diff --git a/test/sparseLM.cpp b/test/sparseLM.cpp
new file mode 100644
index 0000000..8e148f9
--- /dev/null
+++ b/test/sparseLM.cpp
@@ -0,0 +1,176 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#include <iostream>
+#include <fstream>
+#include <iomanip>
+
+#include "main.h"
+#include <Eigen/LevenbergMarquardt>
+
+using namespace std;
+using namespace Eigen;
+
+template <typename Scalar>
+struct sparseGaussianTest : SparseFunctor<Scalar, int>
+{
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  typedef SparseFunctor<Scalar,int> Base;
+  typedef typename Base::JacobianType JacobianType;
+  sparseGaussianTest(int inputs, int values) : SparseFunctor<Scalar,int>(inputs,values)
+  { }
+  
+  VectorType model(const VectorType& uv, VectorType& x)
+  {
+    VectorType y; //Change this to use expression template
+    int m = Base::values(); 
+    int n = Base::inputs();
+    eigen_assert(uv.size()%2 == 0);
+    eigen_assert(uv.size() == n);
+    eigen_assert(x.size() == m);
+    y.setZero(m);
+    int half = n/2;
+    VectorBlock<const VectorType> u(uv, 0, half);
+    VectorBlock<const VectorType> v(uv, half, half);
+    Scalar coeff;
+    for (int j = 0; j < m; j++)
+    {
+      for (int i = 0; i < half; i++) 
+      {
+        coeff = (x(j)-i)/v(i);
+        coeff *= coeff;
+        if (coeff < 1. && coeff > 0.)
+          y(j) += u(i)*std::pow((1-coeff), 2);
+      }
+    }
+    return y;
+  }
+  void initPoints(VectorType& uv_ref, VectorType& x)
+  {
+    m_x = x;
+    m_y = this->model(uv_ref,x);
+  }
+  int operator()(const VectorType& uv, VectorType& fvec)
+  {
+    int m = Base::values(); 
+    int n = Base::inputs();
+    eigen_assert(uv.size()%2 == 0);
+    eigen_assert(uv.size() == n);
+    int half = n/2;
+    VectorBlock<const VectorType> u(uv, 0, half);
+    VectorBlock<const VectorType> v(uv, half, half);
+    fvec = m_y;
+    Scalar coeff;
+    for (int j = 0; j < m; j++)
+    {
+      for (int i = 0; i < half; i++)
+      {
+        coeff = (m_x(j)-i)/v(i);
+        coeff *= coeff;
+        if (coeff < 1. && coeff > 0.)
+          fvec(j) -= u(i)*std::pow((1-coeff), 2);
+      }
+    }
+    return 0;
+  }
+  
+  int df(const VectorType& uv, JacobianType& fjac)
+  {
+    int m = Base::values(); 
+    int n = Base::inputs();
+    eigen_assert(n == uv.size());
+    eigen_assert(fjac.rows() == m);
+    eigen_assert(fjac.cols() == n);
+    int half = n/2;
+    VectorBlock<const VectorType> u(uv, 0, half);
+    VectorBlock<const VectorType> v(uv, half, half);
+    Scalar coeff;
+    
+    //Derivatives with respect to u
+    for (int col = 0; col < half; col++)
+    {
+      for (int row = 0; row < m; row++)
+      {
+        coeff = (m_x(row)-col)/v(col);
+          coeff = coeff*coeff;
+        if(coeff < 1. && coeff > 0.)
+        {
+          fjac.coeffRef(row,col) = -(1-coeff)*(1-coeff);
+        }
+      }
+    }
+    //Derivatives with respect to v
+    for (int col = 0; col < half; col++)
+    {
+      for (int row = 0; row < m; row++)
+      {
+        coeff = (m_x(row)-col)/v(col);
+        coeff = coeff*coeff;
+        if(coeff < 1. && coeff > 0.)
+        {
+          fjac.coeffRef(row,col+half) = -4 * (u(col)/v(col))*coeff*(1-coeff);
+        }
+      }
+    }
+    return 0;
+  }
+  
+  VectorType m_x, m_y; //Data points
+};
+
+
+template<typename T>
+void test_sparseLM_T()
+{
+  typedef Matrix<T,Dynamic,1> VectorType;
+  
+  int inputs = 10;
+  int values = 2000;
+  sparseGaussianTest<T> sparse_gaussian(inputs, values);
+  VectorType uv(inputs),uv_ref(inputs);
+  VectorType x(values);
+  // Generate the reference solution 
+  uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3;
+  //Generate the reference data points
+  x.setRandom();
+  x = 10*x;
+  x.array() += 10;
+  sparse_gaussian.initPoints(uv_ref, x);
+  
+  
+  // Generate the initial parameters 
+  VectorBlock<VectorType> u(uv, 0, inputs/2); 
+  VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
+  v.setOnes();
+  //Generate u or Solve for u from v
+  u.setOnes();
+  
+  // Solve the optimization problem
+  LevenbergMarquardt<sparseGaussianTest<T> > lm(sparse_gaussian);
+  int info;
+//   info = lm.minimize(uv);
+  
+  VERIFY_IS_EQUAL(info,1);
+    // Do a step by step solution and save the residual 
+  int maxiter = 200;
+  int iter = 0;
+  MatrixXd Err(values, maxiter);
+  MatrixXd Mod(values, maxiter);
+  LevenbergMarquardtSpace::Status status; 
+  status = lm.minimizeInit(uv);
+  if (status==LevenbergMarquardtSpace::ImproperInputParameters)
+      return ;
+
+}
+void test_sparseLM()
+{
+  CALL_SUBTEST_1(test_sparseLM_T<double>());
+  
+  // CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
+}