diff --git a/gtsam/nonlinear/tests/testNonlinearConjugateGradientOptimizer.cpp b/gtsam/nonlinear/tests/testNonlinearConjugateGradientOptimizer.cpp
index d7ca70459..36673c7a0 100644
--- a/gtsam/nonlinear/tests/testNonlinearConjugateGradientOptimizer.cpp
+++ b/gtsam/nonlinear/tests/testNonlinearConjugateGradientOptimizer.cpp
@@ -1,56 +1,52 @@
 /**
- * @file   NonlinearConjugateGradientOptimizer.cpp
- * @brief  Test simple CG optimizer
+ * @file   testNonlinearConjugateGradientOptimizer.cpp
+ * @brief  Test nonlinear CG optimizer
  * @author Yong-Dian Jian
+ * @author Varun Agrawal
  * @date   June 11, 2012
  */
 
-/**
- * @file   testGradientDescentOptimizer.cpp
- * @brief  Small test of NonlinearConjugateGradientOptimizer
- * @author Yong-Dian Jian
- * @date   Jun 11, 2012
- */
-
-#include <gtsam/slam/BetweenFactor.h>
+#include <CppUnitLite/TestHarness.h>
+#include <gtsam/geometry/Pose2.h>
 #include <gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h>
 #include <gtsam/nonlinear/NonlinearFactorGraph.h>
 #include <gtsam/nonlinear/Values.h>
-#include <gtsam/geometry/Pose2.h>
-
-#include <CppUnitLite/TestHarness.h>
-
+#include <gtsam/slam/BetweenFactor.h>
 
 using namespace std;
 using namespace gtsam;
 
 // Generate a small PoseSLAM problem
 std::tuple<NonlinearFactorGraph, Values> generateProblem() {
-
   // 1. Create graph container and add factors to it
   NonlinearFactorGraph graph;
 
   // 2a. Add Gaussian prior
-  Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
-  SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(
-      Vector3(0.3, 0.3, 0.1));
+  Pose2 priorMean(0.0, 0.0, 0.0);  // prior at origin
+  SharedDiagonal priorNoise =
+      noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
   graph.addPrior(1, priorMean, priorNoise);
 
   // 2b. Add odometry factors
-  SharedDiagonal odometryNoise = noiseModel::Diagonal::Sigmas(
-      Vector3(0.2, 0.2, 0.1));
-  graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise);
-  graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
-  graph.emplace_shared<BetweenFactor<Pose2>>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
-  graph.emplace_shared<BetweenFactor<Pose2>>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
+  SharedDiagonal odometryNoise =
+      noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
+  graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, Pose2(2.0, 0.0, 0.0),
+                                             odometryNoise);
+  graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, Pose2(2.0, 0.0, M_PI_2),
+                                             odometryNoise);
+  graph.emplace_shared<BetweenFactor<Pose2>>(3, 4, Pose2(2.0, 0.0, M_PI_2),
+                                             odometryNoise);
+  graph.emplace_shared<BetweenFactor<Pose2>>(4, 5, Pose2(2.0, 0.0, M_PI_2),
+                                             odometryNoise);
 
   // 2c. Add pose constraint
-  SharedDiagonal constraintUncertainty = noiseModel::Diagonal::Sigmas(
-      Vector3(0.2, 0.2, 0.1));
+  SharedDiagonal constraintUncertainty =
+      noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
   graph.emplace_shared<BetweenFactor<Pose2>>(5, 2, Pose2(2.0, 0.0, M_PI_2),
-      constraintUncertainty);
+                                             constraintUncertainty);
 
-  // 3. Create the data structure to hold the initialEstimate estimate to the solution
+  // 3. Create the data structure to hold the initialEstimate estimate to the
+  // solution
   Values initialEstimate;
   Pose2 x1(0.5, 0.0, 0.2);
   initialEstimate.insert(1, x1);
@@ -68,16 +64,17 @@ std::tuple<NonlinearFactorGraph, Values> generateProblem() {
 
 /* ************************************************************************* */
 TEST(NonlinearConjugateGradientOptimizer, Optimize) {
-const auto [graph, initialEstimate] = generateProblem();
-//  cout << "initial error = " << graph.error(initialEstimate) << endl;
+  const auto [graph, initialEstimate] = generateProblem();
+  //  cout << "initial error = " << graph.error(initialEstimate) << endl;
 
   NonlinearOptimizerParams param;
-  param.maxIterations = 500; /* requires a larger number of iterations to converge */
+  param.maxIterations =
+      500; /* requires a larger number of iterations to converge */
   param.verbosity = NonlinearOptimizerParams::SILENT;
 
   NonlinearConjugateGradientOptimizer optimizer(graph, initialEstimate, param);
   Values result = optimizer.optimize();
-//  cout << "cg final error = " << graph.error(result) << endl;
+  //  cout << "cg final error = " << graph.error(result) << endl;
 
   EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
 }