammaln(ybar, x, y)
+
+        assert_array_almost_equal(ybar.data[0]*y.data[1], xbar.data[0]*x.data[1])
+
     def test_hyperu(self):
         D,P,N,M = 5,1,3,3
 
diff --git a/algopy/utpm/utpm.py b/algopy/utpm/utpm.py
index 1a6a967..b6de34f 100644
--- a/algopy/utpm/utpm.py
+++ b/algopy/utpm/utpm.py
@@ -831,7 +831,7 @@ def pb_gammaln(cls, ybar, x, y, out=None):
             xbar, = out
 
         cls._pb_gammaln(ybar.data, x.data, y.data, out = xbar.data)
-        return out
+        return xbar
 
     @classmethod
     def erf(cls, x):
diff --git a/documentation/sphinx/examples/minimization/minhelper.py b/documentation/sphinx/examples/minimization/minhelper.py
index ee409ba..aa03389 100644
--- a/documentation/sphinx/examples/minimization/minhelper.py
+++ b/documentation/sphinx/examples/minimization/minhelper.py
@@ -2,12 +2,24 @@
 This is a helper module for the minimization examples.
 """
 
+#FIXME: the pyipopt stuff has been commented out for now;
+#FIXME: update this code to use a better python ipopt interface when available
+# https://github.com/xuy/pyipopt
+# http://gitview.danfis.cz/pipopt
+# https://bitbucket.org/amitibo
+# https://github.com/casadi/casadi
+
 import functools
 
 import numpy
 import scipy.optimize
 import algopy
 import numdifftools
+#import pyipopt
+
+# Suppress log spam from pyipopt.
+# But ipopt itself will stil spam...
+#pyipopt.set_loglevel(0)
 
 
 def eval_grad(f, theta):
@@ -18,6 +30,8 @@ def eval_hess(f, theta):
     theta = algopy.UTPM.init_hessian(theta)
     return algopy.UTPM.extract_hessian(len(theta), f(theta))
 
+
+
 def show_local_curvature(f, g, h, x0):
     print 'point:'
     print x0
@@ -169,6 +183,31 @@ def do_searches(f, g, h, x0):
     print results
     print
 
+    #print 'strategy:', 'ipopt'
+    #print 'options:', 'default'
+    #print 'gradient:', 'autodiff'
+    #print 'hessian:', 'autodiff'
+    #results = pyipopt.fmin_unconstrained(
+            #f,
+            #x0,
+            #fprime=g,
+            #fhess=h,
+            #)
+    #print results
+    #print
+
+    #print 'strategy:', 'ipopt'
+    #print 'options:', 'default'
+    #print 'gradient:', 'autodiff'
+    #print 'hessian:', 'finite differences'
+    #results = pyipopt.fmin_unconstrained(
+            #f,
+            #x0,
+            #fprime=g,
+            #)
+    #print results
+    #print
+
 
 def show_minimization_results(f, target_in, easy_init_in, hard_init_in):
     """