diff --git a/examples/GNCExample.cpp b/examples/GNCExample.cpp
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+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file GNCExample.cpp
+ * @brief Simple example showcasing a Graduated Non-Convexity based solver
+ * @author Achintya Mohan
+ */
+
+/**
+ * A simple 2D pose graph optimization example
+ * - The robot is initially at origin (0.0, 0.0, 0.0) 
+ * - We have full odometry measurements for 2 motions
+ * - The robot first moves to (1.0, 0.0, 0.1) and then to (1.0, 1.0, 0.2) 
+ */
+
+#include <gtsam/geometry/Pose2.h>
+#include <gtsam/nonlinear/GncOptimizer.h>
+#include <gtsam/nonlinear/GncParams.h>
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+#include <gtsam/nonlinear/LevenbergMarquardtParams.h>
+#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/slam/BetweenFactor.h>
+
+#include <iostream>
+
+using namespace std;
+using namespace gtsam;
+
+int main() {
+  cout << "Graduated Non-Convexity Example\n";
+
+  NonlinearFactorGraph graph;
+
+  // Add a prior to the first point, set to the origin
+  auto priorNoise = noiseModel::Isotropic::Sigma(3, 0.1);
+  graph.addPrior(1, Pose2(0.0, 0.0, 0.0), priorNoise);
+
+  // Add additional factors, noise models must be Gaussian 
+  Pose2 x1(1.0, 0.0, 0.1);
+  graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, x1, noiseModel::Isotropic::Sigma(3, 0.2));
+  Pose2 x2(0.0, 1.0, 0.1);
+  graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, x2, noiseModel::Isotropic::Sigma(3, 0.4));
+
+  // Initial estimates
+  Values initial;
+  initial.insert(1, Pose2(0.2, 0.5, -0.1));
+  initial.insert(2, Pose2(0.8, 0.3, 0.1));
+  initial.insert(3, Pose2(0.8, 0.2, 0.3));
+
+  // Set options for the non-minimal solver
+  LevenbergMarquardtParams lmParams;
+  lmParams.setMaxIterations(1000);
+  lmParams.setRelativeErrorTol(1e-5);
+
+  // Set GNC-specific options
+  GncParams<LevenbergMarquardtParams> gncParams(lmParams);
+  gncParams.setLossType(GncLossType::TLS);
+
+  // Optimize the graph and print results
+  GncOptimizer<GncParams<LevenbergMarquardtParams>> optimizer(graph, initial, gncParams);
+  Values result = optimizer.optimize();
+  result.print("Final Result:");
+
+  return 0;
+}
+