diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
index f8d0c747d..7fbdb37fa 100644
--- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
+++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
@@ -97,8 +97,6 @@ void LevenbergMarquardtParams::print(const std::string& str) const {
   std::cout << "               lambdaFactor: " << lambdaFactor << "\n";
   std::cout << "           lambdaUpperBound: " << lambdaUpperBound << "\n";
   std::cout << "           lambdaLowerBound: " << lambdaLowerBound << "\n";
-  std::cout << "     disableInnerIterations: " << disableInnerIterations
-      << "\n";
   std::cout << "           minModelFidelity: " << minModelFidelity << "\n";
   std::cout << "            diagonalDamping: " << diagonalDamping << "\n";
   std::cout << "                verbosityLM: "
@@ -141,10 +139,6 @@ void LevenbergMarquardtOptimizer::decreaseLambda(double stepQuality) {
 GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem(
     const GaussianFactorGraph& linear) {
 
-  //Set two parameters as Ceres, will move out later
-  static const double min_diagonal_ = 1e-6;
-  static const double max_diagonal_ = 1e32;
-
   gttic(damp);
   if (params_.verbosityLM >= LevenbergMarquardtParams::DAMPED)
     cout << "building damped system with lambda " << state_.lambda << endl;
@@ -154,7 +148,7 @@ GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem(
     state_.hessianDiagonal = linear.hessianDiagonal();
     BOOST_FOREACH(Vector& v, state_.hessianDiagonal | map_values) {
       for (int aa = 0; aa < v.size(); aa++) {
-        v(aa) = std::min(std::max(v(aa), min_diagonal_), max_diagonal_);
+        v(aa) = std::min(std::max(v(aa), params_.min_diagonal_), params_.max_diagonal_);
         v(aa) = sqrt(v(aa));
       }
     }
diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.h b/gtsam/nonlinear/LevenbergMarquardtOptimizer.h
index bae82b7c3..47952f9e4 100644
--- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.h
+++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.h
@@ -51,17 +51,19 @@ public:
   double lambdaUpperBound; ///< The maximum lambda to try before assuming the optimization has failed (default: 1e5)
   double lambdaLowerBound; ///< The minimum lambda used in LM (default: 0)
   VerbosityLM verbosityLM; ///< The verbosity level for Levenberg-Marquardt (default: SILENT), see also NonlinearOptimizerParams::verbosity
-  bool disableInnerIterations; ///< If enabled inner iterations on the linearized system are performed
   double minModelFidelity; ///< Lower bound for the modelFidelity to accept the result of an LM iteration
   std::string logFile; ///< an optional CSV log file, with [iteration, time, error, labda]
   bool diagonalDamping; ///< if true, use diagonal of Hessian
-  bool reuse_diagonal_; //an additional option in Ceres for diagonalDamping (related to efficiency)
-  bool useFixedLambdaFactor_; // if true applies constant increase (or decrease) to lambda according to lambdaFactor
+  bool reuse_diagonal_; ///< an additional option in Ceres for diagonalDamping (related to efficiency)
+  bool useFixedLambdaFactor_; ///< if true applies constant increase (or decrease) to lambda according to lambdaFactor
+  double min_diagonal_; ///< when using diagonal damping saturates the minimum diagonal entries (default: 1e-6)
+  double max_diagonal_; ///< when using diagonal damping saturates the maximum diagonal entries (default: 1e32)
 
   LevenbergMarquardtParams() :
       lambdaInitial(1e-5), lambdaFactor(10.0), lambdaUpperBound(1e5), lambdaLowerBound(
-          0.0), verbosityLM(SILENT), disableInnerIterations(false), minModelFidelity(
-          1e-3), diagonalDamping(false), reuse_diagonal_(false), useFixedLambdaFactor_(true) {
+          0.0), verbosityLM(SILENT), minModelFidelity(1e-3),
+          diagonalDamping(false), reuse_diagonal_(false), useFixedLambdaFactor_(true),
+          min_diagonal_(1e-6), max_diagonal_(1e32) {
   }
   virtual ~LevenbergMarquardtParams() {
   }