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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).
This commit is contained in:
Gael Guennebaud
214
unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
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214
unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra. Eigen itself is part of the KDE project.
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//
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// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#ifndef EIGEN_AUTODIFF_VECTOR_H
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#define EIGEN_AUTODIFF_VECTOR_H
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namespace Eigen {
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/* \class AutoDiffScalar
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* \brief A scalar type replacement with automatic differentation capability
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*
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* \param DerType the vector type used to store/represent the derivatives (e.g. Vector3f)
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*
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* This class represents a scalar value while tracking its respective derivatives.
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*
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* It supports the following list of global math function:
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* - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos,
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* - ei_abs, ei_sqrt, ei_pow, ei_exp, ei_log, ei_sin, ei_cos,
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* - ei_conj, ei_real, ei_imag, ei_abs2.
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*
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* AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
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* in that case, the expression template mechanism only occurs at the top Matrix level,
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* while derivatives are computed right away.
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*
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*/
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template<typename ValueType, typename JacobianType>
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class AutoDiffVector
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{
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public:
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typedef typename ei_traits<ValueType>::Scalar Scalar;
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inline AutoDiffVector() {}
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inline AutoDiffVector(const ValueType& values)
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: m_values(values)
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{
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m_jacobian.setZero();
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}
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inline AutoDiffVector(const ValueType& values, const JacobianType& jac)
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: m_values(values), m_jacobian(jac)
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{}
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template<typename OtherValueType, typename OtherJacobianType>
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inline AutoDiffVector(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
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: m_values(other.values()), m_jacobian(other.jacobian())
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{}
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inline AutoDiffVector(const AutoDiffVector& other)
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: m_values(other.values()), m_jacobian(other.jacobian())
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{}
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template<typename OtherValueType, typename OtherJacobianType>
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inline AutoDiffScalar& operator=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
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{
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m_values = other.values();
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m_jacobian = other.jacobian();
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return *this;
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}
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inline AutoDiffVector& operator=(const AutoDiffVector& other)
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{
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m_values = other.values();
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m_jacobian = other.jacobian();
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return *this;
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}
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inline const ValueType& values() const { return m_values; }
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inline ValueType& values() { return m_values; }
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inline const JacobianType& jacobian() const { return m_jacobian; }
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inline JacobianType& jacobian() { return m_jacobian; }
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template<typename OtherValueType,typename OtherJacobianType>
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inline const AutoDiffVector<
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CwiseBinaryOp<ei_scalar_sum_op<Scalar>,ValueType,OtherValueType> >
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CwiseBinaryOp<ei_scalar_sum_op<Scalar>,JacobianType,OtherJacobianType> >
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operator+(const AutoDiffScalar<OtherDerType>& other) const
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{
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return AutoDiffVector<
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CwiseBinaryOp<ei_scalar_sum_op<Scalar>,ValueType,OtherValueType> >
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CwiseBinaryOp<ei_scalar_sum_op<Scalar>,JacobianType,OtherJacobianType> >(
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m_values + other.values(),
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m_jacobian + other.jacobian());
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}
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template<typename OtherValueType, typename OtherJacobianType>
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inline AutoDiffVector&
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operator+=(const AutoDiffVector<OtherValueType,OtherDerType>& other)
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{
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m_values += other.values();
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m_jacobian += other.jacobian();
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return *this;
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}
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template<typename OtherValueType,typename OtherJacobianType>
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inline const AutoDiffVector<
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CwiseBinaryOp<ei_scalar_difference_op<Scalar>,ValueType,OtherValueType> >
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CwiseBinaryOp<ei_scalar_difference_op<Scalar>,JacobianType,OtherJacobianType> >
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operator-(const AutoDiffScalar<OtherDerType>& other) const
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{
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return AutoDiffVector<
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CwiseBinaryOp<ei_scalar_difference_op<Scalar>,ValueType,OtherValueType> >
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CwiseBinaryOp<ei_scalar_difference_op<Scalar>,JacobianType,OtherJacobianType> >(
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m_values - other.values(),
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m_jacobian - other.jacobian());
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}
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template<typename OtherValueType, typename OtherJacobianType>
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inline AutoDiffVector&
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operator-=(const AutoDiffVector<OtherValueType,OtherDerType>& other)
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{
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m_values -= other.values();
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m_jacobian -= other.jacobian();
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return *this;
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}
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inline const AutoDiffVector<
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CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, ValueType>
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CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, JacobianType> >
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operator-() const
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{
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return AutoDiffVector<
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CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, ValueType>
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CwiseUnaryOp<ei_scalar_opposite_op<Scalar>, JacobianType> >(
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-m_values,
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-m_jacobian);
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}
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inline const AutoDiffVector<
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >
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operator*(const Scalar& other) const
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{
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return AutoDiffVector<
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >(
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m_values * other,
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(m_jacobian * other));
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}
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friend inline const AutoDiffVector<
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >
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operator*(const Scalar& other, const AutoDiffVector& v)
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{
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return AutoDiffVector<
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, ValueType>
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CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >(
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v.values() * other,
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v.jacobian() * other);
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}
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// template<typename OtherValueType,typename OtherJacobianType>
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// inline const AutoDiffVector<
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// CwiseBinaryOp<ei_scalar_multiple_op<Scalar>, ValueType, OtherValueType>
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// CwiseBinaryOp<ei_scalar_sum_op<Scalar>,
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// NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >,
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// NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherJacobianType> > > >
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// operator*(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
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// {
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// return AutoDiffVector<
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// CwiseBinaryOp<ei_scalar_multiple_op<Scalar>, ValueType, OtherValueType>
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// CwiseBinaryOp<ei_scalar_sum_op<Scalar>,
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// NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, JacobianType> >,
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// NestByValue<CwiseUnaryOp<ei_scalar_multiple_op<Scalar>, OtherJacobianType> > > >(
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// m_values.cwise() * other.values(),
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// (m_jacobian * other.values()).nestByValue() + (m_values * other.jacobian()).nestByValue());
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// }
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inline AutoDiffVector& operator*=(const Scalar& other)
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{
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m_values *= other;
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m_jacobian *= other;
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return *this;
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}
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template<typename OtherValueType,typename OtherJacobianType>
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inline AutoDiffVector& operator*=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
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{
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*this = *this * other;
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return *this;
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}
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protected:
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ValueType m_values;
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JacobianType m_jacobian;
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};
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}
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#endif // EIGEN_AUTODIFF_VECTOR_H
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