add an auto-diff module in unsupported. it is similar to adolc's forward

mode but the advantage of using Eigen's expression template to compute
the derivatives (unless you nest an AutoDiffScalar into an Eigen's
matrix).

8 files changed, 863 insertions, 7 deletions

diff --git a/unsupported/Eigen/AutoDiff b/unsupported/Eigen/AutoDiff
new file mode 100644
index 000000000..f47bb0228
--- /dev/null
+++ b/unsupported/Eigen/AutoDiff
@@ -0,0 +1,54 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_AUTODIFF_MODULE
+#define EIGEN_AUTODIFF_MODULE
+
+#include <Eigen/Core>
+
+namespace Eigen {
+
+/** \ingroup Unsupported_modules
+  * \defgroup AutoDiff_Module Auto Diff module
+  *
+  * This module features forward automatic differentation via a simple
+  * templated scalar type wrapper AutoDiffScalar.
+  *
+  * \code
+  * #include <unsupported/Eigen/AutoDiff>
+  * \endcode
+  */
+//@{
+
+}
+
+#include "src/AutoDiff/AutoDiffScalar.h"
+// #include "src/AutoDiff/AutoDiffVector.h"
+#include "src/AutoDiff/AutoDiffJacobian.h"
+
+namespace Eigen {
+//@}
+}
+
+#endif // EIGEN_AUTODIFF_MODULE
diff --git a/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h b/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
new file mode 100644
index 000000000..2b7987975
--- /dev/null
+++ b/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
@@ -0,0 +1,100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_AUTODIFF_JACOBIAN_H
+#define EIGEN_AUTODIFF_JACOBIAN_H
+
+namespace Eigen
+{
+
+template<typename Functor> class AutoDiffJacobian : public Functor
+{
+public:
+  AutoDiffJacobian() : Functor() {}
+  AutoDiffJacobian(const Functor& f) : Functor(f) {}
+
+  // forward constructors
+  template<typename T0>
+  AutoDiffJacobian(const T0& a0) : Functor(a0) {}
+  template<typename T0, typename T1>
+  AutoDiffJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
+  template<typename T0, typename T1, typename T2>
+  AutoDiffJacobian(const T0& a0, const T1& a1, const T1& a2) : Functor(a0, a1, a2) {}
+
+  enum {
+    InputsAtCompileTime = Functor::InputsAtCompileTime,
+    ValuesAtCompileTime = Functor::ValuesAtCompileTime
+  };
+  
+  typedef typename Functor::InputType InputType;
+  typedef typename Functor::ValueType ValueType;
+  typedef typename Functor::JacobianType JacobianType;
+
+  typedef AutoDiffScalar<Matrix<double,InputsAtCompileTime,1> > ActiveScalar;
+  
+  typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
+  typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;
+
+  void operator() (const InputType& x, ValueType* v, JacobianType* _jac) const
+  {
+    ei_assert(v!=0);
+    if (!_jac)
+    {
+      Functor::operator()(x, v);
+      return;
+    }
+
+    JacobianType& jac = *_jac;
+
+    ActiveInput ax = x.template cast<ActiveScalar>();
+    ActiveValue av(jac.rows());
+    
+    if(InputsAtCompileTime==Dynamic)
+    {
+      for (int j=0; j<jac.cols(); j++)
+        ax[j].derivatives().resize(this->inputs());
+      for (int j=0; j<jac.rows(); j++)
+        av[j].derivatives().resize(this->inputs());
+    }
+    
+    for (int j=0; j<jac.cols(); j++)
+      for (int i=0; i<jac.cols(); i++)
+        ax[i].derivatives().coeffRef(j) = i==j ? 1 : 0;
+
+    Functor::operator()(ax, &av);
+
+    for (int i=0; i<jac.rows(); i++)
+    {
+      (*v)[i] = av[i].value();
+      for (int j=0; j<jac.cols(); j++)
+        jac.coeffRef(i,j) = av[i].derivatives().coeff(j);
+    }
+  }
+protected:
+
+};
+
+}
+
+#endif // EIGEN_AUTODIFF_JACOBIAN_H
diff --git a/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h b/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
new file mode 100644
index 000000000..852b43bf5
--- /dev/null
+++ b/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
@@ -0,0 +1,321 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_AUTODIFF_SCALAR_H
+#define EIGEN_AUTODIFF_SCALAR_H
+
+namespace Eigen {
+
+/** \class AutoDiffScalar
+  * \brief A scalar type replacement with automatic differentation capability
+  *
+  * \param DerType the vector type used to store/represent the derivatives (e.g. Vector3f)
+  *
+  * This class represents a scalar value while tracking its respective derivatives.
+  *
+  * It supports the following list of global math function:
+  *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos, 
+  *  - ei_abs, ei_sqrt, ei_pow, ei_exp, ei_log, ei_sin, ei_cos,
+  *  - ei_conj, ei_real, ei_imag, ei_abs2.
+  *
+  * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
+  * in that case, the expression template mechanism only occurs at the top Matrix level,
+  * while derivatives are computed right away.
+  *
+  */
+template<typename DerType>
+class AutoDiffScalar
+{
+  public:
+    typedef typename ei_traits<DerType>::Scalar Scalar;
+    
+    inline AutoDiffScalar() {}
+    
+    inline AutoDiffScalar(const Scalar& value)
+      : m_value(value)
+    {
+      if(m_derivatives.size()>0)
+        m_derivatives.setZero();
+    }
+    
+    inline AutoDiffScalar(const Scalar& value, const DerType& der)
+      : m_value(value), m_derivatives(der)
+    {}
+    
+    template<typename OtherDerType>
+    inline AutoDiffScalar(const AutoDiffScalar<OtherDerType>& other)
+      : m_value(other.value()), m_derivatives(other.derivatives())
+    {}
+    
+    inline AutoDiffScalar(const AutoDiffScalar& other)
+      : m_value(other.value()), m_derivatives(other.derivatives())
+    {}
+    
+    template<typename OtherDerType>
+    inline AutoDiffScalar& operator=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      m_value = other.value();
+      m_derivatives = other.derivatives();
+      return *this;
+    }
+    
+    inline AutoDiffScalar& operator=(const AutoDiffScalar& other)
+    {
+      m_value = other.value();
+      m_derivatives = other.derivatives();
+      return *this;
+    }
+    
+//     inline operator const Scalar& () const { return m_value; }
+//     inline operator Scalar& () { return m_value; }
+
+    inline const Scalar& value() const { return m_value; }
+    inline Scalar& value() { return m_value; }
+    
+    inline const DerType& derivatives() const { return m_derivatives; }
+    inline DerType& derivatives() { return m_derivatives; }
+    
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,DerType,OtherDerType> >
+    operator+(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      return AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,DerType,OtherDerType> >(
+        m_value + other.value(),
+        m_derivatives + other.derivatives());
+    }
+    
+    template<typename OtherDerType>
+    inline AutoDiffScalar&
+    operator+=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      (*this) = (*this) + other;
+      return *this;
+    }
+    
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseBinaryOp<ei_scalar_difference_op<Scalar>, DerType,OtherDerType> >
+    operator-(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      return AutoDiffScalar<CwiseBinaryOp<ei_scalar_difference_op<Scalar>, DerType,OtherDerType> >(
+        m_value - other.value(),
+        m_derivatives - other.derivatives());
+    }
+    
+    template<typename OtherDerType>
+    inline AutoDiffScalar&
+    operator-=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      *this = *this - other;
+      return *this;
+    }
+    
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, DerType> >
+    operator-() const
+    {
+      return AutoDiffScalar<CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, DerType> >(
+        -m_value,
+        -m_derivatives);
+    }
+    
+    inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >
+    operator*(const Scalar& other) const
+    {
+      return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >(
+        m_value * other,
+        (m_derivatives * other));
+    }
+    
+    friend inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >
+    operator*(const Scalar& other, const AutoDiffScalar& a)
+    {
+      return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >(
+        a.value() * other,
+        a.derivatives() * other);
+    }
+    
+    inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >
+    operator/(const Scalar& other) const
+    {
+      return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >(
+        m_value / other,
+        (m_derivatives * (Scalar(1)/other)));
+    }
+    
+    friend inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >
+    operator/(const Scalar& other, const AutoDiffScalar& a)
+    {
+      return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >(
+        other / a.value(),
+        a.derivatives() * (-Scalar(1)/other));
+    }
+    
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>,
+        NestByValue<CwiseBinaryOp<ei_scalar_difference_op<Scalar>,
+          NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >,
+          NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherDerType> > > > > >
+    operator/(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>,
+        NestByValue<CwiseBinaryOp<ei_scalar_difference_op<Scalar>,
+          NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >,
+          NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherDerType> > > > > >(
+        m_value / other.value(),
+          ((m_derivatives * other.value()).nestByValue() - (m_value * other.derivatives()).nestByValue()).nestByValue()
+        * (Scalar(1)/(other.value()*other.value())));
+    }
+    
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,
+        NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >,
+        NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherDerType> > > >
+    operator*(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      return AutoDiffScalar<CwiseBinaryOp<ei_scalar_sum_op<Scalar>,
+        NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >,
+        NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherDerType> > > >(
+        m_value * other.value(),
+        (m_derivatives * other.value()).nestByValue() + (m_value * other.derivatives()).nestByValue());
+    }
+    
+    inline AutoDiffScalar& operator*=(const Scalar& other)
+    {
+      *this = *this * other;
+      return *this;
+    }
+    
+    template<typename OtherDerType>
+    inline AutoDiffScalar& operator*=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      *this = *this * other;
+      return *this;
+    }
+    
+  protected:
+    Scalar m_value;
+    DerType m_derivatives;
+    
+};
+
+}
+
+#define EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(FUNC,CODE) \
+  template<typename DerType> \
+  inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<DerType>::Scalar>, DerType> > \
+  FUNC(const AutoDiffScalar<DerType>& x) { \
+    typedef typename ei_traits<DerType>::Scalar Scalar; \
+    typedef AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> > ReturnType; \
+    CODE; \
+  }
+
+namespace std
+{
+  EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs,
+    return ReturnType(std::abs(x.value()), x.derivatives() * (sign(x.value())));)
+  
+  EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sqrt,
+    Scalar sqrtx = std::sqrt(x.value());
+    return ReturnType(sqrtx,x.derivatives() * (Scalar(0.5) / sqrtx));)
+  
+  EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cos,
+    return ReturnType(std::cos(x.value()), x.derivatives() * (-std::sin(x.value())));)
+  
+  EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sin,
+    return ReturnType(std::sin(x.value()),x.derivatives() * std::cos(x.value()));)
+  
+  EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(exp,
+    Scalar expx = std::exp(x.value());
+    return ReturnType(expx,x.derivatives() * expx);)
+
+  EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_log,
+    return ReturnType(std::log(x.value),x.derivatives() * (Scalar(1).x.value()));)
+  
+  template<typename DerType>
+  inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<DerType>::Scalar>, DerType> >
+  pow(const AutoDiffScalar<DerType>& x, typename ei_traits<DerType>::Scalar y)
+  {
+    typedef typename ei_traits<DerType>::Scalar Scalar;
+    return AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, DerType> >(
+      std::pow(x.value(),y),
+      x.derivatives() * (y * std::pow(x.value(),y-1)));
+  }
+  
+}
+
+namespace Eigen {
+
+template<typename DerType>
+inline const AutoDiffScalar<DerType>& ei_conj(const AutoDiffScalar<DerType>& x)  { return x; }
+template<typename DerType>
+inline const AutoDiffScalar<DerType>& ei_real(const AutoDiffScalar<DerType>& x)  { return x; }
+template<typename DerType>
+inline typename DerType::Scalar ei_imag(const AutoDiffScalar<DerType>&)    { return 0.; }
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_abs,
+  return ReturnType(ei_abs(x.value()), x.derivatives() * (sign(x.value())));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_abs2,
+  return ReturnType(ei_abs2(x.value()), x.derivatives() * (Scalar(2)*x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_sqrt,
+  Scalar sqrtx = ei_sqrt(x.value());
+  return ReturnType(sqrtx,x.derivatives() * (Scalar(0.5) / sqrtx));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_cos,
+  return ReturnType(ei_cos(x.value()), x.derivatives() * (-ei_sin(x.value())));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_sin,
+  return ReturnType(ei_sin(x.value()),x.derivatives() * ei_cos(x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_exp,
+  Scalar expx = ei_exp(x.value());
+  return ReturnType(expx,x.derivatives() * expx);)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(ei_log,
+  return ReturnType(ei_log(x.value),x.derivatives() * (Scalar(1).x.value()));)
+
+template<typename DerType>
+inline const AutoDiffScalar<CwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<DerType>::Scalar>, DerType> >
+ei_pow(const AutoDiffScalar<DerType>& x, typename ei_traits<DerType>::Scalar y)
+{ return std::pow(x,y);}
+
+#undef EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY
+
+template<typename DerType> struct NumTraits<AutoDiffScalar<DerType> >
+{
+  typedef typename DerType::Scalar Real;
+  typedef AutoDiffScalar<DerType> FloatingPoint;
+  enum {
+    IsComplex = 0,
+    HasFloatingPoint = 1,
+    ReadCost = 1,
+    AddCost = 1,
+    MulCost = 1
+  };
+};
+
+}
+
+#endif // EIGEN_AUTODIFF_SCALAR_H
diff --git a/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h b/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
new file mode 100644
index 000000000..a58bb6b2e
--- /dev/null
+++ b/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
@@ -0,0 +1,214 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_AUTODIFF_VECTOR_H
+#define EIGEN_AUTODIFF_VECTOR_H
+
+namespace Eigen {
+
+/* \class AutoDiffScalar
+  * \brief A scalar type replacement with automatic differentation capability
+  *
+  * \param DerType the vector type used to store/represent the derivatives (e.g. Vector3f)
+  *
+  * This class represents a scalar value while tracking its respective derivatives.
+  *
+  * It supports the following list of global math function:
+  *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos, 
+  *  - ei_abs, ei_sqrt, ei_pow, ei_exp, ei_log, ei_sin, ei_cos,
+  *  - ei_conj, ei_real, ei_imag, ei_abs2.
+  *
+  * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
+  * in that case, the expression template mechanism only occurs at the top Matrix level,
+  * while derivatives are computed right away.
+  *
+  */
+template<typename ValueType, typename JacobianType>
+class AutoDiffVector
+{
+  public:
+    typedef typename ei_traits<ValueType>::Scalar Scalar;
+    
+    inline AutoDiffVector() {}
+    
+    inline AutoDiffVector(const ValueType& values)
+      : m_values(values)
+    {
+      m_jacobian.setZero();
+    }
+    
+    inline AutoDiffVector(const ValueType& values, const JacobianType& jac)
+      : m_values(values), m_jacobian(jac)
+    {}
+    
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffVector(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
+      : m_values(other.values()), m_jacobian(other.jacobian())
+    {}
+    
+    inline AutoDiffVector(const AutoDiffVector& other)
+      : m_values(other.values()), m_jacobian(other.jacobian())
+    {}
+    
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffScalar& operator=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
+    {
+      m_values = other.values();
+      m_jacobian = other.jacobian();
+      return *this;
+    }
+    
+    inline AutoDiffVector& operator=(const AutoDiffVector& other)
+    {
+      m_values = other.values();
+      m_jacobian = other.jacobian();
+      return *this;
+    }
+    
+    inline const ValueType& values() const { return m_values; }
+    inline ValueType& values() { return m_values; }
+    
+    inline const JacobianType& jacobian() const { return m_jacobian; }
+    inline JacobianType& jacobian() { return m_jacobian; }
+    
+    template<typename OtherValueType,typename OtherJacobianType>
+    inline const AutoDiffVector<
+      CwiseBinaryOp<ei_scalar_sum_op<Scalar>,ValueType,OtherValueType> >
+      CwiseBinaryOp<ei_scalar_sum_op<Scalar>,JacobianType,OtherJacobianType> >
+    operator+(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      return AutoDiffVector<
+      CwiseBinaryOp<ei_scalar_sum_op<Scalar>,ValueType,OtherValueType> >
+      CwiseBinaryOp<ei_scalar_sum_op<Scalar>,JacobianType,OtherJacobianType> >(
+        m_values + other.values(),
+        m_jacobian + other.jacobian());
+    }
+    
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffVector&
+    operator+=(const AutoDiffVector<OtherValueType,OtherDerType>& other)
+    {
+      m_values += other.values();
+      m_jacobian += other.jacobian();
+      return *this;
+    }
+    
+    template<typename OtherValueType,typename OtherJacobianType>
+    inline const AutoDiffVector<
+      CwiseBinaryOp<ei_scalar_difference_op<Scalar>,ValueType,OtherValueType> >
+      CwiseBinaryOp<ei_scalar_difference_op<Scalar>,JacobianType,OtherJacobianType> >
+    operator-(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      return AutoDiffVector<
+      CwiseBinaryOp<ei_scalar_difference_op<Scalar>,ValueType,OtherValueType> >
+      CwiseBinaryOp<ei_scalar_difference_op<Scalar>,JacobianType,OtherJacobianType> >(
+        m_values - other.values(),
+        m_jacobian - other.jacobian());
+    }
+    
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffVector&
+    operator-=(const AutoDiffVector<OtherValueType,OtherDerType>& other)
+    {
+      m_values -= other.values();
+      m_jacobian -= other.jacobian();
+      return *this;
+    }
+    
+    inline const AutoDiffVector<
+      CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, ValueType>
+      CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, JacobianType> >
+    operator-() const
+    {
+      return AutoDiffVector<
+      CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, ValueType>
+      CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, JacobianType> >(
+        -m_values,
+        -m_jacobian);
+    }
+    
+    inline const AutoDiffVector<
+      CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
+      CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >
+    operator*(const Scalar& other) const
+    {
+      return AutoDiffVector<
+        CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
+        CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >(
+          m_values * other,
+          (m_jacobian * other));
+    }
+    
+    friend inline const AutoDiffVector<
+      CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
+      CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >
+    operator*(const Scalar& other, const AutoDiffVector& v)
+    {
+      return AutoDiffVector<
+        CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
+        CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >(
+          v.values() * other,
+          v.jacobian() * other);
+    }
+    
+//     template<typename OtherValueType,typename OtherJacobianType>
+//     inline const AutoDiffVector<
+//       CwiseBinaryOp<ei_scalar_multiple_op<Scalar>, ValueType, OtherValueType>
+//       CwiseBinaryOp<ei_scalar_sum_op<Scalar>,
+//         NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >,
+//         NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherJacobianType> > > >
+//     operator*(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
+//     {
+//       return AutoDiffVector<
+//         CwiseBinaryOp<ei_scalar_multiple_op<Scalar>, ValueType, OtherValueType>
+//         CwiseBinaryOp<ei_scalar_sum_op<Scalar>,
+//           NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >,
+//           NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherJacobianType> > > >(
+//             m_values.cwise() * other.values(),
+//             (m_jacobian * other.values()).nestByValue() + (m_values * other.jacobian()).nestByValue());
+//     }
+    
+    inline AutoDiffVector& operator*=(const Scalar& other)
+    {
+      m_values *= other;
+      m_jacobian *= other;
+      return *this;
+    }
+    
+    template<typename OtherValueType,typename OtherJacobianType>
+    inline AutoDiffVector& operator*=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
+    {
+      *this = *this * other;
+      return *this;
+    }
+    
+  protected:
+    ValueType m_values;
+    JacobianType m_jacobian;
+    
+};
+
+}
+
+#endif // EIGEN_AUTODIFF_VECTOR_H
diff --git a/unsupported/Eigen/src/CMakeLists.txt b/unsupported/Eigen/src/CMakeLists.txt
index 727b18cf5..c2f63db20 100644
--- a/unsupported/Eigen/src/CMakeLists.txt
+++ b/unsupported/Eigen/src/CMakeLists.txt
@@ -1,2 +1,3 @@
 ADD_SUBDIRECTORY(IterativeSolvers)
 ADD_SUBDIRECTORY(BVH)
+ADD_SUBDIRECTORY(AutoDiff)
diff --git a/unsupported/test/CMakeLists.txt b/unsupported/test/CMakeLists.txt
index 2e4bde3e1..16ad1e7a1 100644
--- a/unsupported/test/CMakeLists.txt
+++ b/unsupported/test/CMakeLists.txt
@@ -15,4 +15,5 @@ else(ADOLC_FOUND)
   ei_add_property(EIGEN_MISSING_BACKENDS "Adolc")
 endif(ADOLC_FOUND)
 
+ei_add_test(autodiff)
 ei_add_test(BVH)
diff --git a/unsupported/test/autodiff.cpp b/unsupported/test/autodiff.cpp
new file mode 100644
index 000000000..7d619897c
--- /dev/null
+++ b/unsupported/test/autodiff.cpp
@@ -0,0 +1,156 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#include "main.h"
+#include <unsupported/Eigen/AutoDiff>
+
+template<typename Scalar>
+EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y)
+{
+//   return x+std::sin(y);
+  asm("#mybegin");
+  return x*2 - std::pow(x,2) + 2*std::sqrt(y*y) - 4 * std::sin(x) + 2 * std::cos(y) - std::exp(-0.5*x*x);
+//   return y/x;// - y*2;
+  asm("#myend");
+}
+
+template<typename _Scalar, int NX=Dynamic, int NY=Dynamic>
+struct TestFunc1
+{
+  typedef _Scalar Scalar;
+  enum {
+    InputsAtCompileTime = NX,
+    ValuesAtCompileTime = NY
+  };
+  typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
+  typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
+  typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
+  
+  int m_inputs, m_values;
+  
+  TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  TestFunc1(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+  
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  template<typename T>
+  void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const
+  {
+    Matrix<T,ValuesAtCompileTime,1>& v = *_v;
+
+    v[0] = 2 * x[0] * x[0] + x[0] * x[1];
+    v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
+    if(inputs()>2)
+    {
+      v[0] += 0.5 * x[2];
+      v[1] += x[2];
+    }
+    if(values()>2)
+    {
+      v[2] = 3 * x[1] * x[0] * x[0];
+    }
+    if (inputs()>2 && values()>2)
+      v[2] *= x[2];
+  }
+
+  void operator() (const InputType& x, ValueType* v, JacobianType* _j) const
+  {
+    (*this)(x, v);
+
+    if(_j)
+    {
+      JacobianType& j = *_j;
+
+      j(0,0) = 4 * x[0] + x[1];
+      j(1,0) = 3 * x[1];
+
+      j(0,1) = x[0];
+      j(1,1) = 3 * x[0] + 2 * 0.5 * x[1];
+
+      if (inputs()>2)
+      {
+        j(0,2) = 0.5;
+        j(1,2) = 1;
+      }
+      if(values()>2)
+      {
+        j(2,0) = 3 * x[1] * 2 * x[0];
+        j(2,1) = 3 * x[0] * x[0];
+      }
+      if (inputs()>2 && values()>2)
+      {
+        j(2,0) *= x[2];
+        j(2,1) *= x[2];
+
+        j(2,2) = 3 * x[1] * x[0] * x[0];
+        j(2,2) = 3 * x[1] * x[0] * x[0];
+      }
+    }
+  }
+};
+
+template<typename Func> void adolc_forward_jacobian(const Func& f)
+{
+    typename Func::InputType x = Func::InputType::Random(f.inputs());
+    typename Func::ValueType y(f.values()), yref(f.values());
+    typename Func::JacobianType j(f.values(),f.inputs()), jref(f.values(),f.inputs());
+
+    jref.setZero();
+    yref.setZero();
+    f(x,&yref,&jref);
+//     std::cerr << y.transpose() << "\n\n";;
+//     std::cerr << j << "\n\n";;
+
+    j.setZero();
+    y.setZero();
+    AutoDiffJacobian<Func> autoj(f);
+    autoj(x, &y, &j);
+//     std::cerr << y.transpose() << "\n\n";;
+//     std::cerr << j << "\n\n";;
+
+    VERIFY_IS_APPROX(y, yref);
+    VERIFY_IS_APPROX(j, jref);
+}
+
+void test_autodiff()
+{
+  std::sqrt(3);
+  std::sin(3);
+  std::cerr << foo<float>(1,2) << "\n";
+  AutoDiffScalar<Vector2f> ax(1,Vector2f::UnitX());
+  AutoDiffScalar<Vector2f> ay(2,Vector2f::UnitY());
+  std::cerr << foo<AutoDiffScalar<Vector2f> >(ax,ay).value() << " <> "
+            << foo<AutoDiffScalar<Vector2f> >(ax,ay).derivatives().transpose() << "\n\n";
+  
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,2>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,3>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,2>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,3>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double>(3,3)) ));
+  }
+  
+//   exit(1);
+}
diff --git a/unsupported/test/forward_adolc.cpp b/unsupported/test/forward_adolc.cpp
index 016e20cdb..aa7618bff 100644
--- a/unsupported/test/forward_adolc.cpp
+++ b/unsupported/test/forward_adolc.cpp
@@ -28,7 +28,7 @@
 
 int adtl::ADOLC_numDir;
 
-template<typename _Scalar, int NX, int NY>
+template<typename _Scalar, int NX=Dynamic, int NY=Dynamic>
 struct TestFunc1
 {
   typedef _Scalar Scalar;
@@ -39,6 +39,14 @@ struct TestFunc1
   typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
   typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
   typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
+  
+  int m_inputs, m_values;
+  
+  TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  TestFunc1(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+  
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values }
 
   template<typename T>
   void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const
@@ -47,16 +55,16 @@ struct TestFunc1
 
     v[0] = 2 * x[0] * x[0] + x[0] * x[1];
     v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
-    if(NX>2)
+    if(inputs()>2)
     {
       v[0] += 0.5 * x[2];
       v[1] += x[2];
     }
-    if(NY>2)
+    if(values()>2)
     {
       v[2] = 3 * x[1] * x[0] * x[0];
     }
-    if (NX>2 && NY>2)
+    if (inputs()>2 && values()>2)
       v[2] *= x[2];
   }
 
@@ -74,17 +82,17 @@ struct TestFunc1
       j(0,1) = x[0];
       j(1,1) = 3 * x[0] + 2 * 0.5 * x[1];
 
-      if (NX>2)
+      if (inputs()>2)
       {
         j(0,2) = 0.5;
         j(1,2) = 1;
       }
-      if(NY>2)
+      if(values()>2)
       {
         j(2,0) = 3 * x[1] * 2 * x[0];
         j(2,1) = 3 * x[0] * x[0];
       }
-      if (NX>2 && NY>2)
+      if (inputs()>2 && values()>2)
       {
         j(2,0) *= x[2];
         j(2,1) *= x[2];
@@ -128,5 +136,6 @@ void test_forward_adolc()
     CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,3>()) ));
     CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,2>()) ));
     CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,3>()) ));
+    CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double>(3,3)) ));
   }
 }