diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp
index 85eb6880f..393c58dfb 100644
--- a/gtsam/linear/JacobianFactor.cpp
+++ b/gtsam/linear/JacobianFactor.cpp
@@ -447,7 +447,11 @@ namespace gtsam {
       size_t nj = Ab_(pos).cols();
       Vector dj(nj);
       for (size_t k = 0; k < nj; ++k) {
-        Vector column_k = model_->whiten(Ab_(pos).col(k));
+        Vector column_k;
+        if(model_)
+          column_k = model_->whiten(Ab_(pos).col(k));
+        else
+          column_k = Ab_(pos).col(k);
         dj(k) = dot(column_k,column_k);
       }
       d.insert(j,dj);
diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
index de77dbc47..0b496d2a4 100644
--- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
+++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
@@ -113,7 +113,9 @@ void LevenbergMarquardtOptimizer::iterate() {
       double sigma = 1.0 / std::sqrt(state_.lambda);
       dampedSystem.reserve(dampedSystem.size() + state_.values.size());
       // for each of the variables, add a prior
+      VectorValues diagHessian = linear->hessianDiagonal();
       BOOST_FOREACH(const Values::KeyValuePair& key_value, state_.values) {
+
         size_t dim = key_value.value.dim();
         Matrix A = Matrix::Identity(dim, dim);
         Vector b = Vector::Zero(dim);