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diff --git a/eigen/test/denseLM.cpp b/eigen/test/denseLM.cpp
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+// 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 DenseLM : DenseFunctor<Scalar>
+{
+  typedef DenseFunctor<Scalar> Base;
+  typedef typename Base::JacobianType JacobianType;
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  
+  DenseLM(int n, int m) : DenseFunctor<Scalar>(n,m) 
+  { }
+ 
+  VectorType model(const VectorType& uv, VectorType& x)
+  {
+    VectorType y; // Should change 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);
+    for (int j = 0; j < m; j++)
+    {
+      for (int i = 0; i < half; i++)
+        y(j) += u(i)*std::exp(-(x(j)-i)*(x(j)-i)/(v(i)*v(i)));
+    }
+    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);
+    eigen_assert(fvec.size() == m);
+    int half = n/2;
+    VectorBlock<const VectorType> u(uv, 0, half);
+    VectorBlock<const VectorType> v(uv, half, half);
+    for (int j = 0; j < m; j++)
+    {
+      fvec(j) = m_y(j);
+      for (int i = 0; i < half; i++)
+      {
+        fvec(j) -= u(i) *std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
+      }
+    }
+    
+    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);
+    for (int j = 0; j < m; j++)
+    {
+      for (int i = 0; i < half; i++)
+      {
+        fjac.coeffRef(j,i) = -std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
+        fjac.coeffRef(j,i+half) = -2.*u(i)*(m_x(j)-i)*(m_x(j)-i)/(std::pow(v(i),3)) * std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
+      }
+    }
+    return 0;
+  }
+  VectorType m_x, m_y; //Data Points
+};
+
+template<typename FunctorType, typename VectorType>
+int test_minimizeLM(FunctorType& functor, VectorType& uv)
+{
+  LevenbergMarquardt<FunctorType> lm(functor);
+  LevenbergMarquardtSpace::Status info; 
+  
+  info = lm.minimize(uv);
+  
+  VERIFY_IS_EQUAL(info, 1);
+  //FIXME Check other parameters
+  return info;
+}
+
+template<typename FunctorType, typename VectorType>
+int test_lmder(FunctorType& functor, VectorType& uv)
+{
+  typedef typename VectorType::Scalar Scalar;
+  LevenbergMarquardtSpace::Status info; 
+  LevenbergMarquardt<FunctorType> lm(functor);
+  info = lm.lmder1(uv);
+  
+  VERIFY_IS_EQUAL(info, 1);
+  //FIXME Check other parameters
+  return info;
+}
+
+template<typename FunctorType, typename VectorType>
+int test_minimizeSteps(FunctorType& functor, VectorType& uv)
+{
+  LevenbergMarquardtSpace::Status info;   
+  LevenbergMarquardt<FunctorType> lm(functor);
+  info = lm.minimizeInit(uv);
+  if (info==LevenbergMarquardtSpace::ImproperInputParameters)
+      return info;
+  do 
+  {
+    info = lm.minimizeOneStep(uv);
+  } while (info==LevenbergMarquardtSpace::Running);
+  
+  VERIFY_IS_EQUAL(info, 1);
+  //FIXME Check other parameters
+  return info;
+}
+
+template<typename T>
+void test_denseLM_T()
+{
+  typedef Matrix<T,Dynamic,1> VectorType;
+  
+  int inputs = 10; 
+  int values = 1000; 
+  DenseLM<T> dense_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;
+  dense_gaussian.initPoints(uv_ref, x);
+  
+  // Generate the initial parameters 
+  VectorBlock<VectorType> u(uv, 0, inputs/2); 
+  VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
+  
+  // Solve the optimization problem
+  
+  //Solve in one go
+  u.setOnes(); v.setOnes();
+  test_minimizeLM(dense_gaussian, uv);
+  
+  //Solve until the machine precision
+  u.setOnes(); v.setOnes();
+  test_lmder(dense_gaussian, uv); 
+  
+  // Solve step by step
+  v.setOnes(); u.setOnes();
+  test_minimizeSteps(dense_gaussian, uv);
+  
+}
+
+void test_denseLM()
+{
+  CALL_SUBTEST_2(test_denseLM_T<double>());
+  
+  // CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
+}