diff --git a/gtsam/linear/Preconditioner.cpp b/gtsam/linear/Preconditioner.cpp
index d83385d32..1c602a963 100644
--- a/gtsam/linear/Preconditioner.cpp
+++ b/gtsam/linear/Preconditioner.cpp
@@ -183,21 +183,21 @@ void BlockJacobiPreconditioner::clean() {
 }
 
 /***************************************************************************************/
-boost::shared_ptr<Preconditioner> createPreconditioner(const boost::shared_ptr<PreconditionerParameters> parameters) {
-
-  if ( DummyPreconditionerParameters::shared_ptr dummy = boost::dynamic_pointer_cast<DummyPreconditionerParameters>(parameters) ) {
+boost::shared_ptr<Preconditioner> createPreconditioner(
+    const boost::shared_ptr<PreconditionerParameters> params) {
+  using boost::dynamic_pointer_cast;
+  if (dynamic_pointer_cast<DummyPreconditionerParameters>(params)) {
     return boost::make_shared<DummyPreconditioner>();
-  }
-  else if ( BlockJacobiPreconditionerParameters::shared_ptr blockJacobi = boost::dynamic_pointer_cast<BlockJacobiPreconditionerParameters>(parameters) ) {
+  } else if (dynamic_pointer_cast<BlockJacobiPreconditionerParameters>(
+                 params)) {
     return boost::make_shared<BlockJacobiPreconditioner>();
-  }
-  else if ( SubgraphPreconditionerParameters::shared_ptr subgraph = boost::dynamic_pointer_cast<SubgraphPreconditionerParameters>(parameters) ) {
+  } else if (auto subgraph =
+                 dynamic_pointer_cast<SubgraphPreconditionerParameters>(
+                     params)) {
     return boost::make_shared<SubgraphPreconditioner>(*subgraph);
   }
 
-  throw invalid_argument("createPreconditioner: unexpected preconditioner parameter type");
+  throw invalid_argument(
+      "createPreconditioner: unexpected preconditioner parameter type");
 }
-
-}
-
-
+}  // namespace gtsam
diff --git a/gtsam/nonlinear/NonlinearOptimizer.cpp b/gtsam/nonlinear/NonlinearOptimizer.cpp
index 0c5d99c0f..328a3facf 100644
--- a/gtsam/nonlinear/NonlinearOptimizer.cpp
+++ b/gtsam/nonlinear/NonlinearOptimizer.cpp
@@ -147,13 +147,15 @@ VectorValues NonlinearOptimizer::solve(const GaussianFactorGraph& gfg,
   } else if (params.isIterative()) {
     // Conjugate Gradient -> needs params.iterativeParams
     if (!params.iterativeParams)
-      throw std::runtime_error("NonlinearOptimizer::solve: cg parameter has to be assigned ...");
+      throw std::runtime_error(
+          "NonlinearOptimizer::solve: cg parameter has to be assigned ...");
 
-    if (boost::shared_ptr<PCGSolverParameters> pcg =
-            boost::dynamic_pointer_cast<PCGSolverParameters>(params.iterativeParams)) {
+    if (auto pcg = boost::dynamic_pointer_cast<PCGSolverParameters>(
+            params.iterativeParams)) {
       delta = PCGSolver(*pcg).optimize(gfg);
-    } else if (boost::shared_ptr<SubgraphSolverParameters> spcg =
-                   boost::dynamic_pointer_cast<SubgraphSolverParameters>(params.iterativeParams)) {
+    } else if (auto spcg =
+                   boost::dynamic_pointer_cast<SubgraphSolverParameters>(
+                       params.iterativeParams)) {
       if (!params.ordering)
         throw std::runtime_error("SubgraphSolver needs an ordering");
       delta = SubgraphSolver(gfg, *spcg, *params.ordering).optimize();
diff --git a/tests/testNonlinearOptimizer.cpp b/tests/testNonlinearOptimizer.cpp
index dd708b37a..5d4c3f844 100644
--- a/tests/testNonlinearOptimizer.cpp
+++ b/tests/testNonlinearOptimizer.cpp
@@ -493,23 +493,24 @@ TEST(NonlinearOptimizer, disconnected_graph) {
 /* ************************************************************************* */
 #include <gtsam/linear/iterative.h>
 
-class IterativeLM: public LevenbergMarquardtOptimizer {
-
+class IterativeLM : public LevenbergMarquardtOptimizer {
   /// Solver specific parameters
   ConjugateGradientParameters cgParams_;
   Values initial_;
 
-public:
+ public:
   /// Constructor
   IterativeLM(const NonlinearFactorGraph& graph, const Values& initialValues,
-      const ConjugateGradientParameters &p,
-      const LevenbergMarquardtParams& params = LevenbergMarquardtParams::LegacyDefaults()) :
-      LevenbergMarquardtOptimizer(graph, initialValues, params), cgParams_(p), initial_(initialValues) {
-  }
+              const ConjugateGradientParameters& p,
+              const LevenbergMarquardtParams& params =
+                  LevenbergMarquardtParams::LegacyDefaults())
+      : LevenbergMarquardtOptimizer(graph, initialValues, params),
+        cgParams_(p),
+        initial_(initialValues) {}
 
   /// Solve that uses conjugate gradient
-  virtual VectorValues solve(const GaussianFactorGraph &gfg,
-      const NonlinearOptimizerParams& params) const {
+  virtual VectorValues solve(const GaussianFactorGraph& gfg,
+                             const NonlinearOptimizerParams& params) const {
     VectorValues zeros = initial_.zeroVectors();
     return conjugateGradientDescent(gfg, zeros, cgParams_);
   }
@@ -518,19 +519,20 @@ public:
 /* ************************************************************************* */
 TEST(NonlinearOptimizer, subclass_solver) {
   Values expected;
-  expected.insert(X(1), Pose2(0.,0.,0.));
-  expected.insert(X(2), Pose2(1.5,0.,0.));
-  expected.insert(X(3), Pose2(3.0,0.,0.));
+  expected.insert(X(1), Pose2(0., 0., 0.));
+  expected.insert(X(2), Pose2(1.5, 0., 0.));
+  expected.insert(X(3), Pose2(3.0, 0., 0.));
 
   Values init;
-  init.insert(X(1), Pose2(0.,0.,0.));
-  init.insert(X(2), Pose2(0.,0.,0.));
-  init.insert(X(3), Pose2(0.,0.,0.));
+  init.insert(X(1), Pose2(0., 0., 0.));
+  init.insert(X(2), Pose2(0., 0., 0.));
+  init.insert(X(3), Pose2(0., 0., 0.));
 
   NonlinearFactorGraph graph;
-  graph.addPrior(X(1), Pose2(0.,0.,0.), noiseModel::Isotropic::Sigma(3,1));
-  graph += BetweenFactor<Pose2>(X(1),X(2), Pose2(1.5,0.,0.), noiseModel::Isotropic::Sigma(3,1));
-  graph.addPrior(X(3), Pose2(3.,0.,0.), noiseModel::Isotropic::Sigma(3,1));
+  graph.addPrior(X(1), Pose2(0., 0., 0.), noiseModel::Isotropic::Sigma(3, 1));
+  graph += BetweenFactor<Pose2>(X(1), X(2), Pose2(1.5, 0., 0.),
+                                noiseModel::Isotropic::Sigma(3, 1));
+  graph.addPrior(X(3), Pose2(3., 0., 0.), noiseModel::Isotropic::Sigma(3, 1));
 
   ConjugateGradientParameters p;
   Values actual = IterativeLM(graph, init, p).optimize();
diff --git a/tests/testPCGSolver.cpp b/tests/testPCGSolver.cpp
index 66b022c82..9d6cc49ac 100644
--- a/tests/testPCGSolver.cpp
+++ b/tests/testPCGSolver.cpp
@@ -126,65 +126,63 @@ TEST( GaussianFactorGraphSystem, multiply_getb)
 
 /* ************************************************************************* */
 // Test Dummy Preconditioner
-TEST( PCGSolver, dummy )
-{
-  LevenbergMarquardtParams paramsPCG;
-  paramsPCG.linearSolverType = LevenbergMarquardtParams::Iterative;
-  PCGSolverParameters::shared_ptr pcg = boost::make_shared<PCGSolverParameters>();
+TEST(PCGSolver, dummy) {
+  LevenbergMarquardtParams params;
+  params.linearSolverType = LevenbergMarquardtParams::Iterative;
+  auto pcg = boost::make_shared<PCGSolverParameters>();
   pcg->preconditioner_ = boost::make_shared<DummyPreconditionerParameters>();
-  paramsPCG.iterativeParams = pcg;
+  params.iterativeParams = pcg;
 
   NonlinearFactorGraph fg = example::createReallyNonlinearFactorGraph();
 
-  Point2 x0(10,10);
+  Point2 x0(10, 10);
   Values c0;
   c0.insert(X(1), x0);
 
-  Values actualPCG = LevenbergMarquardtOptimizer(fg, c0, paramsPCG).optimize();
+  Values actualPCG = LevenbergMarquardtOptimizer(fg, c0, params).optimize();
 
-  DOUBLES_EQUAL(0,fg.error(actualPCG),tol);
+  DOUBLES_EQUAL(0, fg.error(actualPCG), tol);
 }
 
 /* ************************************************************************* */
 // Test Block-Jacobi Precondioner
-TEST( PCGSolver, blockjacobi )
-{
-  LevenbergMarquardtParams paramsPCG;
-  paramsPCG.linearSolverType = LevenbergMarquardtParams::Iterative;
-  PCGSolverParameters::shared_ptr pcg = boost::make_shared<PCGSolverParameters>();
-  pcg->preconditioner_ = boost::make_shared<BlockJacobiPreconditionerParameters>();
-  paramsPCG.iterativeParams = pcg;
+TEST(PCGSolver, blockjacobi) {
+  LevenbergMarquardtParams params;
+  params.linearSolverType = LevenbergMarquardtParams::Iterative;
+  auto pcg = boost::make_shared<PCGSolverParameters>();
+  pcg->preconditioner_ =
+      boost::make_shared<BlockJacobiPreconditionerParameters>();
+  params.iterativeParams = pcg;
 
   NonlinearFactorGraph fg = example::createReallyNonlinearFactorGraph();
 
-  Point2 x0(10,10);
+  Point2 x0(10, 10);
   Values c0;
   c0.insert(X(1), x0);
 
-  Values actualPCG = LevenbergMarquardtOptimizer(fg, c0, paramsPCG).optimize();
+  Values actualPCG = LevenbergMarquardtOptimizer(fg, c0, params).optimize();
 
-  DOUBLES_EQUAL(0,fg.error(actualPCG),tol);
+  DOUBLES_EQUAL(0, fg.error(actualPCG), tol);
 }
 
 /* ************************************************************************* */
 // Test Incremental Subgraph PCG Solver
-TEST( PCGSolver, subgraph )
-{
-  LevenbergMarquardtParams paramsPCG;
-  paramsPCG.linearSolverType = LevenbergMarquardtParams::Iterative;
-  PCGSolverParameters::shared_ptr pcg = boost::make_shared<PCGSolverParameters>();
+TEST(PCGSolver, subgraph) {
+  LevenbergMarquardtParams params;
+  params.linearSolverType = LevenbergMarquardtParams::Iterative;
+  auto pcg = boost::make_shared<PCGSolverParameters>();
   pcg->preconditioner_ = boost::make_shared<SubgraphPreconditionerParameters>();
-  paramsPCG.iterativeParams = pcg;
+  params.iterativeParams = pcg;
 
   NonlinearFactorGraph fg = example::createReallyNonlinearFactorGraph();
 
-  Point2 x0(10,10);
+  Point2 x0(10, 10);
   Values c0;
   c0.insert(X(1), x0);
 
-  Values actualPCG = LevenbergMarquardtOptimizer(fg, c0, paramsPCG).optimize();
+  Values actualPCG = LevenbergMarquardtOptimizer(fg, c0, params).optimize();
 
-  DOUBLES_EQUAL(0,fg.error(actualPCG),tol);
+  DOUBLES_EQUAL(0, fg.error(actualPCG), tol);
 }
 
 /* ************************************************************************* */