diff --git a/gtsam/nonlinear/DoglegOptimizer.cpp b/gtsam/nonlinear/DoglegOptimizer.cpp
index 1e76886bd..03da76eb6 100644
--- a/gtsam/nonlinear/DoglegOptimizer.cpp
+++ b/gtsam/nonlinear/DoglegOptimizer.cpp
@@ -64,14 +64,20 @@ void DoglegOptimizer::iterate(void) {
 
   if ( params_.isMultifrontal() ) {
     GaussianBayesTree bt = *linear->eliminateMultifrontal(*params_.ordering, params_.getEliminationFunction());
-    result = DoglegOptimizerImpl::Iterate(state_.Delta, DoglegOptimizerImpl::ONE_STEP_PER_ITERATION, bt, graph_, state_.values, state_.error, dlVerbose);
+    VectorValues dx_u = bt.optimizeGradientSearch();
+    VectorValues dx_n = bt.optimize();
+    result = DoglegOptimizerImpl::Iterate(state_.Delta, DoglegOptimizerImpl::ONE_STEP_PER_ITERATION,
+      dx_u, dx_n, bt, graph_, state_.values, state_.error, dlVerbose);
   }
   else if ( params_.isSequential() ) {
     GaussianBayesNet bn = *linear->eliminateSequential(*params_.ordering, params_.getEliminationFunction());
-    result = DoglegOptimizerImpl::Iterate(state_.Delta, DoglegOptimizerImpl::ONE_STEP_PER_ITERATION, bn, graph_, state_.values, state_.error, dlVerbose);
+    VectorValues dx_u = bn.optimizeGradientSearch();
+    VectorValues dx_n = bn.optimize();
+    result = DoglegOptimizerImpl::Iterate(state_.Delta, DoglegOptimizerImpl::ONE_STEP_PER_ITERATION,
+      dx_u, dx_n, bn, graph_, state_.values, state_.error, dlVerbose);
   }
   else if ( params_.isCG() ) {
-    throw runtime_error("todo: ");
+    throw runtime_error("Dogleg is not currently compatible with the linear conjugate gradient solver");
   }
   else {
     throw runtime_error("Optimization parameter is invalid: DoglegParams::elimination");
diff --git a/gtsam/nonlinear/DoglegOptimizerImpl.h b/gtsam/nonlinear/DoglegOptimizerImpl.h
index 3344da43a..c0b52f88b 100644
--- a/gtsam/nonlinear/DoglegOptimizerImpl.h
+++ b/gtsam/nonlinear/DoglegOptimizerImpl.h
@@ -100,8 +100,8 @@ struct GTSAM_EXPORT DoglegOptimizerImpl {
    */
   template<class M, class F, class VALUES>
   static IterationResult Iterate(
-      double Delta, TrustRegionAdaptationMode mode, const M& Rd,
-      const F& f, const VALUES& x0, const double f_error, const bool verbose=false);
+      double Delta, TrustRegionAdaptationMode mode, const VectorValues& dx_u, const VectorValues& dx_n,
+      const M& Rd, const F& f, const VALUES& x0, const double f_error, const bool verbose=false);
 
   /**
    * Compute the dogleg point given a trust region radius \f$ \Delta \f$.  The
@@ -143,16 +143,9 @@ struct GTSAM_EXPORT DoglegOptimizerImpl {
 /* ************************************************************************* */
 template<class M, class F, class VALUES>
 typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate(
-    double Delta, TrustRegionAdaptationMode mode, const M& Rd,
-    const F& f, const VALUES& x0, const double f_error, const bool verbose)
+    double Delta, TrustRegionAdaptationMode mode, const VectorValues& dx_u, const VectorValues& dx_n,
+    const M& Rd, const F& f, const VALUES& x0, const double f_error, const bool verbose)
 {
-  // Compute steepest descent and Newton's method points
-  gttic(optimizeGradientSearch);
-  VectorValues dx_u = GaussianFactorGraph(Rd).optimizeGradientSearch();
-  gttoc(optimizeGradientSearch);
-  gttic(optimize);
-  VectorValues dx_n = Rd.optimize();
-  gttoc(optimize);
   gttic(M_error);
   const double M_error = Rd.error(VectorValues::Zero(dx_u));
   gttoc(M_error);