LeastSquareConjugateGradient.h

// 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/.
// SPDX-License-Identifier: 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 {

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::sqrt;
  using RealScalar = typename Dest::RealScalar;
  using Scalar = typename Dest::Scalar;
  using VectorType = Matrix<Scalar, Dynamic, 1>;
 
  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;
  VectorType normal_rhs = mat.adjoint() * rhs;
 
  RealScalar rhsNorm = normal_rhs.stableNorm();
  if (rhsNorm == 0) {
    x.setZero();
    iters = 0;
    tol_error = 0;
    return;
  }
  RealScalar threshold = tol * rhsNorm;
  RealScalar residualNorm = normal_residual.stableNorm();
  if (residualNorm == 0 || residualNorm < threshold) {
    iters = 0;
    tol_error = residualNorm / rhsNorm;
    return;
  }
 
  // Keep the quadratic recurrence terms representable for very small or large residuals.
  const RealScalar residualScale = internal::iterative_solver_scaling_factor(residualNorm);
  normal_residual /= residualScale;
  threshold /= residualScale;
 
  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 += (residualScale * alpha) * p;                      // update solution
    residual -= (residualScale * alpha) * tmp;             // update residual
    normal_residual.noalias() = mat.adjoint() * residual;  // update residual of the normal equation
    normal_residual /= residualScale;
 
    residualNorm = normal_residual.stableNorm();
    if (residualNorm < 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 = residualNorm / (rhsNorm / residualScale);
  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_> > {
  using MatrixType = MatrixType_;
  using Preconditioner = Preconditioner_;
};
 
}  // namespace internal

template <typename MatrixType_, typename Preconditioner_>
class LeastSquaresConjugateGradient
    : public IterativeSolverBase<LeastSquaresConjugateGradient<MatrixType_, Preconditioner_> > {
 protected:
  using Base = IterativeSolverBase<LeastSquaresConjugateGradient>;
  using Base::m_error;
  using Base::m_info;
  using Base::m_isInitialized;
  using Base::m_iterations;
  using Base::matrix;
 
 public:
  using MatrixType = MatrixType_;
  using Scalar = typename MatrixType::Scalar;
  using RealScalar = typename MatrixType::RealScalar;
  using Preconditioner = Preconditioner_;
 
 public:
  LeastSquaresConjugateGradient() : Base() {}

  template <typename MatrixDerived>
  explicit LeastSquaresConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}

  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