move sparse solvers from unsupported/ to main Eigen/ and remove the "not stable yet" warning

1 files changed, 255 insertions, 0 deletions

diff --git a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
new file mode 100644
index 000000000..ced3e310c
--- /dev/null
+++ b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -0,0 +1,255 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.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_CONJUGATE_GRADIENT_H
+#define EIGEN_CONJUGATE_GRADIENT_H
+
+namespace internal {
+
+/** \internal Low-level conjugate gradient algorithm
+  * \param mat The matrix A
+  * \param rhs The right hand side vector b
+  * \param x On input and initial solution, on output the computed solution.
+  * \param precond A preconditioner being able to efficiently solve for an
+  *                approximation of Ax=b (regardless of b)
+  * \param iters On input the max number of iteration, on output the number of performed iterations.
+  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+  */
+template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+EIGEN_DONT_INLINE
+void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
+                        const Preconditioner& precond, int& iters,
+                        typename Dest::RealScalar& tol_error)
+{
+  using std::sqrt;
+  using std::abs;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  
+  RealScalar tol = tol_error;
+  int maxIters = iters;
+  
+  int n = mat.cols();
+  VectorType residual = rhs - mat * x; //initial residual
+  VectorType p(n);
+
+  p = precond.solve(residual);      //initial search direction
+
+  VectorType z(n), tmp(n);
+  RealScalar absNew = internal::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
+  RealScalar absInit = absNew;          // the initial absolute value
+
+  int i = 0;
+  while ((i < maxIters) && (absNew > tol*tol*absInit))
+  {
+    tmp.noalias() = mat * p;              // the bottleneck of the algorithm
+
+    Scalar alpha = absNew / p.dot(tmp);   // the amount we travel on dir
+    x += alpha * p;                       // update solution
+    residual -= alpha * tmp;              // update residue
+    z = precond.solve(residual);          // approximately solve for "A z = residual"
+
+    RealScalar absOld = absNew;
+    absNew = internal::real(residual.dot(z));     // update the absolute value of r
+    RealScalar beta = absNew / absOld;            // calculate the Gram-Schmidit value used to create the new search direction
+    p = z + beta * p;                             // update search direction
+    i++;
+  }
+
+  tol_error = sqrt(abs(absNew / absInit));
+  iters = i;
+}
+
+}
+
+template< typename _MatrixType, int _UpLo=Lower,
+          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
+class ConjugateGradient;
+
+namespace internal {
+
+template< typename _MatrixType, int _UpLo, typename _Preconditioner>
+struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
+{
+  typedef _MatrixType MatrixType;
+  typedef _Preconditioner Preconditioner;
+};
+
+}
+
+/** \brief A conjugate gradient solver for sparse self-adjoint problems
+  *
+  * This class allows to solve for A.x = b sparse linear problems using a conjugate gradient algorithm.
+  * The sparse matrix A must be selfadjoint. The vectors x and b can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The default are 1000 max iterations and NumTraits<Scalar>::epsilon()
+  * for the tolerance.
+  * 
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+  * \code
+  * int n = 10000;
+  * VectorXd x(n), b(n);
+  * SparseMatrix<double> A(n,n);
+  * // fill A and b
+  * ConjugateGradient<SparseMatrix<double> > cg;
+  * cg(A);
+  * x = cg.solve(b);
+  * std::cout << "#iterations:     " << cg.iterations() << std::endl;
+  * std::cout << "estimated error: " << cg.error()      << std::endl;
+  * // update b, and solve again
+  * x = cg.solve(b);
+  * \endcode
+  * 
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method. Here is a step by
+  * step execution example starting with a random guess and printing the evolution
+  * of the estimated error:
+  * * \code
+  * x = VectorXd::Random(n);
+  * cg.setMaxIterations(1);
+  * int i = 0;
+  * do {
+  *   x = cg.solveWithGuess(b,x);
+  *   std::cout << i << " : " << cg.error() << std::endl;
+  *   ++i;
+  * } while (cg.info()!=Success && i<100);
+  * \endcode
+  * Note that such a step by step excution is slightly slower.
+  * 
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename _MatrixType, int _UpLo, typename _Preconditioner>
+class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
+{
+  typedef IterativeSolverBase<ConjugateGradient> Base;
+  using Base::mp_matrix;
+  using Base::m_error;
+  using Base::m_iterations;
+  using Base::m_info;
+  using Base::m_isInitialized;
+public:
+  typedef _MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef _Preconditioner Preconditioner;
+
+  enum {
+    UpLo = _UpLo
+  };
+
+public:
+
+  /** Default constructor. */
+  ConjugateGradient() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  ConjugateGradient(const MatrixType& A) : Base(A) {}
+
+  ~ConjugateGradient() {}
+  
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
+    * \a x0 as an initial solution.
+    *
+    * \sa compute()
+    */
+  template<typename Rhs,typename Guess>
+  inline const internal::solve_retval_with_guess<ConjugateGradient, Rhs, Guess>
+  solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const
+  {
+    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
+    eigen_assert(Base::rows()==b.rows()
+              && "ConjugateGradient::solve(): invalid number of rows of the right hand side matrix b");
+    return internal::solve_retval_with_guess
+            <ConjugateGradient, Rhs, Guess>(*this, b.derived(), x0);
+  }
+
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solveWithGuess(const Rhs& b, Dest& x) const
+  {
+    m_iterations = Base::m_maxIterations;
+    m_error = Base::m_tolerance;
+
+    for(int j=0; j<b.cols(); ++j)
+    {
+      m_iterations = Base::m_maxIterations;
+      m_error = Base::m_tolerance;
+
+      typename Dest::ColXpr xj(x,j);
+      internal::conjugate_gradient(mp_matrix->template selfadjointView<UpLo>(), b.col(j), xj,
+                                   Base::m_preconditioner, m_iterations, m_error);
+    }
+
+    m_isInitialized = true;
+    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+  
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve(const Rhs& b, Dest& x) const
+  {
+    x.setOnes();
+    _solveWithGuess(b,x);
+  }
+
+protected:
+
+};
+
+
+namespace internal {
+
+template<typename _MatrixType, int _UpLo, typename _Preconditioner, typename Rhs>
+struct solve_retval<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner>, Rhs>
+  : solve_retval_base<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner>, Rhs>
+{
+  typedef ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+}
+
+#endif // EIGEN_CONJUGATE_GRADIENT_H