// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 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/.

#ifndef EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
#define EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H

// IWYU pragma: private
#include "./InternalHeaderCheck.h"

namespace Eigen {

namespace internal {

/** \internal Low-level conjugate gradient algorithm for least-square problems
 * \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 A'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 least_square_conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
                                                       const Preconditioner& precond, Index& iters,
                                                       typename Dest::RealScalar& tol_error) {
  using std::abs;
  using std::sqrt;
  typedef typename Dest::RealScalar RealScalar;
  typedef typename Dest::Scalar Scalar;
  typedef Matrix<Scalar, Dynamic, 1> VectorType;

  RealScalar tol = tol_error;
  Index maxIters = iters;

  Index m = mat.rows(), n = mat.cols();

  VectorType residual = rhs - mat * x;
  VectorType normal_residual = mat.adjoint() * residual;

  RealScalar rhsNorm2 = (mat.adjoint() * rhs).squaredNorm();
  if (rhsNorm2 == 0) {
    x.setZero();
    iters = 0;
    tol_error = 0;
    return;
  }
  RealScalar threshold = tol * tol * rhsNorm2;
  RealScalar residualNorm2 = normal_residual.squaredNorm();
  if (residualNorm2 < threshold) {
    iters = 0;
    tol_error = sqrt(residualNorm2 / rhsNorm2);
    return;
  }

  VectorType p(n);
  p = precond.solve(normal_residual);  // initial search direction

  VectorType z(n), tmp(m);
  RealScalar absNew = numext::real(normal_residual.dot(p));  // the square of the absolute value of r scaled by invM
  Index i = 0;
  while (i < maxIters) {
    tmp.noalias() = mat * p;

    Scalar alpha = absNew / tmp.squaredNorm();             // the amount we travel on dir
    x += alpha * p;                                        // update solution
    residual -= alpha * tmp;                               // update residual
    normal_residual.noalias() = mat.adjoint() * residual;  // update residual of the normal equation

    residualNorm2 = normal_residual.squaredNorm();
    if (residualNorm2 < threshold) break;

    z = precond.solve(normal_residual);  // approximately solve for "A'A z = normal_residual"

    RealScalar absOld = absNew;
    absNew = numext::real(normal_residual.dot(z));  // update the absolute value of r
    RealScalar beta = absNew / absOld;  // calculate the Gram-Schmidt value used to create the new search direction
    p = z + beta * p;                   // update search direction
    i++;
  }
  tol_error = sqrt(residualNorm2 / rhsNorm2);
  iters = i;
}

}  // namespace internal

template <typename MatrixType_,
          typename Preconditioner_ = LeastSquareDiagonalPreconditioner<typename MatrixType_::Scalar> >
class LeastSquaresConjugateGradient;

namespace internal {

template <typename MatrixType_, typename Preconditioner_>
struct traits<LeastSquaresConjugateGradient<MatrixType_, Preconditioner_> > {
  typedef MatrixType_ MatrixType;
  typedef Preconditioner_ Preconditioner;
};

}  // namespace internal

/** \ingroup IterativeLinearSolvers_Module
  * \brief A conjugate gradient solver for sparse (or dense) least-square problems
  *
  * This class solves for the least-squares solution to A x = b using an iterative conjugate gradient algorithm.
  * The matrix A can be non symmetric and rectangular, but the matrix A' A should be positive-definite to guaranty
  stability.
  * Otherwise, the SparseLU or SparseQR classes might be preferable.
  * The matrix A and the vectors x and b can be either dense or sparse.
  *
  * \tparam MatrixType_ the type of the matrix A, can be a dense or a sparse matrix.
  * \tparam Preconditioner_ the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner
  *
  * \implsparsesolverconcept
  *
  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of 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 m=1000000, n = 10000;
    VectorXd x(n), b(m);
    SparseMatrix<double> A(m,n);
    // fill A and b
    LeastSquaresConjugateGradient<SparseMatrix<double> > lscg;
    lscg.compute(A);
    x = lscg.solve(b);
    std::cout << "#iterations:     " << lscg.iterations() << std::endl;
    std::cout << "estimated error: " << lscg.error()      << std::endl;
    // update b, and solve again
    x = lscg.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.
  *
  * \sa class ConjugateGradient, SparseLU, SparseQR
  */
template <typename MatrixType_, typename Preconditioner_>
class LeastSquaresConjugateGradient
    : public IterativeSolverBase<LeastSquaresConjugateGradient<MatrixType_, Preconditioner_> > {
  typedef IterativeSolverBase<LeastSquaresConjugateGradient> Base;
  using Base::m_error;
  using Base::m_info;
  using Base::m_isInitialized;
  using Base::m_iterations;
  using Base::matrix;

 public:
  typedef MatrixType_ MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::RealScalar RealScalar;
  typedef Preconditioner_ Preconditioner;

 public:
  /** Default constructor. */
  LeastSquaresConjugateGradient() : 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.
   */
  template <typename MatrixDerived>
  explicit LeastSquaresConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}

  ~LeastSquaresConjugateGradient() {}

  /** \internal */
  template <typename Rhs, typename Dest>
  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const {
    m_iterations = Base::maxIterations();
    m_error = Base::m_tolerance;

    internal::least_square_conjugate_gradient(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
  }
};

}  // end namespace Eigen

#endif  // EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H