Issue #15 Approximate Hessians. By default these are not implemented, and are the same as J and Jt

SimPEG/forward/Problem.py

@@ -150,10 +150,38 @@ class Problem(object):
             :rtype: numpy.array
             :return: JTv
 
-            Transpose of J
+            Effect of transpose of J on a vector v.
         """
         pass
 
+
+    def J_approx(self, m, v, u=None):
+        """
+
+            :param numpy.array m: model
+            :param numpy.array v: vector to multiply
+            :param numpy.array u: fields
+            :rtype: numpy.array
+            :return: Jv
+
+            Approximate effect of J on a vector v
+
+        """
+        return self.J(m, v, u)
+
+    def Jt_approx(self, m, v, u=None):
+        """
+            :param numpy.array m: model
+            :param numpy.array v: vector to multiply
+            :param numpy.array u: fields
+            :rtype: numpy.array
+            :return: JTv
+
+            Approximate transpose of J*v
+
+        """
+        return self.Jt(m, v, u)
+
     def field(self, m):
         """
             The field given the model.

SimPEG/inverse/Inversion.py

@@ -215,7 +215,7 @@ class Inversion(object):
 
         # TODO: abstract to different norms a little cleaner.
         #                                 \/ it goes here. in l2 it is the identity.
-        dmisfit = self.prob.Jt(m, self.Wd * self.Wd * self.prob.J(m, v, u=u), u=u)
+        dmisfit = self.prob.Jt_approx(m, self.Wd * self.Wd * self.prob.J_approx(m, v, u=u), u=u)
 
         return dmisfit