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).
2026-04-10 11:34:33 +08:00 · 2009-04-01 14:43:37 +00:00
parent 0f8e692b3f
commit 0170eb0dbe
8 changed files with 863 additions and 7 deletions
--- a/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
+++ 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