complex_smoother.h

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// LIC// ====================================================================
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// LIC// Copyright (C) 2006-2023 Matthias Heil and Andrew Hazel
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// Include guards
#ifndef OOMPH_COMPLEX_SMOOTHER_HEADER
#define OOMPH_COMPLEX_SMOOTHER_HEADER
 
// Config header generated by autoconfig
#ifdef HAVE_CONFIG_H
#include <oomph-lib-config.h>
#endif
 
// Namespace extension
namespace oomph
{
  //====================================================================
  /// Helmholtz smoother class:
  /// The smoother class is designed for the Helmholtz equation to be used
  /// in conjunction with multigrid. The action of the smoother should
  /// reduce the high frequency errors. These methods are inefficient as
  /// stand-alone solvers
  //====================================================================
  class HelmholtzSmoother : public IterativeLinearSolver
  {
  public:
    /// Empty constructor
    HelmholtzSmoother() : Use_as_smoother(false) {}
 
    /// Virtual empty destructor
    virtual ~HelmholtzSmoother(){};
 
    /// The smoother_solve function performs fixed number of iterations
    /// on the system A*result=rhs. The number of (smoothing) iterations is
    /// the same as the max. number of iterations in the underlying
    /// IterativeLinearSolver class.
    virtual void complex_smoother_solve(const Vector<DoubleVector>& rhs,
                                        Vector<DoubleVector>& result) = 0;
 
 
    /// Setup the smoother for the matrix specified by the pointer
    virtual void complex_smoother_setup(Vector<CRDoubleMatrix*> matrix_pt) = 0;
 
    /// Helper function to calculate a complex matrix-vector product.
    /// Assumes the matrix has been provided as a Vector of length two; the
    /// first entry containing the real part of the system matrix and the
    /// second entry containing the imaginary part
    void complex_matrix_multiplication(Vector<CRDoubleMatrix*> matrices_pt,
                                       const Vector<DoubleVector>& x,
                                       Vector<DoubleVector>& soln)
    {
#ifdef PARANOID
      // PARANOID check - Make sure the input matrix has the right size
      if (matrices_pt.size() != 2)
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create the error message
        error_message_stream << "Can only deal with two matrices. You have "
                             << matrices_pt.size() << " matrices." << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check - Make sure the vector x has the right size
      if (x.size() != 2)
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create the error message
        error_message_stream
          << "Can only deal with two input vectors. You have " << x.size()
          << " vectors." << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check - Make sure the vector soln has the right size
      if (soln.size() != 2)
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create the error message
        error_message_stream
          << "Can only deal with two output vectors. You have " << soln.size()
          << " output vectors." << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      //-----------------------------------------------------------------------
      // Suppose we have a complex matrix, A, and two complex vectors, x and
      // soln. We wish to compute the product A*x=soln (note, * does not mean
      // we are using complex conjugates here, it is simply used to indicate
      // a multiplication). To do this we must make use of the fact that we
      // possess the real and imaginary separately. As a result, it is computed
      // using:
      //           soln = A*x,
      //                = (A_r + i*A_c)*(x_r + i*x_c),
      //                = [A_r*x_r - A_c*x_c] + i*[A_r*x_c + A_c*x_r],
      // ==> real(soln) = A_r*x_r - A_c*x_c,
      //   & imag(soln) = A_r*x_c + A_c*x_r,
      // where the subscripts _r and _c are used to identify the real and
      // imaginary part, respectively.
      //-----------------------------------------------------------------------
 
      // Store the value of A_r*x_r in the real part of soln
      matrices_pt[0]->multiply(x[0], soln[0]);
 
      // Store the value of A_r*x_c in the imaginary part of soln
      matrices_pt[0]->multiply(x[1], soln[1]);
 
      // Create a temporary vector
      DoubleVector temp(this->distribution_pt(), 0.0);
 
      // Calculate the value of A_c*x_c
      matrices_pt[1]->multiply(x[1], temp);
 
      // Subtract the value of temp from soln[0] to get the real part of soln
      soln[0] -= temp;
 
      // Calculate the value of A_c*x_r
      matrices_pt[1]->multiply(x[0], temp);
 
      // Add the value of temp to soln[1] to get the imaginary part of soln
      soln[1] += temp;
    } // End of complex_matrix_multiplication
 
    /// Self-test to check that all the dimensions of the inputs to
    /// solve helper are consistent and everything that needs to be built, is.
    template<typename MATRIX>
    void check_validity_of_solve_helper_inputs(
      CRDoubleMatrix* const& real_matrix_pt,
      CRDoubleMatrix* const& imag_matrix_pt,
      const Vector<DoubleVector>& rhs,
      Vector<DoubleVector>& solution,
      const double& n_dof);
 
  protected:
    /// When a derived class object is being used as a smoother in
    /// the MG algorithm the residual norm
    /// does not need to be calculated. This boolean is used as a flag to
    /// indicate this in solve_helper(...)
    bool Use_as_smoother;
  };
 
  //==================================================================
  /// Self-test to be called inside solve_helper to ensure
  /// that all inputs are consistent and everything that needs to
  /// be built, is.
  //==================================================================
  template<typename MATRIX>
  void HelmholtzSmoother::check_validity_of_solve_helper_inputs(
    CRDoubleMatrix* const& real_matrix_pt,
    CRDoubleMatrix* const& imag_matrix_pt,
    const Vector<DoubleVector>& rhs,
    Vector<DoubleVector>& solution,
    const double& n_dof)
  {
    // Number of dof types should be 2 (real & imaginary)
    unsigned n_dof_types = 2;
 
    // Create a vector to hold the matrices
    Vector<CRDoubleMatrix*> matrix_storage_pt(2, 0);
 
    // Assign the first entry in matrix_storage_pt
    matrix_storage_pt[0] = real_matrix_pt;
 
    // Assign the second entry in matrix_storage_pt
    matrix_storage_pt[1] = imag_matrix_pt;
 
    // Loop over the real and imaginary parts
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Check if the matrix is distributable. If it is then it should
      // not be distributed
      if (dynamic_cast<DistributableLinearAlgebraObject*>(
            matrix_storage_pt[dof_type]) != 0)
      {
        if (dynamic_cast<DistributableLinearAlgebraObject*>(
              matrix_storage_pt[dof_type])
              ->distributed())
        {
          std::ostringstream error_message_stream;
          error_message_stream << "The matrix must not be distributed.";
          throw OomphLibError(error_message_stream.str(),
                              OOMPH_CURRENT_FUNCTION,
                              OOMPH_EXCEPTION_LOCATION);
        }
      }
      // Check that this rhs distribution is setup
      if (!(rhs[dof_type].built()))
      {
        std::ostringstream error_message_stream;
        error_message_stream << "The rhs vector distribution must be setup.";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // Check that the rhs has the right number of global rows
      if (rhs[dof_type].nrow() != n_dof)
      {
        std::ostringstream error_message_stream;
        error_message_stream << "RHS does not have the same dimension as the "
                             << "linear system";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // Check that the rhs is not distributed
      if (rhs[dof_type].distribution_pt()->distributed())
      {
        std::ostringstream error_message_stream;
        error_message_stream << "The rhs vector must not be distributed.";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // Check that if the result is setup it matches the distribution
      // of the rhs
      if (solution[dof_type].built())
      {
        if (!(*rhs[dof_type].distribution_pt() ==
              *solution[dof_type].distribution_pt()))
        {
          std::ostringstream error_message_stream;
          error_message_stream << "If the result distribution is setup then it "
                               << "must be the same as the rhs distribution";
          throw OomphLibError(error_message_stream.str(),
                              OOMPH_CURRENT_FUNCTION,
                              OOMPH_EXCEPTION_LOCATION);
        }
      } // if ((solution[0].built())||(solution[1].built()))
    } // for (unsigned dof_type=0;dof_type<n_dof_types;dof_type)
  } // End of check_validity_of_solve_helper_inputs
 
 
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
 
 
  //=========================================================================
  /// Damped Jacobi "solver" templated by matrix type. The "solver"
  /// exists in many different incarnations: It's an IterativeLinearSolver,
  /// and a Smoother, all of which use the same basic iteration.
  //=========================================================================
  template<typename MATRIX>
  class ComplexDampedJacobi : public HelmholtzSmoother
  {
  public:
    /// Constructor (empty)
    ComplexDampedJacobi(const double& omega = 0.5)
      : Matrix_can_be_deleted(true),
        Matrix_real_pt(0),
        Matrix_imag_pt(0),
        Omega(omega){};
 
    /// Empty destructor
    ~ComplexDampedJacobi()
    {
      // Run clean_up_memory
      clean_up_memory();
    } // End of ~ComplexDampedJacobi
 
    /// Cleanup data that's stored for resolve (if any has been stored)
    void clean_up_memory()
    {
      // If the real matrix pointer isn't null AND we're allowed to delete
      // the matrix which is only when we create the matrix ourselves
      if ((Matrix_real_pt != 0) && (Matrix_can_be_deleted))
      {
        // Delete the matrix
        delete Matrix_real_pt;
 
        // Assign the associated pointer the value NULL
        Matrix_real_pt = 0;
      }
 
      // If the real matrix pointer isn't null AND we're allowed to delete
      // the matrix which is only when we create the matrix ourselves
      if ((Matrix_imag_pt != 0) && (Matrix_can_be_deleted))
      {
        // Delete the matrix
        delete Matrix_imag_pt;
 
        // Assign the associated pointer the value NULL
        Matrix_imag_pt = 0;
      }
    } // End of clean_up_memory
 
    /// Broken copy constructor
    ComplexDampedJacobi(const ComplexDampedJacobi&) = delete;
 
    /// Broken assignment operator
    void operator=(const ComplexDampedJacobi&) = delete;
 
    /// Function to calculate the value of Omega by passing in the
    /// value of k and h [see Elman et al. "A multigrid method enhanced by
    /// Krylov subspace iteration for discrete Helmholtz equations", p.1303]
    void calculate_omega(const double& k, const double& h)
    {
      // Create storage for the parameter kh
      double kh = k * h;
 
      // Store the value of pi
      double pi = MathematicalConstants::Pi;
 
      // Calculate the value of Omega
      double omega = (12.0 - 4.0 * pow(kh, 2.0)) / (18.0 - 3.0 * pow(kh, 2.0));
 
      // Set the tolerance for how close omega can be to 0
      double tolerance = 1.0e-03;
 
      // Only store the value of omega if it lies in the range (0,1]. If it
      // isn't it will produce a divergent scheme. Note, to avoid letting
      // omega become too small we make sure it is greater than some tolerance
      if ((omega > tolerance) && !(omega > 1))
      {
        // Since omega lies in the specified range, store it
        Omega = omega;
      }
      // On the coarsest grids: if the wavenumber is greater than pi and
      // kh>2cos(pi*h/2) then we can choose omega from the range (0,omega_2)
      // where omega_2 is defined in [Elman et al."A multigrid method
      // enhanced by Krylov subspace iteration for discrete Helmholtz
      // equations", p.1295]
      else if ((k > pi) && (kh > 2 * cos(pi * h / 2)))
      {
        // Calculate the numerator of omega_2
        double omega_2 = (2.0 - pow(kh, 2.0));
 
        // Divide by the denominator of the fraction
        omega_2 /= (2.0 * pow(sin(pi * h / 2), 2.0) - 0.5 * pow(kh, 2.0));
 
        // If 2/3 lies in the range (0,omega_2), use it
        if (omega_2 > (2.0 / 3.0))
        {
          // Assign Omega the damping factor used for the Poisson equation
          Omega = 2.0 / 3.0;
        }
        // If omega_2 is less than 2/3 use the midpoint of (tolerance,omega_2)
        else
        {
          // Set the value of Omega
          Omega = 0.5 * (tolerance + omega_2);
        }
      }
      // Since the value of kh must be fairly large, make the value of
      // omega small to compensate
      else
      {
        // Since kh doesn't lie in the chosen range, set it to some small value
        Omega = 0.2;
      }
    } // End of calculate_omega
 
    /// Get access to the value of Omega (lvalue)
    double& omega()
    {
      // Return the value of Omega
      return Omega;
    } // End of omega
 
    /// Setup: Pass pointer to the matrix and store in cast form
    void complex_smoother_setup(Vector<CRDoubleMatrix*> helmholtz_matrix_pt)
    {
      // Assume the matrices have been passed in from the outside so we must
      // not delete it. This is needed to avoid pre- and post-smoothers
      // deleting the same matrix in the MG solver. If this was originally
      // set to TRUE then this will be sorted out in the other functions
      // from which this was called
      Matrix_can_be_deleted = false;
 
      // Assign the real matrix pointers
      Matrix_real_pt = helmholtz_matrix_pt[0];
 
      // Assign the imaginary matrix pointers
      Matrix_imag_pt = helmholtz_matrix_pt[1];
 
#ifdef PARANOID
      // PARANOID check that if the matrix is distributable. If it is not then
      // it should not be distributed
      if (Matrix_real_pt->nrow() != Matrix_imag_pt->nrow())
      {
        std::ostringstream error_message_stream;
        error_message_stream << "Incompatible real and complex matrix sizes.";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      //--------------------------------------------------------------------
      // We need the matrix diagonals to calculate inv(D) (where D is the
      // diagonal of A) as it remains the same for each use of the iterative
      // scheme. To avoid unnecessary computations we store it now so it can
      // simply be called in each iteration.
      //--------------------------------------------------------------------
 
      // Grab the diagonal entries of the real part of the system matrix
      Matrix_diagonal_real = Matrix_real_pt->diagonal_entries();
 
      // Grab the diagonal entries of the imaginary part of the system matrix
      Matrix_diagonal_imag = Matrix_imag_pt->diagonal_entries();
 
      // Find the number of entries in the vectors containing the diagonal
      // entries of both the real and the imaginary matrix
      unsigned n_dof = Matrix_diagonal_real.size();
 
      // Make a dummy vector to store the entries of Matrix_diagonal_inv_sq
      Matrix_diagonal_inv_sq.resize(n_dof, 0.0);
 
      // Create a helper variable to store A_r(j,j), for some j
      double dummy_r;
 
      // Create a helper variable to store A_c(j,j), for some j
      double dummy_c;
 
      // Loop over the entries of Matrix_diagonal_inv_sq and set the i-th
      // entry to 1/(A_r(i,i)^2 + A_c(i,i)^2)
      for (unsigned i = 0; i < n_dof; i++)
      {
        // Store the value of A_r(i,i)
        dummy_r = Matrix_diagonal_real[i];
 
        // Store the value of A_c(i,i)
        dummy_c = Matrix_diagonal_imag[i];
 
        // Store the value of 1/(A_r(i,i)^2 + A_c(i,i)^2)
        Matrix_diagonal_inv_sq[i] =
          1.0 / (dummy_r * dummy_r + dummy_c * dummy_c);
      }
    } // End of complex_smoother_setup
 
    /// The smoother_solve function performs fixed number of iterations
    /// on the system A*result=rhs. The number of (smoothing) iterations is
    /// the same as the max. number of iterations in the underlying
    /// IterativeLinearSolver class.
    void complex_smoother_solve(const Vector<DoubleVector>& rhs,
                                Vector<DoubleVector>& solution)
    {
      // If you use a smoother but you don't want to calculate the residual
      Use_as_smoother = true;
 
      // Call the helper function
      complex_solve_helper(rhs, solution);
    } // End of complex_smoother_solve
 
    /// Use damped Jacobi iteration as an IterativeLinearSolver:
    /// This obtains the Jacobian matrix J and the residual vector r
    /// (needed for the Newton method) from the problem's get_jacobian
    /// function and returns the result of Jx=r.
    void solve(Problem* const& problem_pt, DoubleVector& result)
    {
      BrokenCopy::broken_assign("ComplexDampedJacobi");
    }
 
    /// Number of iterations taken
    unsigned iterations() const
    {
      return Iterations;
    }
 
  private:
    /// This is where the actual work is done
    void complex_solve_helper(const Vector<DoubleVector>& rhs,
                              Vector<DoubleVector>& solution);
 
    /// Boolean flag to indicate if the matrices pointed to by
    /// Matrix_real_pt and Matrix_imag_pt can be deleted.
    bool Matrix_can_be_deleted;
 
    /// Pointer to the real part of the system matrix
    CRDoubleMatrix* Matrix_real_pt;
 
    /// Pointer to the real part of the system matrix
    CRDoubleMatrix* Matrix_imag_pt;
 
    /// Vector containing the diagonal entries of A_r (real(A))
    Vector<double> Matrix_diagonal_real;
 
    /// Vector containing the diagonal entries of A_c (imag(A))
    Vector<double> Matrix_diagonal_imag;
 
    /// Vector whose j'th entry is given by 1/(A_r(j,j)^2 + A_c(j,j)^2)
    Vector<double> Matrix_diagonal_inv_sq;
 
    /// Number of iterations taken
    unsigned Iterations;
 
    /// Damping factor
    double Omega;
  };
 
  //======================================================================
  /// This is where the actual work is done.
  //======================================================================
  template<typename MATRIX>
  void ComplexDampedJacobi<MATRIX>::complex_solve_helper(
    const Vector<DoubleVector>& rhs, Vector<DoubleVector>& solution)
  {
    // Get number of dofs
    unsigned n_dof = Matrix_real_pt->nrow();
 
#ifdef PARANOID
    // Upcast the matrix to the appropriate type
    CRDoubleMatrix* tmp_rmatrix_pt =
      dynamic_cast<CRDoubleMatrix*>(Matrix_real_pt);
 
    // Upcast the matrix to the appropriate type
    CRDoubleMatrix* tmp_imatrix_pt =
      dynamic_cast<CRDoubleMatrix*>(Matrix_imag_pt);
 
    // PARANOID Run the self-tests to check the inputs are correct
    this->check_validity_of_solve_helper_inputs<MATRIX>(
      tmp_rmatrix_pt, tmp_imatrix_pt, rhs, solution, n_dof);
 
    // We don't need the real matrix pointer any more
    tmp_rmatrix_pt = 0;
 
    // We don't need the imaginary matrix pointer any more
    tmp_imatrix_pt = 0;
#endif
 
    // Setup the solution if it is not
    if ((!solution[0].distribution_pt()->built()) ||
        (!solution[1].distribution_pt()->built()))
    {
      solution[0].build(rhs[0].distribution_pt(), 0.0);
      solution[1].build(rhs[1].distribution_pt(), 0.0);
    }
 
    // Initialise timer
    double t_start = TimingHelpers::timer();
 
    // Copy the real and imaginary part of the solution vector
    DoubleVector x_real(solution[0]);
    DoubleVector x_imag(solution[1]);
 
    // Copy the real and imaginary part of the RHS vector
    DoubleVector rhs_real(rhs[0]);
    DoubleVector rhs_imag(rhs[1]);
 
    // Create storage for the real and imaginary part of the constant term
    DoubleVector constant_term_real(this->distribution_pt(), 0.0);
    DoubleVector constant_term_imag(this->distribution_pt(), 0.0);
 
    // Create storage for the real and imaginary part of the residual vector.
    // These aren't used/built if we're inside the multigrid solver
    DoubleVector residual_real;
    DoubleVector residual_imag;
 
    // Create storage for the norm of the real and imaginary parts of the
    // residual vector. These aren't used if we're inside the multigrid
    // solver
    double res_real_norm = 0.0;
    double res_imag_norm = 0.0;
 
    // Variable to hold the current residual norm. This isn't used if
    // we're inside the multigrid solver
    double norm_res = 0.0;
 
    // Variables to hold the initial residual norm. This isn't used if
    // we're inside the multigrid solver
    double norm_f = 0.0;
 
    // Initialise the value of Iterations
    Iterations = 0;
 
    //------------------------------------------------------------------------
    // Initial guess isn't necessarily zero (restricted solution from finer
    // grids) therefore both x and the residual need to be assigned values
    // from inputs. The constant term at the end of the stationary iteration
    // is also calculated here since it does not change at all throughout the
    // iterative process:
    //------------------------------------------------------------------------
 
    // Loop over all the entries of each vector
    for (unsigned i = 0; i < n_dof; i++)
    {
      // Calculate the numerator of the i'th entry in the real component of
      // the constant term
      constant_term_real[i] = (Matrix_diagonal_real[i] * rhs_real[i] +
                               Matrix_diagonal_imag[i] * rhs_imag[i]);
 
      // Divide by the denominator
      constant_term_real[i] *= Matrix_diagonal_inv_sq[i];
 
      // Scale by the damping factor
      constant_term_real[i] *= Omega;
 
      // Calculate the numerator of the i'th entry in the imaginary component of
      // the constant term
      constant_term_imag[i] = (Matrix_diagonal_real[i] * rhs_imag[i] -
                               Matrix_diagonal_imag[i] * rhs_real[i]);
 
      // Divide by the denominator
      constant_term_imag[i] *= Matrix_diagonal_inv_sq[i];
 
      // Scale by the damping factor
      constant_term_imag[i] *= Omega;
    }
 
    // Create 4 temporary vectors to store the various matrix-vector products
    // required. The appropriate combination has been appended to the name. For
    // instance, the product A_r*x_c corresponds to temp_vec_rc (real*imag)
    DoubleVector temp_vec_rr(this->distribution_pt(), 0.0);
    DoubleVector temp_vec_cc(this->distribution_pt(), 0.0);
    DoubleVector temp_vec_rc(this->distribution_pt(), 0.0);
    DoubleVector temp_vec_cr(this->distribution_pt(), 0.0);
 
    // Calculate the different combinations of A*x (or A*e depending on the
    // level in the heirarchy) in the complex case (i.e. A_r*x_r, A_c*x_c,
    // A_r*x_c and A_c*x_r)
    Matrix_real_pt->multiply(x_real, temp_vec_rr);
    Matrix_imag_pt->multiply(x_imag, temp_vec_cc);
    Matrix_real_pt->multiply(x_imag, temp_vec_rc);
    Matrix_imag_pt->multiply(x_real, temp_vec_cr);
 
    //---------------------------------------------------------------------------
    // Calculating the residual r=b-Ax in the complex case requires more care
    // than the real case. The correct calculation can be derived rather easily.
    // Consider the splitting of A, x and b into their complex components:
    //           r = b - A*x
    //             = (b_r + i*b_c) - (A_r + i*A_c)*(x_r + i*x_c)
    //             = [b_r - A_r*x_r + A_c*x_c] + i*[b_c - A_r*x_c - A_c*x_r]
    // ==> real(r) = b_r - A_r*x_r + A_c*x_c
    //   & imag(r) = b_c - A_r*x_c - A_c*x_r.
    //---------------------------------------------------------------------------
 
    // Calculate the residual only if we're not inside the multigrid solver
    if (!Use_as_smoother)
    {
      // Create storage for the real and imaginary part of the residual vector
      residual_real.build(this->distribution_pt(), 0.0);
      residual_imag.build(this->distribution_pt(), 0.0);
 
      // Real part of the residual:
      residual_real = rhs_real;
      residual_real -= temp_vec_rr;
      residual_real += temp_vec_cc;
 
      // Imaginary part of the residual:
      residual_imag = rhs_imag;
      residual_imag -= temp_vec_rc;
      residual_imag -= temp_vec_cr;
 
      // Calculate the 2-norm (noting that the 2-norm of a complex vector a of
      // length n is simply the square root of the sum of the squares of each
      // real and imaginary component). That is:
      /// \f[ \|a\|_2^2=\sum_{i=0}^{i=n-1}\Re(a[i])^2+\Im(a[i])^2. \f]
      // can be written as:
      /// \f[ \|a\|_2^2=\|\Re(a)\|_2^2+\|\Im(a)\|_2^2. \f]
      double res_real_norm = residual_real.norm();
      double res_imag_norm = residual_imag.norm();
      double norm_res =
        sqrt(res_real_norm * res_real_norm + res_imag_norm * res_imag_norm);
 
      // If required will document convergence history to screen
      // or file (if stream is open)
      if (Doc_convergence_history)
      {
        if (!Output_file_stream.is_open())
        {
          oomph_info << Iterations << " " << norm_res << std::endl;
        }
        else
        {
          Output_file_stream << Iterations << " " << norm_res << std::endl;
        }
      } // if (Doc_convergence_history)
    } // if (!Use_as_smoother)
 
    // Two temporary vectors to store the value of A_r*x_r - A_c*x_c and
    // A_c*x_r + A_r*x_c in each iteration
    DoubleVector temp_vec_real(this->distribution_pt(), 0.0);
    DoubleVector temp_vec_imag(this->distribution_pt(), 0.0);
 
    // Calculate A_r*x_r - A_c*x_c
    temp_vec_real = temp_vec_rr;
    temp_vec_real -= temp_vec_cc;
 
    // Calculate A_c*x_r + A_r*x_c
    temp_vec_imag = temp_vec_cr;
    temp_vec_imag += temp_vec_rc;
 
    // Outermost loop: Run up to Max_iter times
    for (unsigned iter_num = 0; iter_num < Max_iter; iter_num++)
    {
      // Loop over each degree of freedom and update
      // the current approximation
      for (unsigned i = 0; i < n_dof; i++)
      {
        // Make a couple of dummy variables to help with calculations
        double dummy_r = 0.0;
        double dummy_c = 0.0;
 
        // Calculate one part of the correction term (real)
        dummy_r = (Matrix_diagonal_real[i] * temp_vec_real[i] +
                   Matrix_diagonal_imag[i] * temp_vec_imag[i]);
 
        // Calculate one part of the correction term (imaginary)
        dummy_c = (Matrix_diagonal_real[i] * temp_vec_imag[i] -
                   Matrix_diagonal_imag[i] * temp_vec_real[i]);
 
        // Scale by Omega/([A(i,i)_r]^2+[A(i,i)_c]^2)
        dummy_r *= Omega * Matrix_diagonal_inv_sq[i];
        dummy_c *= Omega * Matrix_diagonal_inv_sq[i];
 
        // Update the value of x_real
        x_real[i] -= dummy_r;
        x_imag[i] -= dummy_c;
      }
 
      // Update the value of x (or e depending on the level in the heirarchy)
      x_real += constant_term_real;
      x_imag += constant_term_imag;
 
      // Calculate the different combinations of A*x (or A*e depending on the
      // level in the heirarchy) in the complex case (i.e. A_r*x_r, A_c*x_c,
      // A_r*x_c and A_c*x_r)
      Matrix_real_pt->multiply(x_real, temp_vec_rr);
      Matrix_imag_pt->multiply(x_imag, temp_vec_cc);
      Matrix_real_pt->multiply(x_imag, temp_vec_rc);
      Matrix_imag_pt->multiply(x_real, temp_vec_cr);
 
      // Calculate A_r*x_r - A_c*x_c
      temp_vec_real = temp_vec_rr;
      temp_vec_real -= temp_vec_cc;
 
      // Calculate A_c*x_r + A_r*x_c
      temp_vec_imag = temp_vec_cr;
      temp_vec_imag += temp_vec_rc;
 
      // Calculate the residual only if we're not inside the multigrid solver
      if (!Use_as_smoother)
      {
        // Calculate the residual
        residual_real = rhs_real;
        residual_real -= temp_vec_rr;
        residual_real += temp_vec_cc;
 
        // Calculate the imaginary part of the residual vector
        residual_imag = rhs_imag;
        residual_imag -= temp_vec_rc;
        residual_imag -= temp_vec_cr;
 
        // Calculate the 2-norm using the method mentioned previously
        res_real_norm = residual_real.norm();
        res_imag_norm = residual_imag.norm();
        norm_res =
          sqrt(res_real_norm * res_real_norm + res_imag_norm * res_imag_norm) /
          norm_f;
 
        // If required, this will document convergence history to
        // screen or file (if the stream is open)
        if (Doc_convergence_history)
        {
          if (!Output_file_stream.is_open())
          {
            oomph_info << Iterations << " " << norm_res << std::endl;
          }
          else
          {
            Output_file_stream << Iterations << " " << norm_res << std::endl;
          }
        } // if (Doc_convergence_history)
 
        // Check the tolerance only if the residual norm is being computed
        if (norm_res < Tolerance)
        {
          // Break out of the for-loop
          break;
        }
      } // if (!Use_as_smoother)
    } // for (unsigned iter_num=0;iter_num<Max_iter;iter_num++)
 
    // Calculate the residual only if we're not inside the multigrid solver
    if (!Use_as_smoother)
    {
      // If time documentation is enabled
      if (Doc_time)
      {
        oomph_info << "\n(Complex) damped Jacobi converged. Residual norm: "
                   << norm_res
                   << "\nNumber of iterations to convergence: " << Iterations
                   << "\n"
                   << std::endl;
      }
    } // if (!Use_as_smoother)
 
    // Copy the solution into the solution vector
    solution[0] = x_real;
    solution[1] = x_imag;
 
    // Doc time for solver
    double t_end = TimingHelpers::timer();
    Solution_time = t_end - t_start;
    if (Doc_time)
    {
      oomph_info << "Time for solve with (complex) damped Jacobi [sec]: "
                 << Solution_time << std::endl;
    }
 
    // If the solver failed to converge and the user asked for an error if
    // this happened
    if ((Iterations > Max_iter - 1) && (Throw_error_after_max_iter))
    {
      std::string error_message =
        "Solver failed to converge and you requested ";
      error_message += "an error on convergence failures.";
      throw OomphLibError(
        error_message, OOMPH_EXCEPTION_LOCATION, OOMPH_CURRENT_FUNCTION);
    }
  } // End of complex_solve_helper function
 
  //======================================================================
  /// The GMRES method rewritten for complex matrices
  //======================================================================
  template<typename MATRIX>
  class ComplexGMRES : public HelmholtzSmoother
  {
  public:
    /// Constructor
    ComplexGMRES()
      : Iterations(0),
        Matrices_storage_pt(0),
        Resolving(false),
        Matrix_can_be_deleted(true)
    {
    } // End of ComplexGMRES constructor
 
    /// Empty destructor
    ~ComplexGMRES()
    {
      // Run clean_up_memory
      clean_up_memory();
    } // End of ~ComplexGMRES
 
    /// Broken copy constructor
    ComplexGMRES(const ComplexGMRES&) = delete;
 
    /// Broken assignment operator
    void operator=(const ComplexGMRES&) = delete;
 
    /// Overload disable resolve so that it cleans up memory too
    void disable_resolve()
    {
      // Disable resolve using the LinearSolver function
      LinearSolver::disable_resolve();
 
      // Clean up anything kept in memory
      clean_up_memory();
    } // End of disable resolve
 
    /// Solver: Takes pointer to problem and returns the results vector
    /// which contains the solution of the linear system defined by
    /// the problem's fully assembled Jacobian and residual vector.
    void solve(Problem* const& problem_pt, DoubleVector& result)
    {
      // Write the error message into a string
      std::string error_message = "Solve function for class\n\n";
      error_message += "ComplexGMRES\n\n";
      error_message += "is deliberately broken to avoid the accidental \n";
      error_message += "use of the inappropriate C++ default. If you \n";
      error_message += "really need one for this class, write it yourself...\n";
 
      // Throw an error
      throw OomphLibError(
        error_message, OOMPH_CURRENT_FUNCTION, OOMPH_EXCEPTION_LOCATION);
    } // End of solve
 
    /// Linear-algebra-type solver: Takes pointer to a matrix
    /// and rhs vector and returns the solution of the linear system
    /// Call the broken base-class version. If you want this, please
    /// implement it
    void solve(DoubleMatrixBase* const& matrix_pt,
               const Vector<double>& rhs,
               Vector<double>& result)
    {
      LinearSolver::solve(matrix_pt, rhs, result);
    } // End of solve
 
    /// Number of iterations taken
    unsigned iterations() const
    {
      // Return the number of iterations used
      return Iterations;
    } // End of iterations
 
    /// Setup: Pass pointer to the matrix and store in cast form
    void complex_smoother_setup(Vector<CRDoubleMatrix*> helmholtz_matrix_pt)
    {
      // Assume the matrices have been passed in from the outside so we must
      // not delete it. This is needed to avoid pre- and post-smoothers
      // deleting the same matrix in the MG solver. If this was originally
      // set to TRUE then this will be sorted out in the other functions
      // from which this was called
      Matrix_can_be_deleted = false;
 
#ifdef PARANOID
      // PARANOID check - Make sure the input has the right number of matrices
      if (helmholtz_matrix_pt.size() != 2)
      {
        std::ostringstream error_message_stream;
        error_message_stream << "Can only deal with two matrices. You have "
                             << helmholtz_matrix_pt.size() << " matrices."
                             << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      // Resize the storage for the system matrices
      Matrices_storage_pt.resize(2, 0);
 
      // Assign the real matrix pointer
      Matrices_storage_pt[0] = helmholtz_matrix_pt[0];
 
      // Assign the imaginary matrix pointers
      Matrices_storage_pt[1] = helmholtz_matrix_pt[1];
 
#ifdef PARANOID
      // PARANOID check - Make sure that the constituent matrices have the same
      // number of rows
      if (Matrices_storage_pt[0]->nrow() != Matrices_storage_pt[1]->nrow())
      {
        std::ostringstream error_message_stream;
        error_message_stream << "Incompatible real and imag. matrix sizes.";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check - Make sure that the constituent matrices have the same
      // number of columns
      if (Matrices_storage_pt[0]->ncol() != Matrices_storage_pt[1]->ncol())
      {
        std::ostringstream error_message_stream;
        error_message_stream << "Incompatible real and imag. matrix sizes.";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
#endif
    } // End of complex_smoother_setup
 
    /// The smoother_solve function performs fixed number of iterations
    /// on the system A*result=rhs. The number of (smoothing) iterations is
    /// the same as the max. number of iterations in the underlying
    /// IterativeLinearSolver class.
    void complex_smoother_solve(const Vector<DoubleVector>& rhs,
                                Vector<DoubleVector>& solution)
    {
      // If you use a smoother but you don't want to calculate the residual
      Use_as_smoother = true;
 
      // Use the helper function where the work is actually done
      complex_solve_helper(rhs, solution);
    } // End of complex_smoother_solve
 
  private:
    /// Cleanup data that's stored for resolve (if any has been stored)
    void clean_up_memory()
    {
      // If the real matrix pointer isn't null AND we're allowed to delete
      // the matrix which is only when we create the matrix ourselves
      if ((Matrices_storage_pt[0] != 0) && (Matrix_can_be_deleted))
      {
        // Delete the matrix
        delete Matrices_storage_pt[0];
 
        // Assign the associated pointer the value NULL
        Matrices_storage_pt[0] = 0;
      }
 
      // If the real matrix pointer isn't null AND we're allowed to delete
      // the matrix which is only when we create the matrix ourselves
      if ((Matrices_storage_pt[1] != 0) && (Matrix_can_be_deleted))
      {
        // Delete the matrix
        delete Matrices_storage_pt[1];
 
        // Assign the associated pointer the value NULL
        Matrices_storage_pt[1] = 0;
      }
    } // End of clean_up_memory
 
    /// This is where the actual work is done
    void complex_solve_helper(const Vector<DoubleVector>& rhs,
                              Vector<DoubleVector>& solution);
 
    /// Helper function to update the result vector
    void update(const unsigned& k,
                const Vector<Vector<std::complex<double>>>& hessenberg,
                const Vector<std::complex<double>>& s,
                const Vector<Vector<DoubleVector>>& v,
                Vector<DoubleVector>& x)
    {
      // Make a local copy of s
      Vector<std::complex<double>> y(s);
 
      //-----------------------------------------------------------------
      // The Hessenberg matrix should be an upper triangular matrix at
      // this point (although from its storage it would appear to be a
      // lower triangular matrix since the indexing has been reversed)
      // so finding the minimiser of J(y)=min||s-R_m*y|| where R_m is
      // the matrix R in the QR factorisation of the Hessenberg matrix.
      // Therefore, to obtain y we simply need to use a backwards
      // substitution. Note: The implementation here may appear to be
      // somewhat confusing as the indexing in the Hessenberg matrix is
      // reversed. This implementation of a backwards substitution does
      // not run along the columns of the triangular matrix but rather
      // up the rows.
      //-----------------------------------------------------------------
      // The outer loop is a loop over the columns of the Hessenberg matrix
      // since the indexing is reversed
      for (int i = int(k); i >= 0; i--)
      {
        // Divide the i-th entry of y by the i-th diagonal entry of H
        y[i] /= hessenberg[i][i];
 
        // The inner loop is a loop over the rows of the Hessenberg matrix
        for (int j = i - 1; j >= 0; j--)
        {
          // Update the j-th entry of y
          y[j] -= hessenberg[i][j] * y[i];
        }
      } // for (int i=int(k);i>=0;i--)
 
      // Calculate the number of entries in x (simply use the real part as
      // both the real and imaginary part should have the same length)
      unsigned n_row = x[0].nrow();
 
      // We assume here that the vector x (which is passed in) is actually x_0
      // so we simply need to update its entries to calculate the solution, x
      // which is given by x=x_0+Vy.
      for (unsigned j = 0; j <= k; j++)
      {
        // For fast access (real part)
        const double* vj_r_pt = v[j][0].values_pt();
 
        // For fast access (imaginary part)
        const double* vj_c_pt = v[j][1].values_pt();
 
        // Loop over the entries in x and update them
        for (unsigned i = 0; i < n_row; i++)
        {
          // Update the real part of the i-th entry in x
          x[0][i] += (vj_r_pt[i] * y[j].real()) - (vj_c_pt[i] * y[j].imag());
 
          // Update the imaginary part of the i-th entry in x
          x[1][i] += (vj_c_pt[i] * y[j].real()) + (vj_r_pt[i] * y[j].imag());
        }
      } // for (unsigned j=0;j<=k;j++)
    } // End of update
 
    /// Helper function: Generate a plane rotation. This is done by
    /// finding the value of \f$ \cos(\theta) \f$ (i.e. cs) and the value of
    /// \f$ \sin(\theta) \f$ (i.e. sn) such that:
    /// \f[ \begin{bmatrix} \overline{\cos\theta} & \overline{\sin\theta} \cr -\sin\theta & \cos\theta \end{bmatrix} \begin{bmatrix} dx \\ dy \end{bmatrix} = \begin{bmatrix} r \\ 0 \end{bmatrix}, \f]
    /// where \f$ r=\sqrt{pow(|dx|,2)+pow(|dy|,2)} \f$. The values of a and b
    /// are given by:
    /// The values of dx and dy are given by:
    /// \f[ \cos\theta=\dfrac{dx}{\sqrt{|dx|^2+|dy|^2}}, \f]
    /// and
    /// \f[ \sin\theta=\dfrac{dy}{\sqrt{|dx|^2+|dy|^2}}. \f]
    /// Taken from: Saad Y."Iterative methods for sparse linear systems", p.193.
    /// We also check to see that sn is always a real (nonnegative) number. See
    /// pp.193-194 for an explanation.
    void generate_plane_rotation(std::complex<double>& dx,
                                 std::complex<double>& dy,
                                 std::complex<double>& cs,
                                 std::complex<double>& sn)
    {
      // If dy=0 then we do not need to apply a rotation
      if (dy == 0.0)
      {
        // Using theta=0 gives cos(theta)=1
        cs = 1.0;
 
        // Using theta=0 gives sin(theta)=0
        sn = 0.0;
      }
      // If dx or dy is large using the original form of calculting cs and sn is
      // naive since this may overflow or underflow so instead we calculate
      // r=sqrt(pow(|dx|,2)+pow(|dy|,2)) using r=|dy|sqrt(1+pow(|dx|/|dy|,2)) if
      // |dy|>|dx| [see <A
      // HREF=https://en.wikipedia.org/wiki/Hypot">Hypot</A>.].
      else if (std::abs(dy) > std::abs(dx))
      {
        // Since |dy|>|dx| calculate the ratio |dx|/|dy|
        std::complex<double> temp = dx / dy;
 
        // Calculate the value of sin(theta) using:
        //          sin(theta)=dy/sqrt(pow(|dx|,2)+pow(|dy|,2))
        //                    =(dy/|dy|)/sqrt(1+pow(|dx|/|dy|,2)).
        sn = (dy / std::abs(dy)) / sqrt(1.0 + pow(std::abs(temp), 2.0));
 
        // Calculate the value of cos(theta) using:
        //            cos(theta)=dx/sqrt(pow(|dy|,2)+pow(|dx|,2))
        //                      =(dx/|dy|)/sqrt(1+pow(|dx|/|dy|,2))
        //                      =(dx/dy)*sin(theta).
        cs = temp * sn;
      }
      // Otherwise, we have |dx|>=|dy| so to, again, avoid overflow or underflow
      // calculate the values of cs and sn using the method above
      else
      {
        // Since |dx|>=|dy| calculate the ratio dy/dx
        std::complex<double> temp = dy / dx;
 
        // Calculate the value of cos(theta) using:
        //          cos(theta)=dx/sqrt(pow(|dx|,2)+pow(|dy|,2))
        //                    =(dx/|dx|)/sqrt(1+pow(|dy|/|dx|,2)).
        cs = (dx / std::abs(dx)) / sqrt(1.0 + pow(std::abs(temp), 2.0));
 
        // Calculate the value of sin(theta) using:
        //            sin(theta)=dy/sqrt(pow(|dx|,2)+pow(|dy|,2))
        //                      =(dy/|dx|)/sqrt(1+pow(|dy|/|dx|,2))
        //                      =(dy/dx)*cos(theta).
        sn = temp * cs;
      }
 
      // Set the tolerance for sin(theta)
      double tolerance = 1.0e-15;
 
      // Make sure sn is real and nonnegative (it should be!)
      if ((std::fabs(sn.imag()) > tolerance) || (sn.real() < 0))
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create an error message
        error_message_stream << "The value of sin(theta) is not real "
                             << "and/or nonnegative. Value is: " << sn
                             << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
    } // End of generate_plane_rotation
 
    /// Helper function: Apply plane rotation. This is done using the
    /// update:
    /// \f[ \begin{bmatrix} dx \\ dy \end{bmatrix} \leftarrow \begin{bmatrix} \overline{\cos\theta} & \overline{\sin\theta} \\ -\sin\theta & \cos\theta \end{bmatrix} \begin{bmatrix} dx \\ dy \end{bmatrix}. \f]
    /// Taken from: Saad Y."Iterative methods for sparse linear systems", p.193.
    void apply_plane_rotation(std::complex<double>& dx,
                              std::complex<double>& dy,
                              std::complex<double>& cs,
                              std::complex<double>& sn)
    {
      // Calculate the value of dx but don't update it yet
      std::complex<double> temp = std::conj(cs) * dx + std::conj(sn) * dy;
 
      // Set the value of dy
      dy = -sn * dx + cs * dy;
 
      // Set the value of dx using the correct values of dx and dy
      dx = temp;
    } // End of apply_plane_rotation
 
    /// Number of iterations taken
    unsigned Iterations;
 
    /// Vector of pointers to the real and imaginary part of the system matrix
    Vector<CRDoubleMatrix*> Matrices_storage_pt;
 
    /// Boolean flag to indicate if the solve is done in re-solve mode,
    /// bypassing setup of matrix and preconditioner
    bool Resolving;
 
    /// Boolean flag to indicate if the real and imaginary system
    /// matrices can be deleted
    bool Matrix_can_be_deleted;
  };
 
  //======================================================================
  /// This is where the actual work is done
  //======================================================================
  template<typename MATRIX>
  void ComplexGMRES<MATRIX>::complex_solve_helper(
    const Vector<DoubleVector>& rhs, Vector<DoubleVector>& solution)
  {
    // Set the number of dof types (real and imaginary for this solver)
    unsigned n_dof_types = 2;
 
    // Get the number of dofs (note, the total number of dofs in the problem
    // is 2*n_row but if the constituent vectors and matrices were stored in
    // complex objects there would only be (n_row) rows so we use that)
    unsigned n_row = Matrices_storage_pt[0]->nrow();
 
    // Make sure Max_iter isn't greater than n_dof. The user cannot use this
    // many iterations when using Krylov subspace methods
    if (Max_iter > n_row)
    {
      // Create an output string stream
      std::ostringstream error_message_stream;
 
      // Create the error message
      error_message_stream << "The maximum number of iterations cannot exceed "
                           << "the number of rows in the problem."
                           << "\nMaximum number of iterations: " << Max_iter
                           << "\nNumber of rows: " << n_row << std::endl;
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
 
    // Loop over the real and imaginary parts
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
#ifdef PARANOID
      // PARANOID check that if the matrix is distributable then it should not
      // be then it should not be distributed
      if (dynamic_cast<DistributableLinearAlgebraObject*>(
            Matrices_storage_pt[dof_type]) != 0)
      {
        if (dynamic_cast<DistributableLinearAlgebraObject*>(
              Matrices_storage_pt[dof_type])
              ->distributed())
        {
          std::ostringstream error_message_stream;
          error_message_stream << "The matrix must not be distributed.";
          throw OomphLibError(error_message_stream.str(),
                              OOMPH_CURRENT_FUNCTION,
                              OOMPH_EXCEPTION_LOCATION);
        }
      }
      // PARANOID check that this rhs distribution is setup
      if (!rhs[dof_type].built())
      {
        std::ostringstream error_message_stream;
        error_message_stream << "The rhs vector distribution must be setup.";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check that the rhs has the right number of global rows
      if (rhs[dof_type].nrow() != n_row)
      {
        std::ostringstream error_message_stream;
        error_message_stream << "RHS does not have the same dimension as the"
                             << " linear system";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check that the rhs is not distributed
      if (rhs[dof_type].distribution_pt()->distributed())
      {
        std::ostringstream error_message_stream;
        error_message_stream << "The rhs vector must not be distributed.";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check that if the result is setup it matches the distribution
      // of the rhs
      if (solution[dof_type].built())
      {
        if (!(*rhs[dof_type].distribution_pt() ==
              *solution[dof_type].distribution_pt()))
        {
          std::ostringstream error_message_stream;
          error_message_stream << "If the result distribution is setup then it "
                               << "must be the same as the rhs distribution";
          throw OomphLibError(error_message_stream.str(),
                              OOMPH_CURRENT_FUNCTION,
                              OOMPH_EXCEPTION_LOCATION);
        }
      } // if (solution[dof_type].built())
#endif
      // Set up the solution distribution if it's not already distributed
      if (!solution[dof_type].built())
      {
        // Build the distribution
        solution[dof_type].build(this->distribution_pt(), 0.0);
      }
      // Otherwise initialise all entries to zero
      else
      {
        // Initialise the entries in the k-th vector in solution to zero
        solution[dof_type].initialise(0.0);
      }
    } // for (unsigned dof_type=0;dof_type<n_dof_types;dof_type++)
 
    // Start the solver timer
    double t_start = TimingHelpers::timer();
 
    // Storage for the relative residual
    double resid;
 
    // Initialise vectors (since these are not distributed vectors we template
    // one vector by the type std::complex<double> instead of using two vectors,
    // each templated by the type double
 
    // Vector, s, used in the minimisation problem: J(y)=min||s-R_m*y||
    // [see Saad Y."Iterative methods for sparse linear systems", p.176.]
    Vector<std::complex<double>> s(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Vector to store the value of cos(theta) when using the Givens rotation
    Vector<std::complex<double>> cs(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Vector to store the value of sin(theta) when using the Givens rotation
    Vector<std::complex<double>> sn(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Create a vector of DoubleVectors (this is a distributed vector so we have
    // to create two separate DoubleVector objects to cope with the arithmetic)
    Vector<DoubleVector> w(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      w[dof_type].build(this->distribution_pt(), 0.0);
    }
 
    // Create a vector of DoubleVectors to store the RHS of b-Jx=Mr. We assume
    // both x=0 and that a preconditioner is not applied by which we deduce b=r
    Vector<DoubleVector> r(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      r[dof_type].build(this->distribution_pt(), 0.0);
    }
 
    // Store the value of b (the RHS vector) in r
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      r[dof_type] = rhs[dof_type];
    }
 
    // Calculate the norm of the real part of r
    double norm_r = r[0].norm();
 
    // Calculate the norm of the imaginary part of r
    double norm_c = r[1].norm();
 
    // Compute norm(r)
    double normb = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
    // Set the value of beta (the initial residual)
    double beta = normb;
 
    // Compute the initial relative residual. If the entries of the RHS vector
    // are all zero then set normb equal to one. This is because we divide the
    // value of the norm at later stages by normb and dividing by zero is not
    // definied
    if (normb == 0.0)
    {
      // Set the value of normb
      normb = 1.0;
    }
 
    // Calculate the ratio between the initial norm and the current norm.
    // Since we haven't completed an iteration yet this will simply be one
    // unless normb was zero, in which case resid will have value zero
    resid = beta / normb;
 
    // If required, will document convergence history to screen or file (if
    // stream open)
    if (Doc_convergence_history)
    {
      // If an output file which is open isn't provided then output to screen
      if (!Output_file_stream.is_open())
      {
        // Output the residual value to the screen
        oomph_info << 0 << " " << resid << std::endl;
      }
      // Otherwise, output to file
      else
      {
        // Output the residual value to file
        Output_file_stream << 0 << " " << resid << std::endl;
      }
    } // if (Doc_convergence_history)
 
    // If the GMRES algorithm converges immediately
    if (resid <= Tolerance)
    {
      // If time documentation is enabled
      if (Doc_time)
      {
        // Notify the user
        oomph_info << "GMRES converged immediately. Normalised residual norm: "
                   << resid << std::endl;
      }
 
      // Finish running the solver
      return;
    } // if (resid<=Tolerance)
 
    // Initialise a vector of orthogonal basis vectors
    Vector<Vector<DoubleVector>> v;
 
    // Resize the number of vectors needed
    v.resize(n_row + 1);
 
    // Resize each Vector of DoubleVectors to store the real and imaginary
    // part of a given vector
    for (unsigned dof_type = 0; dof_type < n_row + 1; dof_type++)
    {
      // Create two DoubleVector objects in each Vector
      v[dof_type].resize(n_dof_types);
    }
 
    // Initialise the upper hessenberg matrix. Since we are not using
    // distributed vectors here, the algebra is best done using entries
    // of the type std::complex<double>. NOTE: For implementation purposes
    // the upper Hessenberg matrix indices are swapped so the matrix is
    // effectively transposed
    Vector<Vector<std::complex<double>>> hessenberg(n_row + 1);
 
    // Build the zeroth basis vector
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the k-th part of the zeroth vector. Here k=0 and k=1 correspond
      // to the real and imaginary part of the zeroth vector, respectively
      v[0][dof_type].build(this->distribution_pt(), 0.0);
    }
 
    // Loop over the real and imaginary parts of v
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // For fast access
      double* v0_pt = v[0][dof_type].values_pt();
 
      // For fast access
      const double* r_pt = r[dof_type].values_pt();
 
      // Set the zeroth basis vector v[0] to r/beta
      for (unsigned i = 0; i < n_row; i++)
      {
        // Assign the i-th entry of the zeroth basis vector
        v0_pt[i] = r_pt[i] / beta;
      }
    } // for (unsigned k=0;k<n_dof_types;k++)
 
    // Set the first entry in the minimisation problem RHS vector (is meant
    // to the vector beta*e_1 initially, where e_1 is the unit vector with
    // one in its first entry)
    s[0] = beta;
 
    // Compute the next step of the iterative scheme
    for (unsigned j = 0; j < Max_iter; j++)
    {
      // Resize the next column of the upper hessenberg matrix
      hessenberg[j].resize(j + 2, std::complex<double>(0.0, 0.0));
 
      // Calculate w=J*v_j. Note, we cannot use inbuilt complex matrix algebra
      // here as we're using distributed vectors
      complex_matrix_multiplication(Matrices_storage_pt, v[j], w);
 
      // For fast access
      double* w_r_pt = w[0].values_pt();
 
      // For fast access
      double* w_c_pt = w[1].values_pt();
 
      // Loop over all of the entries on and above the principal subdiagonal of
      // the Hessenberg matrix in the j-th column (remembering that
      // the indices of the upper Hessenberg matrix are swapped for the purpose
      // of implementation)
      for (unsigned i = 0; i < j + 1; i++)
      {
        // For fast access
        const double* vi_r_pt = v[i][0].values_pt();
 
        // For fast access
        const double* vi_c_pt = v[i][1].values_pt();
 
        // Loop over the entries of v and w
        for (unsigned k = 0; k < n_row; k++)
        {
          // Store the appropriate entry in v as a complex value
          std::complex<double> complex_v(vi_r_pt[k], vi_c_pt[k]);
 
          // Store the appropriate entry in w as a complex value
          std::complex<double> complex_w(w_r_pt[k], w_c_pt[k]);
 
          // Update the value of H(i,j) noting we're computing a complex
          // inner product here
          hessenberg[j][i] += std::conj(complex_v) * complex_w;
        }
 
        // Orthonormalise w against all previous orthogonal vectors, v_i by
        // looping over its entries and updating them
        for (unsigned k = 0; k < n_row; k++)
        {
          // Update the real part of the k-th entry of w
          w_r_pt[k] -= (hessenberg[j][i].real() * vi_r_pt[k] -
                        hessenberg[j][i].imag() * vi_c_pt[k]);
 
          // Update the imaginary part of the k-th entry of w
          w_c_pt[k] -= (hessenberg[j][i].real() * vi_c_pt[k] +
                        hessenberg[j][i].imag() * vi_r_pt[k]);
        }
      } // for (unsigned i=0;i<j+1;i++)
 
      // Calculate the norm of the real part of w
      norm_r = w[0].norm();
 
      // Calculate the norm of the imaginary part of w
      norm_c = w[1].norm();
 
      // Calculate the norm of the vector w using norm_r and norm_c and assign
      // its value to the appropriate entry in the Hessenberg matrix
      hessenberg[j][j + 1] = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
      // Build the real part of the next orthogonal vector
      v[j + 1][0].build(this->distribution_pt(), 0.0);
 
      // Build the imaginary part of the next orthogonal vector
      v[j + 1][1].build(this->distribution_pt(), 0.0);
 
      // Check if the value of hessenberg[j][j+1] is zero. If it
      // isn't then we update the next entries in v
      if (hessenberg[j][j + 1] != 0.0)
      {
        // For fast access
        double* v_r_pt = v[j + 1][0].values_pt();
 
        // For fast access
        double* v_c_pt = v[j + 1][1].values_pt();
 
        // For fast access
        const double* w_r_pt = w[0].values_pt();
 
        // For fast access
        const double* w_c_pt = w[1].values_pt();
 
        // Notice, the value of H(j,j+1), as calculated above, is clearly a real
        // number. As such, calculating the division
        //                      v_{j+1}=w_{j}/h_{j+1,j},
        // here is simple, i.e. we don't need to worry about cross terms in the
        // algebra. To avoid computing h_{j+1,j} several times we precompute it
        double h_subdiag_val = hessenberg[j][j + 1].real();
 
        // Loop over the entries of the new orthogonal vector and set its values
        for (unsigned k = 0; k < n_row; k++)
        {
          // The i-th entry of the real component is given by
          v_r_pt[k] = w_r_pt[k] / h_subdiag_val;
 
          // Similarly, the i-th entry of the imaginary component is given by
          v_c_pt[k] = w_c_pt[k] / h_subdiag_val;
        }
      }
      // Otherwise, we have to jump to the next part of the algorithm; if
      // the value of hessenberg[j][j+1] is zero then the norm of the latest
      // orthogonal vector is zero. This is only possible if the entries
      // in w are all zero. As a result, the Krylov space of A and r_0 has
      // been spanned by the previously calculated orthogonal vectors
      else
      {
        // Book says "Set m=j and jump to step 11" (p.172)...
        // Do something here!
        oomph_info << "Subdiagonal Hessenberg entry is zero."
                   << "Do something here..." << std::endl;
      } // if (hessenberg[j][j+1]!=0.0)
 
      // Loop over the entries in the Hessenberg matrix and calculate the
      // entries of the Givens rotation matrices
      for (unsigned k = 0; k < j; k++)
      {
        // Apply the plane rotation to all of the previous entries in the
        // (j)-th column (remembering the indexing is reversed)
        apply_plane_rotation(
          hessenberg[j][k], hessenberg[j][k + 1], cs[k], sn[k]);
      }
 
      // Now calculate the entries of the latest Givens rotation matrix
      generate_plane_rotation(
        hessenberg[j][j], hessenberg[j][j + 1], cs[j], sn[j]);
 
      // Apply the plane rotation using the newly calculated entries
      apply_plane_rotation(
        hessenberg[j][j], hessenberg[j][j + 1], cs[j], sn[j]);
 
      // Apply a plane rotation to the corresponding entry in the vector
      // s used in the minimisation problem, J(y)=min||s-R_m*y||
      apply_plane_rotation(s[j], s[j + 1], cs[j], sn[j]);
 
      // Compute current residual using equation (6.42) in Saad Y, "Iterative
      // methods for sparse linear systems", p.177.]. Note, since s has complex
      // entries we have to use std::abs instead of std::fabs
      beta = std::abs(s[j + 1]);
 
      // Compute the relative residual
      resid = beta / normb;
 
      // If required will document convergence history to screen or file (if
      // stream open)
      if (Doc_convergence_history)
      {
        // If an output file which is open isn't provided then output to screen
        if (!Output_file_stream.is_open())
        {
          // Output the residual value to the screen
          oomph_info << j + 1 << " " << resid << std::endl;
        }
        // Otherwise, output to file
        else
        {
          // Output the residual value to file
          Output_file_stream << j + 1 << " " << resid << std::endl;
        }
      } // if (Doc_convergence_history)
 
      // If the required tolerance has been met
      if (resid < Tolerance)
      {
        // Store the number of iterations taken
        Iterations = j + 1;
 
        // Update the result vector using the result, x=x_0+V_m*y (where V_m
        // is given by v here)
        update(j, hessenberg, s, v, solution);
 
        // If time documentation was enabled
        if (Doc_time)
        {
          // Output the current normalised residual norm
          oomph_info << "\nGMRES converged (1). Normalised residual norm: "
                     << resid << std::endl;
 
          // Output the number of iterations it took for convergence
          oomph_info << "Number of iterations to convergence: " << j + 1 << "\n"
                     << std::endl;
        }
 
        // Stop the timer
        double t_end = TimingHelpers::timer();
 
        // Calculate the time taken to calculate the solution
        Solution_time = t_end - t_start;
 
        // If time documentation was enabled
        if (Doc_time)
        {
          // Output the time taken to solve the problem using GMRES
          oomph_info << "Time for solve with GMRES [sec]: " << Solution_time
                     << std::endl;
        }
 
        // As we've met the tolerance for the solver and everything that should
        // be documented, has been, finish using the solver
        return;
      } // if (resid<Tolerance)
    } // for (unsigned j=0;j<Max_iter;j++)
 
    // Store the number of iterations taken
    Iterations = Max_iter;
 
    // Only update if we actually did something
    if (Max_iter > 0)
    {
      // Update the result vector using the result, x=x_0+V_m*y (where V_m
      // is given by v here)
      update(Max_iter - 1, hessenberg, s, v, solution);
    }
 
    // Stop the timer
    double t_end = TimingHelpers::timer();
 
    // Calculate the time taken to calculate the solution
    Solution_time = t_end - t_start;
 
    // If time documentation was enabled
    if (Doc_time)
    {
      // Output the time taken to solve the problem using GMRES
      oomph_info << "Time for solve with GMRES [sec]: " << Solution_time
                 << std::endl;
    }
 
    // Finish using the solver
    return;
  } // End of complex_solve_helper
 
 
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
 
 
  //======================================================================
  /// The GMRES method for the Helmholtz solver.
  //======================================================================
  template<typename MATRIX>
  class HelmholtzGMRESMG : public IterativeLinearSolver,
                           public BlockPreconditioner<MATRIX>
  {
  public:
    /// Constructor
    HelmholtzGMRESMG()
      : BlockPreconditioner<CRDoubleMatrix>(),
        Iterations(0),
        Resolving(false),
        Matrix_can_be_deleted(true)
    {
      Preconditioner_LHS = true;
    }
 
    /// Destructor (cleanup storage)
    virtual ~HelmholtzGMRESMG()
    {
      clean_up_memory();
    }
 
    /// Broken copy constructor
    HelmholtzGMRESMG(const HelmholtzGMRESMG&) = delete;
 
    /// Broken assignment operator
    void operator=(const HelmholtzGMRESMG&) = delete;
 
    /// Overload disable resolve so that it cleans up memory too
    void disable_resolve()
    {
      LinearSolver::disable_resolve();
      clean_up_memory();
    }
 
    /// Implementation of the pure virtual base class function. The
    /// function has been broken because this is meant to be used as a linear
    /// solver
    void preconditioner_solve(const DoubleVector& r, DoubleVector& z)
    {
      // Open an output stream
      std::ostringstream error_message_stream;
 
      // Create an error message
      error_message_stream << "Preconditioner_solve(...) is broken. "
                           << "HelmholtzGMRESMG is only meant to be used as "
                           << "a linear solver.\n";
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
 
    /// Implementation of the pure virtual base class function. This
    /// accompanies the preconditioner_solve function and so is also broken
    void setup()
    {
      // Open an output stream
      std::ostringstream error_message_stream;
 
      // Create an error message
      error_message_stream << "This function is broken. HelmholtzGMRESMG is "
                           << "only meant to be used as a linear solver.\n";
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
 
    /// Solver: Takes pointer to problem and returns the results vector
    /// which contains the solution of the linear system defined by
    /// the problem's fully assembled Jacobian and residual vector.
    void solve(Problem* const& problem_pt, DoubleVector& result)
    {
#ifdef OOMPH_HAS_MPI
      // Make sure that this is running in serial. Can't guarantee it'll
      // work when the problem is distributed over several processors
      if (MPI_Helpers::communicator_pt()->nproc() > 1)
      {
        // Throw a warning
        OomphLibWarning("Can't guarantee the MG solver will work in parallel!",
                        OOMPH_CURRENT_FUNCTION,
                        OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      // Find # of degrees of freedom (variables)
      unsigned n_dof = problem_pt->ndof();
 
      // Initialise timer
      double t_start = TimingHelpers::timer();
 
      // We're not re-solving
      Resolving = false;
 
      // Get rid of any previously stored data
      clean_up_memory();
 
      // Grab the communicator from the MGProblem object and assign it
      this->set_comm_pt(problem_pt->communicator_pt());
 
      // Setup the distribution
      LinearAlgebraDistribution dist(
        problem_pt->communicator_pt(), n_dof, false);
 
      // Build the internal distribution in this way because both the
      // IterativeLinearSolver and BlockPreconditioner class have base-
      // class subobjects of type oomph::DistributableLinearAlgebraObject
      IterativeLinearSolver::build_distribution(dist);
 
      // Get Jacobian matrix in format specified by template parameter
      // and nonlinear residual vector
      MATRIX* matrix_pt = new MATRIX;
      DoubleVector f;
      if (dynamic_cast<DistributableLinearAlgebraObject*>(matrix_pt) != 0)
      {
        if (dynamic_cast<CRDoubleMatrix*>(matrix_pt) != 0)
        {
          dynamic_cast<CRDoubleMatrix*>(matrix_pt)->build(
            IterativeLinearSolver::distribution_pt());
          f.build(IterativeLinearSolver::distribution_pt(), 0.0);
        }
      }
 
      // Get the Jacobian and residuals vector
      problem_pt->get_jacobian(f, *matrix_pt);
 
      // We've made the matrix, we can delete it...
      Matrix_can_be_deleted = true;
 
      // Replace the current matrix used in Preconditioner by the new matrix
      this->set_matrix_pt(matrix_pt);
 
      // The preconditioner works with one mesh; set it! Since we only use
      // the block preconditioner on the finest level, we use the mesh from
      // that level
      this->set_nmesh(1);
 
      // Elements in actual pml layer are trivially wrapped versions of
      // their bulk counterparts. Technically they are different elements
      // so we have to allow different element types
      bool allow_different_element_types_in_mesh = true;
      this->set_mesh(
        0, problem_pt->mesh_pt(), allow_different_element_types_in_mesh);
 
      // Set up the generic block look up scheme
      this->block_setup();
 
      // Extract the number of blocks.
      unsigned nblock_types = this->nblock_types();
 
#ifdef PARANOID
      // PARANOID check - there must only be two block types
      if (nblock_types != 2)
      {
        // Create the error message
        std::stringstream tmp;
        tmp << "There are supposed to be two block types.\nYours has "
            << nblock_types << std::endl;
 
        // Throw an error
        throw OomphLibError(
          tmp.str(), OOMPH_CURRENT_FUNCTION, OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      // Resize the storage for the system matrices
      Matrices_storage_pt.resize(2, 0);
 
      // Loop over the rows of the block matrix
      for (unsigned i = 0; i < nblock_types; i++)
      {
        // Fix the column index
        unsigned j = 0;
 
        // Create new CRDoubleMatrix objects
        Matrices_storage_pt[i] = new CRDoubleMatrix;
 
        // Extract the required blocks, i.e. the first column
        this->get_block(i, j, *Matrices_storage_pt[i]);
      }
 
      // Doc time for setup
      double t_end = TimingHelpers::timer();
      Jacobian_setup_time = t_end - t_start;
 
      if (Doc_time)
      {
        oomph_info << "\nTime for setup of block Jacobian [sec]: "
                   << Jacobian_setup_time << std::endl;
      }
 
      // Call linear algebra-style solver
      // If the result distribution is wrong, then redistribute
      // before the solve and return to original distribution
      // afterwards
      if ((!(*result.distribution_pt() ==
             *IterativeLinearSolver::distribution_pt())) &&
          (result.built()))
      {
        // Make a distribution object
        LinearAlgebraDistribution temp_global_dist(result.distribution_pt());
 
        // Build the result vector distribution
        result.build(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Solve the problem
        solve_helper(matrix_pt, f, result);
 
        // Redistribute the vector
        result.redistribute(&temp_global_dist);
      }
      // Otherwise just solve
      else
      {
        // Solve
        solve_helper(matrix_pt, f, result);
      }
 
      // Kill matrix unless it's still required for resolve
      if (!Enable_resolve)
      {
        // Clean up anything left in memory
        clean_up_memory();
      }
    } // End of solve
 
    /// Linear-algebra-type solver: Takes pointer to a matrix and rhs
    /// vector and returns the solution of the linear system.
    void solve(DoubleMatrixBase* const& matrix_pt,
               const DoubleVector& rhs,
               DoubleVector& solution)
    {
      // Open an output stream
      std::ostringstream error_message_stream;
 
      // Create an error message
      error_message_stream
        << "This function is broken. The block preconditioner "
        << "needs access to the underlying mesh.\n";
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
 
 
    /// Linear-algebra-type solver: Takes pointer to a matrix
    /// and rhs vector and returns the solution of the linear system
    /// Call the broken base-class version. If you want this, please
    /// implement it
    void solve(DoubleMatrixBase* const& matrix_pt,
               const Vector<double>& rhs,
               Vector<double>& result)
    {
      LinearSolver::solve(matrix_pt, rhs, result);
    }
 
    /// Re-solve the system defined by the last assembled Jacobian
    /// and the rhs vector specified here. Solution is returned in the
    /// vector result.
    void resolve(const DoubleVector& rhs, DoubleVector& result)
    {
      // We are re-solving
      Resolving = true;
 
#ifdef PARANOID
      if ((Matrices_storage_pt[0] == 0) || (Matrices_storage_pt[1] == 0))
      {
        throw OomphLibError("No matrix was stored -- cannot re-solve",
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      // Set up a dummy matrix. As we're resolving this won't be used in
      // solve_helper but we need to pass a matrix in to fill the input.
      // The matrices used in the calculations have already been stored
      CRDoubleMatrix* matrix_pt = new CRDoubleMatrix;
 
      // Call the helper function
      solve_helper(matrix_pt, rhs, result);
 
      // Delete the matrix
      delete matrix_pt;
 
      // Make it a null pointer
      matrix_pt = 0;
 
      // Reset re-solving flag
      Resolving = false;
    }
 
    /// Number of iterations taken
    unsigned iterations() const
    {
      return Iterations;
    }
 
    /// Set left preconditioning (the default)
    void set_preconditioner_LHS()
    {
      Preconditioner_LHS = true;
    }
 
    /// Enable right preconditioning
    void set_preconditioner_RHS()
    {
      Preconditioner_LHS = false;
    }
 
  protected:
    /// General interface to solve function
    void solve_helper(DoubleMatrixBase* const& matrix_pt,
                      const DoubleVector& rhs,
                      DoubleVector& solution);
 
    /// Cleanup data that's stored for resolve (if any has been stored)
    void clean_up_memory()
    {
      // If the matrix storage has been resized
      if (Matrices_storage_pt.size() > 0)
      {
        // If the real matrix pointer isn't null AND we're allowed to delete
        // the matrix which is only when we create the matrix ourselves
        if ((Matrices_storage_pt[0] != 0) && (Matrix_can_be_deleted))
        {
          // Delete the matrix
          delete Matrices_storage_pt[0];
 
          // Assign the associated pointer the value NULL
          Matrices_storage_pt[0] = 0;
        }
 
        // If the real matrix pointer isn't null AND we're allowed to delete
        // the matrix which is only when we create the matrix ourselves
        if ((Matrices_storage_pt[1] != 0) && (Matrix_can_be_deleted))
        {
          // Delete the matrix
          delete Matrices_storage_pt[1];
 
          // Assign the associated pointer the value NULL
          Matrices_storage_pt[1] = 0;
        }
      }
    } // End of clean_up_memory
 
    /// Helper function to calculate a complex matrix-vector product.
    /// Assumes the matrix has been provided as a Vector of length two; the
    /// first entry containing the real part of the system matrix and the
    /// second entry containing the imaginary part
    void complex_matrix_multiplication(
      Vector<CRDoubleMatrix*> const matrices_pt,
      const Vector<DoubleVector>& x,
      Vector<DoubleVector>& soln)
    {
#ifdef PARANOID
      // PARANOID check - Make sure the input matrix has the right size
      if (matrices_pt.size() != 2)
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create the error message
        error_message_stream << "Can only deal with two matrices. You have "
                             << matrices_pt.size() << " matrices." << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check - Make sure the vector x has the right size
      if (x.size() != 2)
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create the error message
        error_message_stream
          << "Can only deal with two input vectors. You have " << x.size()
          << " vectors." << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
      // PARANOID check - Make sure the vector soln has the right size
      if (soln.size() != 2)
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create the error message
        error_message_stream
          << "Can only deal with two output vectors. You have " << soln.size()
          << " output vectors." << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      // NOTE: We assume all vectors have been distributed at this point but
      // code can be written at a later time to build the vectors if they're
      // not already built.
 
      //-----------------------------------------------------------------------
      // Suppose we have a complex matrix, A, and two complex vectors, x and
      // soln. We wish to compute the product A*x=soln (note, * does not mean
      // we are using complex conjugates here, it is simply used to indicate
      // a multiplication). To do this we must make use of the fact that we
      // possess the real and imaginary separately. As a result, it is computed
      // using:
      //           soln = A*x,
      //                = (A_r + i*A_c)*(x_r + i*x_c),
      //                = [A_r*x_r - A_c*x_c] + i*[A_r*x_c + A_c*x_r],
      // ==> real(soln) = A_r*x_r - A_c*x_c,
      //   & imag(soln) = A_r*x_c + A_c*x_r,
      // where the subscripts _r and _c are used to identify the real and
      // imaginary part, respectively.
      //-----------------------------------------------------------------------
 
      // Store the value of A_r*x_r in the real part of soln
      matrices_pt[0]->multiply(x[0], soln[0]);
 
      // Store the value of A_r*x_c in the imaginary part of soln
      matrices_pt[0]->multiply(x[1], soln[1]);
 
      // Create a temporary vector
      DoubleVector temp(Matrices_storage_pt[0]->distribution_pt(), 0.0);
 
      // Calculate the value of A_c*x_c
      matrices_pt[1]->multiply(x[1], temp);
 
      // Subtract the value of temp from soln[0] to get the real part of soln
      soln[0] -= temp;
 
      // Calculate the value of A_c*x_r
      matrices_pt[1]->multiply(x[0], temp);
 
      // Add the value of temp to soln[1] to get the imaginary part of soln
      soln[1] += temp;
    } // End of complex_matrix_multiplication
 
    /// Helper function to update the result vector
    void update(const unsigned& k,
                const Vector<Vector<std::complex<double>>>& hessenberg,
                const Vector<std::complex<double>>& s,
                const Vector<Vector<DoubleVector>>& v,
                Vector<DoubleVector>& x)
    {
      // Make a local copy of s
      Vector<std::complex<double>> y(s);
 
      //-----------------------------------------------------------------
      // The Hessenberg matrix should be an upper triangular matrix at
      // this point (although from its storage it would appear to be a
      // lower triangular matrix since the indexing has been reversed)
      // so finding the minimiser of J(y)=min||s-R_m*y|| where R_m is
      // the matrix R in the QR factorisation of the Hessenberg matrix.
      // Therefore, to obtain y we simply need to use a backwards
      // substitution. Note: The implementation here may appear to be
      // somewhat confusing as the indexing in the Hessenberg matrix is
      // reversed. This implementation of a backwards substitution does
      // not run along the columns of the triangular matrix but rather
      // up the rows.
      //-----------------------------------------------------------------
 
      // The outer loop is a loop over the columns of the Hessenberg matrix
      // since the indexing is reversed
      for (int i = int(k); i >= 0; i--)
      {
        // Divide the i-th entry of y by the i-th diagonal entry of H
        y[i] /= hessenberg[i][i];
 
        // The inner loop is a loop over the rows of the Hessenberg matrix
        for (int j = i - 1; j >= 0; j--)
        {
          // Update the j-th entry of y
          y[j] -= hessenberg[i][j] * y[i];
        }
      } // for (int i=int(k);i>=0;i--)
 
      // Calculate the number of entries in x (simply use the real part as
      // both the real and imaginary part should have the same length)
      unsigned n_row = x[0].nrow();
 
      // Build a temporary vector with entries initialised to 0.0
      Vector<DoubleVector> block_z(2);
 
      // Build a temporary vector with entries initialised to 0.0
      Vector<DoubleVector> block_temp(2);
 
      // Build the distributions
      for (unsigned dof_type = 0; dof_type < 2; dof_type++)
      {
        // Build the (dof_type)-th vector
        block_z[dof_type].build(x[0].distribution_pt(), 0.0);
 
        // Build the (dof_type)-th vector
        block_temp[dof_type].build(x[0].distribution_pt(), 0.0);
      }
 
      // Get access to the underlying values
      double* block_temp_r_pt = block_temp[0].values_pt();
 
      // Get access to the underlying values
      double* block_temp_c_pt = block_temp[1].values_pt();
 
      // Calculate x=Vy
      for (unsigned j = 0; j <= k; j++)
      {
        // Get access to j-th column of Z_m
        const double* vj_r_pt = v[j][0].values_pt();
 
        // Get access to j-th column of Z_m
        const double* vj_c_pt = v[j][1].values_pt();
 
        // Loop over the entries in x and update them
        for (unsigned i = 0; i < n_row; i++)
        {
          // Update the real part of the i-th entry in x
          block_temp_r_pt[i] +=
            (vj_r_pt[i] * y[j].real()) - (vj_c_pt[i] * y[j].imag());
 
          // Update the imaginary part of the i-th entry in x
          block_temp_c_pt[i] +=
            (vj_c_pt[i] * y[j].real()) + (vj_r_pt[i] * y[j].imag());
        }
      } // for (unsigned j=0;j<=k;j++)
 
      // If we're using LHS preconditioning
      if (Preconditioner_LHS)
      {
        // Since we're using LHS preconditioning the preconditioner is applied
        // to the matrix and RHS vector so we simply update the value of x
        for (unsigned dof_type = 0; dof_type < 2; dof_type++)
        {
          // Update
          x[dof_type] += block_temp[dof_type];
        }
      }
      // If we're using RHS preconditioning
      else
      {
        // Create a temporary vector
        DoubleVector temp(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Copy block vectors block_temp back to temp
        this->return_block_vectors(block_temp, temp);
 
        // Create a temporary vector
        DoubleVector z(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Copy block vectors block_z back to z
        this->return_block_vectors(block_z, z);
 
        // Since we're using RHS preconditioning the preconditioner is applied
        // to the solution vector
        preconditioner_pt()->preconditioner_solve(temp, z);
 
        // Split up the solution vector into DoubleVectors, whose entries are
        // arranged to match the matrix blocks and assign it
        this->get_block_vectors(z, block_z);
 
        // Use the update: x_m=x_0+inv(M)Vy [see Saad Y,"Iterative methods for
        // sparse linear systems", p.284]
        for (unsigned dof_type = 0; dof_type < 2; dof_type++)
        {
          // Update
          x[dof_type] += block_z[dof_type];
        }
      } // if(Preconditioner_LHS) else
    } // End of update
 
    /// Helper function: Generate a plane rotation. This is done by
    /// finding the value of \f$ \cos(\theta) \f$ (i.e. cs) and the value of
    /// \f$ \sin(\theta) \f$ (i.e. sn) such that:
    /// \f[ \begin{bmatrix} \overline{\cos\theta} & \overline{\sin\theta} \\ -\sin\theta & \cos\theta \end{bmatrix} \begin{bmatrix} dx \\ dy \end{bmatrix} = \begin{bmatrix} r \\ 0 \end{bmatrix}, \f]
    /// where \f$ r=\sqrt{pow(|dx|,2)+pow(|dy|,2)} \f$. The values of a and b
    /// are given by:
    /// The values of dx and dy are given by:
    /// \f[ \cos\theta=\dfrac{dx}{\sqrt{|dx|^2+|dy|^2}}, \f]
    /// and
    /// \f[ \sin\theta=\dfrac{dy}{\sqrt{|dx|^2+|dy|^2}}. \f]
    /// Taken from: Saad Y."Iterative methods for sparse linear systems", p.193.
    /// We also check to see that sn is always a real (nonnegative) number. See
    /// pp.193-194 for an explanation.
    void generate_plane_rotation(std::complex<double>& dx,
                                 std::complex<double>& dy,
                                 std::complex<double>& cs,
                                 std::complex<double>& sn)
    {
      // If dy=0 then we do not need to apply a rotation
      if (dy == 0.0)
      {
        // Using theta=0 gives cos(theta)=1
        cs = 1.0;
 
        // Using theta=0 gives sin(theta)=0
        sn = 0.0;
      }
      // If dx or dy is large using the original form of calculting cs and sn is
      // naive since this may overflow or underflow so instead we calculate
      // r=sqrt(pow(|dx|,2)+pow(|dy|,2)) using r=|dy|sqrt(1+pow(|dx|/|dy|,2)) if
      // |dy|>|dx| [see <A
      // HREF=https://en.wikipedia.org/wiki/Hypot">Hypot</A>.].
      else if (std::abs(dy) > std::abs(dx))
      {
        // Since |dy|>|dx| calculate the ratio |dx|/|dy|
        std::complex<double> temp = dx / dy;
 
        // Calculate the value of sin(theta) using:
        //          sin(theta)=dy/sqrt(pow(|dx|,2)+pow(|dy|,2))
        //                    =(dy/|dy|)/sqrt(1+pow(|dx|/|dy|,2)).
        sn = (dy / std::abs(dy)) / sqrt(1.0 + pow(std::abs(temp), 2.0));
 
        // Calculate the value of cos(theta) using:
        //            cos(theta)=dx/sqrt(pow(|dy|,2)+pow(|dx|,2))
        //                      =(dx/|dy|)/sqrt(1+pow(|dx|/|dy|,2))
        //                      =(dx/dy)*sin(theta).
        cs = temp * sn;
      }
      // Otherwise, we have |dx|>=|dy| so to, again, avoid overflow or underflow
      // calculate the values of cs and sn using the method above
      else
      {
        // Since |dx|>=|dy| calculate the ratio dy/dx
        std::complex<double> temp = dy / dx;
 
        // Calculate the value of cos(theta) using:
        //          cos(theta)=dx/sqrt(pow(|dx|,2)+pow(|dy|,2))
        //                    =(dx/|dx|)/sqrt(1+pow(|dy|/|dx|,2)).
        cs = (dx / std::abs(dx)) / sqrt(1.0 + pow(std::abs(temp), 2.0));
 
        // Calculate the value of sin(theta) using:
        //            sin(theta)=dy/sqrt(pow(|dx|,2)+pow(|dy|,2))
        //                      =(dy/|dx|)/sqrt(1+pow(|dy|/|dx|,2))
        //                      =(dy/dx)*cos(theta).
        sn = temp * cs;
      }
 
      // Set the tolerance for sin(theta)
      double tolerance = 1.0e-15;
 
      // Make sure sn is real and nonnegative (it should be!)
      if ((std::fabs(sn.imag()) > tolerance) || (sn.real() < 0))
      {
        // Create an output stream
        std::ostringstream error_message_stream;
 
        // Create an error message
        error_message_stream << "The value of sin(theta) is not real "
                             << "and/or nonnegative. Value is: " << sn
                             << std::endl;
 
        // Throw an error
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
    } // End of generate_plane_rotation
 
    /// Helper function: Apply plane rotation. This is done using the
    /// update:
    /// \f[ \begin{bmatrix} dx \\ dy \end{bmatrix} \leftarrow \begin{bmatrix} \overline{\cos\theta} & \overline{\sin\theta} \\ -\sin\theta & \cos\theta \end{bmatrix} \begin{bmatrix} dx \\ dy \end{bmatrix}. \f]
    /// Taken from: Saad Y."Iterative methods for sparse linear systems", p.193.
    void apply_plane_rotation(std::complex<double>& dx,
                              std::complex<double>& dy,
                              std::complex<double>& cs,
                              std::complex<double>& sn)
    {
      // Calculate the value of dx but don't update it yet
      std::complex<double> temp = std::conj(cs) * dx + std::conj(sn) * dy;
 
      // Set the value of dy
      dy = -sn * dx + cs * dy;
 
      // Set the value of dx using the correct values of dx and dy
      dx = temp;
    } // End of apply_plane_rotation
 
    /// Number of iterations taken
    unsigned Iterations;
 
    /// Vector of pointers to the real and imaginary part of the system matrix
    Vector<CRDoubleMatrix*> Matrices_storage_pt;
 
    /// Boolean flag to indicate if the solve is done in re-solve mode,
    /// bypassing setup of matrix and preconditioner
    bool Resolving;
 
    /// Boolean flag to indicate if the matrix pointed to be Matrix_pt
    /// can be deleted.
    bool Matrix_can_be_deleted;
 
    /// boolean indicating use of left hand preconditioning (if true)
    /// or right hand preconditioning (if false)
    bool Preconditioner_LHS;
  };
 
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
 
  //=============================================================================
  /// Linear-algebra-type solver: Takes pointer to a matrix and rhs vector
  /// and returns the solution of the linear system.
  /// based on the algorithm presented in Templates for the
  /// Solution of Linear Systems: Building Blocks for Iterative Methods,
  /// Barrett, Berry et al, SIAM, 2006 and the implementation in the IML++
  /// library : http://math.nist.gov/iml++/
  //=============================================================================
  template<typename MATRIX>
  void HelmholtzGMRESMG<MATRIX>::solve_helper(
    DoubleMatrixBase* const& matrix_pt,
    const DoubleVector& rhs,
    DoubleVector& solution)
  {
    // Set the number of dof types (real and imaginary for this solver)
    unsigned n_dof_types = this->ndof_types();
 
#ifdef PARANOID
    // This only works for 2 dof types
    if (n_dof_types != 2)
    {
      // Create an output stream
      std::stringstream error_message_stream;
 
      // Create the error message
      error_message_stream << "This preconditioner only works for problems "
                           << "with 2 dof types\nYours has " << n_dof_types;
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
#endif
 
    // Get the number of dofs (note, the total number of dofs in the problem
    // is 2*n_row but if the constituent vectors and matrices were stored in
    // complex objects there would only be (n_row) rows so we use that)
    unsigned n_row = Matrices_storage_pt[0]->nrow();
 
    // Make sure Max_iter isn't greater than n_dof. The user cannot use this
    // many iterations when using Krylov subspace methods
    if (Max_iter > n_row)
    {
      // Create an output string stream
      std::ostringstream error_message_stream;
 
      // Create the error message
      error_message_stream << "The maximum number of iterations cannot exceed "
                           << "the number of rows in the problem."
                           << "\nMaximum number of iterations: " << Max_iter
                           << "\nNumber of rows: " << n_row << std::endl;
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
 
#ifdef PARANOID
    // Loop over the real and imaginary parts
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // PARANOID check that if the matrix is distributable then it should not
      // be then it should not be distributed
      if (dynamic_cast<DistributableLinearAlgebraObject*>(
            Matrices_storage_pt[dof_type]) != 0)
      {
        if (dynamic_cast<DistributableLinearAlgebraObject*>(
              Matrices_storage_pt[dof_type])
              ->distributed())
        {
          std::ostringstream error_message_stream;
          error_message_stream << "The matrix must not be distributed.";
          throw OomphLibError(error_message_stream.str(),
                              OOMPH_CURRENT_FUNCTION,
                              OOMPH_EXCEPTION_LOCATION);
        }
      }
    }
    // PARANOID check that this rhs distribution is setup
    if (!rhs.built())
    {
      std::ostringstream error_message_stream;
      error_message_stream << "The rhs vector distribution must be setup.";
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
    // PARANOID check that the rhs has the right number of global rows
    if (rhs.nrow() != 2 * n_row)
    {
      std::ostringstream error_message_stream;
      error_message_stream << "RHS does not have the same dimension as the"
                           << " linear system";
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
    // PARANOID check that the rhs is not distributed
    if (rhs.distribution_pt()->distributed())
    {
      std::ostringstream error_message_stream;
      error_message_stream << "The rhs vector must not be distributed.";
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
    // PARANOID check that if the result is setup it matches the distribution
    // of the rhs
    if (solution.built())
    {
      if (!(*rhs.distribution_pt() == *solution.distribution_pt()))
      {
        std::ostringstream error_message_stream;
        error_message_stream << "If the result distribution is setup then it "
                             << "must be the same as the rhs distribution";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
    } // if (solution[dof_type].built())
#endif
 
    // Set up the solution distribution if it's not already distributed
    if (!solution.built())
    {
      // Build the distribution
      solution.build(IterativeLinearSolver::distribution_pt(), 0.0);
    }
    // Otherwise initialise all entries to zero
    else
    {
      // Initialise the entries in the k-th vector in solution to zero
      solution.initialise(0.0);
    }
 
    // Create a vector of DoubleVectors (this is a distributed vector so we have
    // to create two separate DoubleVector objects to cope with the arithmetic)
    Vector<DoubleVector> block_solution(n_dof_types);
 
    // Create a vector of DoubleVectors (this is a distributed vector so we have
    // to create two separate DoubleVector objects to cope with the arithmetic)
    Vector<DoubleVector> block_rhs(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      block_solution[dof_type].build(this->block_distribution_pt(dof_type),
                                     0.0);
 
      // Build the distribution of the k-th constituent vector
      block_rhs[dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // Grab the solution vector in block form
    this->get_block_vectors(solution, block_solution);
 
    // Grab the RHS vector in block form
    this->get_block_vectors(rhs, block_rhs);
 
    // Start the solver timer
    double t_start = TimingHelpers::timer();
 
    // Storage for the relative residual
    double resid;
 
    // Initialise vectors (since these are not distributed vectors we template
    // one vector by the type std::complex<double> instead of using two vectors,
    // each templated by the type double
 
    // Vector, s, used in the minimisation problem: J(y)=min||s-R_m*y||
    // [see Saad Y."Iterative methods for sparse linear systems", p.176.]
    Vector<std::complex<double>> s(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Vector to store the value of cos(theta) when using the Givens rotation
    Vector<std::complex<double>> cs(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Vector to store the value of sin(theta) when using the Givens rotation
    Vector<std::complex<double>> sn(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Create a vector of DoubleVectors (this is a distributed vector so we have
    // to create two separate DoubleVector objects to cope with the arithmetic)
    Vector<DoubleVector> block_w(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      block_w[dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // Set up the preconditioner only if we're not re-solving
    if (!Resolving)
    {
      // Only set up the preconditioner before solve if required
      if (Setup_preconditioner_before_solve)
      {
        // Set up the preconditioner from the Jacobian matrix
        double t_start_prec = TimingHelpers::timer();
 
        // Use the setup function in the Preconditioner class
        preconditioner_pt()->setup(dynamic_cast<MATRIX*>(matrix_pt));
 
        // Doc time for setup of preconditioner
        double t_end_prec = TimingHelpers::timer();
        Preconditioner_setup_time = t_end_prec - t_start_prec;
 
        // If time documentation is enabled
        if (Doc_time)
        {
          // Output the time taken
          oomph_info << "Time for setup of preconditioner [sec]: "
                     << Preconditioner_setup_time << std::endl;
        }
      }
    }
    else
    {
      // If time documentation is enabled
      if (Doc_time)
      {
        // Notify the user
        oomph_info << "Setup of preconditioner is bypassed in resolve mode"
                   << std::endl;
      }
    } // if (!Resolving) else
 
    // Create a vector of DoubleVectors to store the RHS of b-Jx=Mr. We assume
    // both x=0 and that a preconditioner is not applied by which we deduce b=r
    Vector<DoubleVector> block_r(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      block_r[dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // If we're using LHS preconditioning solve b-Jx=Mr for r (assumes x=0)
    // so calculate r=M^{-1}b otherwise set r=b (RHS prec.)
    if (Preconditioner_LHS)
    {
      // Create a vector of the same size as rhs
      DoubleVector r(IterativeLinearSolver::distribution_pt(), 0.0);
 
      // Copy the vectors in r to full_r
      this->return_block_vectors(block_r, r);
 
      // Use the preconditioner
      preconditioner_pt()->preconditioner_solve(rhs, r);
 
      // Copy the vector full_r into the vectors in r
      this->get_block_vectors(r, block_r);
    }
    else
    {
      // Store the value of b (the RHS vector) in r
      for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
      {
        // Copy the entries of rhs into r
        block_r[dof_type] = block_rhs[dof_type];
      }
    } // if(Preconditioner_LHS)
 
    // Calculate the norm of the real part of r
    double norm_r = block_r[0].norm();
 
    // Calculate the norm of the imaginary part of r
    double norm_c = block_r[1].norm();
 
    // Compute norm(r)
    double normb = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
    // Set the value of beta (the initial residual)
    double beta = normb;
 
    // Compute the initial relative residual. If the entries of the RHS vector
    // are all zero then set normb equal to one. This is because we divide the
    // value of the norm at later stages by normb and dividing by zero is not
    // definied
    if (normb == 0.0)
    {
      // Set the value of normb
      normb = 1.0;
    }
 
    // Calculate the ratio between the initial norm and the current norm.
    // Since we haven't completed an iteration yet this will simply be one
    // unless normb was zero, in which case resid will have value zero
    resid = beta / normb;
 
    // If required, will document convergence history to screen or file (if
    // stream open)
    if (Doc_convergence_history)
    {
      // If an output file which is open isn't provided then output to screen
      if (!Output_file_stream.is_open())
      {
        // Output the residual value to the screen
        oomph_info << 0 << " " << resid << std::endl;
      }
      // Otherwise, output to file
      else
      {
        // Output the residual value to file
        Output_file_stream << 0 << " " << resid << std::endl;
      }
    } // if (Doc_convergence_history)
 
    // If the GMRES algorithm converges immediately
    if (resid <= Tolerance)
    {
      // If time documentation is enabled
      if (Doc_time)
      {
        // Notify the user
        oomph_info << "GMRES converged immediately. Normalised residual norm: "
                   << resid << std::endl;
      }
 
      // Finish running the solver
      return;
    } // if (resid<=Tolerance)
 
    // Initialise a vector of orthogonal basis vectors
    Vector<Vector<DoubleVector>> block_v;
 
    // Resize the number of vectors needed
    block_v.resize(n_row + 1);
 
    // Resize each Vector of DoubleVectors to store the real and imaginary
    // part of a given vector
    for (unsigned dof_type = 0; dof_type < n_row + 1; dof_type++)
    {
      // Create two DoubleVector objects in each Vector
      block_v[dof_type].resize(n_dof_types);
    }
 
    // Initialise the upper hessenberg matrix. Since we are not using
    // distributed vectors here, the algebra is best done using entries
    // of the type std::complex<double>. NOTE: For implementation purposes
    // the upper Hessenberg matrix indices are swapped so the matrix is
    // effectively transposed
    Vector<Vector<std::complex<double>>> hessenberg(n_row + 1);
 
    // Build the zeroth basis vector
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the k-th part of the zeroth vector. Here k=0 and k=1 correspond
      // to the real and imaginary part of the zeroth vector, respectively
      block_v[0][dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // Loop over the real and imaginary parts of v
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // For fast access
      double* v0_pt = block_v[0][dof_type].values_pt();
 
      // For fast access
      const double* block_r_pt = block_r[dof_type].values_pt();
 
      // Set the zeroth basis vector v[0] to r/beta
      for (unsigned i = 0; i < n_row; i++)
      {
        // Assign the i-th entry of the zeroth basis vector
        v0_pt[i] = block_r_pt[i] / beta;
      }
    } // for (unsigned k=0;k<n_dof_types;k++)
 
    // Set the first entry in the minimisation problem RHS vector (is meant
    // to the vector beta*e_1 initially, where e_1 is the unit vector with
    // one in its first entry)
    s[0] = beta;
 
    // Compute the next step of the iterative scheme
    for (unsigned j = 0; j < Max_iter; j++)
    {
      // Resize the next column of the upper hessenberg matrix
      hessenberg[j].resize(j + 2, std::complex<double>(0.0, 0.0));
 
      // Calculate w=M^{-1}(Jv[j]) (LHS prec.) or w=JM^{-1}v (RHS prec.)
      {
        // Create a temporary vector
        DoubleVector vj(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Create a temporary vector
        DoubleVector temp(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Create a temporary vector
        DoubleVector w(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Create a temporary vector of DoubleVectors
        Vector<DoubleVector> block_temp(2);
 
        // Create two DoubleVectors
        for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
        {
          block_temp[dof_type].build(this->block_distribution_pt(dof_type),
                                     0.0);
        }
 
        // If we're using LHS preconditioning
        if (Preconditioner_LHS)
        {
          // Solve Jv[j]=Mw for w. Note, we cannot use inbuilt complex matrix
          // algebra here as we're using distributed vectors
          complex_matrix_multiplication(
            Matrices_storage_pt, block_v[j], block_temp);
 
          // Copy block_temp into temp
          this->return_block_vectors(block_temp, temp);
 
          // Copy block_w into w
          this->return_block_vectors(block_w, w);
 
          // Apply the preconditioner
          this->preconditioner_pt()->preconditioner_solve(temp, w);
 
          // Copy w into block_w
          this->get_block_vectors(w, block_w);
        }
        // If we're using RHS preconditioning
        else
        {
          // Copy the real and imaginary part of v[j] into one vector, vj
          this->return_block_vectors(block_v[j], vj);
 
          // Use w=JM^{-1}v by saad p270
          this->preconditioner_pt()->preconditioner_solve(vj, temp);
 
          // Copy w into block_w
          this->get_block_vectors(temp, block_temp);
 
          // Solve Jv[j] = Mw for w. Note, we cannot use inbuilt complex matrix
          // algebra here as we're using distributed vectors
          complex_matrix_multiplication(
            Matrices_storage_pt, block_temp, block_w);
        }
      } // Calculate w=M^{-1}(Jv[j]) (LHS prec.) or w=JM^{-1}v (RHS prec.)
 
      // For fast access
      double* block_w_r_pt = block_w[0].values_pt();
 
      // For fast access
      double* block_w_c_pt = block_w[1].values_pt();
 
      // Loop over all of the entries on and above the principal subdiagonal of
      // the Hessenberg matrix in the j-th column (remembering that
      // the indices of the upper Hessenberg matrix are swapped for the purpose
      // of implementation)
      for (unsigned i = 0; i < j + 1; i++)
      {
        // For fast access
        const double* vi_r_pt = block_v[i][0].values_pt();
 
        // For fast access
        const double* vi_c_pt = block_v[i][1].values_pt();
 
        // Loop over the entries of v and w
        for (unsigned k = 0; k < n_row; k++)
        {
          // Store the appropriate entry in v as a complex value
          std::complex<double> complex_v(vi_r_pt[k], vi_c_pt[k]);
 
          // Store the appropriate entry in w as a complex value
          std::complex<double> complex_w(block_w_r_pt[k], block_w_c_pt[k]);
 
          // Update the value of H(i,j) noting we're computing a complex
          // inner product here (the ordering is very important here!)
          hessenberg[j][i] += std::conj(complex_v) * complex_w;
        }
 
        // Orthonormalise w against all previous orthogonal vectors, v_i by
        // looping over its entries and updating them
        for (unsigned k = 0; k < n_row; k++)
        {
          // Update the real part of the k-th entry of w
          block_w_r_pt[k] -= (hessenberg[j][i].real() * vi_r_pt[k] -
                              hessenberg[j][i].imag() * vi_c_pt[k]);
 
          // Update the imaginary part of the k-th entry of w
          block_w_c_pt[k] -= (hessenberg[j][i].real() * vi_c_pt[k] +
                              hessenberg[j][i].imag() * vi_r_pt[k]);
        }
      } // for (unsigned i=0;i<j+1;i++)
 
      // Calculate the norm of the real part of w
      norm_r = block_w[0].norm();
 
      // Calculate the norm of the imaginary part of w
      norm_c = block_w[1].norm();
 
      // Calculate the norm of the vector w using norm_r and norm_c and assign
      // its value to the appropriate entry in the Hessenberg matrix
      hessenberg[j][j + 1] = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
      // Build the (j+1)-th basis vector
      for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
      {
        // Build the k-th part of the zeroth vector. Here k=0 and k=1 correspond
        // to the real and imaginary part of the zeroth vector, respectively
        block_v[j + 1][dof_type].build(this->block_distribution_pt(dof_type),
                                       0.0);
      }
 
      // Check if the value of hessenberg[j][j+1] is zero. If it
      // isn't then we update the next entries in v
      if (hessenberg[j][j + 1] != 0.0)
      {
        // For fast access
        double* v_r_pt = block_v[j + 1][0].values_pt();
 
        // For fast access
        double* v_c_pt = block_v[j + 1][1].values_pt();
 
        // For fast access
        const double* block_w_r_pt = block_w[0].values_pt();
 
        // For fast access
        const double* block_w_c_pt = block_w[1].values_pt();
 
        // Notice, the value of H(j,j+1), as calculated above, is clearly a real
        // number. As such, calculating the division
        //                      v_{j+1}=w_{j}/h_{j+1,j},
        // here is simple, i.e. we don't need to worry about cross terms in the
        // algebra. To avoid computing h_{j+1,j} several times we precompute it
        double h_subdiag_val = hessenberg[j][j + 1].real();
 
        // Loop over the entries of the new orthogonal vector and set its values
        for (unsigned k = 0; k < n_row; k++)
        {
          // The i-th entry of the real component is given by
          v_r_pt[k] = block_w_r_pt[k] / h_subdiag_val;
 
          // Similarly, the i-th entry of the imaginary component is given by
          v_c_pt[k] = block_w_c_pt[k] / h_subdiag_val;
        }
      }
      // Otherwise, we have to jump to the next part of the algorithm; if
      // the value of hessenberg[j][j+1] is zero then the norm of the latest
      // orthogonal vector is zero. This is only possible if the entries
      // in w are all zero. As a result, the Krylov space of A and r_0 has
      // been spanned by the previously calculated orthogonal vectors
      else
      {
        // Book says "Set m=j and jump to step 11" (p.172)...
        // Do something here!
        oomph_info << "Subdiagonal Hessenberg entry is zero. "
                   << "Do something here..." << std::endl;
      } // if (hessenberg[j][j+1]!=0.0)
 
      // Loop over the entries in the Hessenberg matrix and calculate the
      // entries of the Givens rotation matrices
      for (unsigned k = 0; k < j; k++)
      {
        // Apply the plane rotation to all of the previous entries in the
        // (j)-th column (remembering the indexing is reversed)
        apply_plane_rotation(
          hessenberg[j][k], hessenberg[j][k + 1], cs[k], sn[k]);
      }
 
      // Now calculate the entries of the latest Givens rotation matrix
      generate_plane_rotation(
        hessenberg[j][j], hessenberg[j][j + 1], cs[j], sn[j]);
 
      // Apply the plane rotation using the newly calculated entries
      apply_plane_rotation(
        hessenberg[j][j], hessenberg[j][j + 1], cs[j], sn[j]);
 
      // Apply a plane rotation to the corresponding entry in the vector
      // s used in the minimisation problem, J(y)=min||s-R_m*y||
      apply_plane_rotation(s[j], s[j + 1], cs[j], sn[j]);
 
      // Compute current residual using equation (6.42) in Saad Y, "Iterative
      // methods for sparse linear systems", p.177.]. Note, since s has complex
      // entries we have to use std::abs instead of std::fabs
      beta = std::abs(s[j + 1]);
 
      // Compute the relative residual
      resid = beta / normb;
 
      // If required will document convergence history to screen or file (if
      // stream open)
      if (Doc_convergence_history)
      {
        // If an output file which is open isn't provided then output to screen
        if (!Output_file_stream.is_open())
        {
          // Output the residual value to the screen
          oomph_info << j + 1 << " " << resid << std::endl;
        }
        // Otherwise, output to file
        else
        {
          // Output the residual value to file
          Output_file_stream << j + 1 << " " << resid << std::endl;
        }
      } // if (Doc_convergence_history)
 
      // If the required tolerance has been met
      if (resid < Tolerance)
      {
        // Store the number of iterations taken
        Iterations = j + 1;
 
        // Update the result vector using the result, x=x_0+V_m*y (where V_m
        // is given by v here)
        update(j, hessenberg, s, block_v, block_solution);
 
        // Copy the vectors in block_solution to solution
        this->return_block_vectors(block_solution, solution);
 
        // If time documentation was enabled
        if (Doc_time)
        {
          // Output the current normalised residual norm
          oomph_info << "\nGMRES converged (1). Normalised residual norm: "
                     << resid << std::endl;
 
          // Output the number of iterations it took for convergence
          oomph_info << "Number of iterations to convergence: " << j + 1 << "\n"
                     << std::endl;
        }
 
        // Stop the timer
        double t_end = TimingHelpers::timer();
 
        // Calculate the time taken to calculate the solution
        Solution_time = t_end - t_start;
 
        // If time documentation was enabled
        if (Doc_time)
        {
          // Output the time taken to solve the problem using GMRES
          oomph_info << "Time for solve with GMRES [sec]: " << Solution_time
                     << std::endl;
        }
 
        // As we've met the tolerance for the solver and everything that should
        // be documented, has been, finish using the solver
        return;
      } // if (resid<Tolerance)
    } // for (unsigned j=0;j<Max_iter;j++)
 
    // Store the number of iterations taken
    Iterations = Max_iter;
 
    // Only update if we actually did something
    if (Max_iter > 0)
    {
      // Update the result vector using the result, x=x_0+V_m*y (where V_m
      // is given by v here)
      update(Max_iter - 1, hessenberg, s, block_v, block_solution);
 
      // Copy the vectors in block_solution to solution
      this->return_block_vectors(block_solution, solution);
    }
 
    // Solve Mr=(b-Jx) for r
    {
      // Create a temporary vector of DoubleVectors
      Vector<DoubleVector> block_temp(2);
 
      // Create two DoubleVectors
      for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
      {
        // Build the distribution of the (dof_type)-th vector
        block_temp[dof_type].build(this->block_distribution_pt(dof_type), 0.0);
      }
 
      // Calculate the value of Jx
      complex_matrix_multiplication(
        Matrices_storage_pt, block_solution, block_temp);
 
      // Get the values pointer of the vector (real)
      double* block_temp_r_pt = block_temp[0].values_pt();
 
      // Get the values pointer of the vector (imaginary)
      double* block_temp_c_pt = block_temp[1].values_pt();
 
      // Get the values pointer of the RHS vector (real)
      const double* block_rhs_r_pt = block_rhs[0].values_pt();
 
      // Get the values pointer of the RHS vector (imaginary)
      const double* block_rhs_c_pt = block_rhs[1].values_pt();
 
      // Loop over the dofs
      for (unsigned i = 0; i < n_row; i++)
      {
        // Calculate b-Jx (real)
        block_temp_r_pt[i] = block_rhs_r_pt[i] - block_temp_r_pt[i];
 
        // Calculate b-Jx (imaginary)
        block_temp_c_pt[i] = block_rhs_c_pt[i] - block_temp_c_pt[i];
      }
 
      // If we're using LHS preconditioning
      if (Preconditioner_LHS)
      {
        // Create a temporary DoubleVectors
        DoubleVector temp(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Create a vector of the same size as rhs
        DoubleVector r(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Copy the vectors in r to full_r
        this->return_block_vectors(block_temp, temp);
 
        // Copy the vectors in r to full_r
        this->return_block_vectors(block_r, r);
 
        // Apply the preconditioner
        preconditioner_pt()->preconditioner_solve(temp, r);
      }
    }
 
    // Compute the current residual
    beta = 0.0;
 
    // Get access to the values pointer (real)
    norm_r = block_r[0].norm();
 
    // Get access to the values pointer (imaginary)
    norm_c = block_r[1].norm();
 
    // Calculate the full norm
    beta = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
    // Calculate the relative residual
    resid = beta / normb;
 
    // If the relative residual lies within tolerance
    if (resid < Tolerance)
    {
      // If time documentation is enabled
      if (Doc_time)
      {
        // Notify the user
        oomph_info << "\nGMRES converged (2). Normalised residual norm: "
                   << resid
                   << "\nNumber of iterations to convergence: " << Iterations
                   << "\n"
                   << std::endl;
      }
 
      // End the timer
      double t_end = TimingHelpers::timer();
 
      // Calculate the time taken for the solver
      Solution_time = t_end - t_start;
 
      // If time documentation is enabled
      if (Doc_time)
      {
        oomph_info << "Time for solve with GMRES [sec]: " << Solution_time
                   << std::endl;
      }
      return;
    }
 
    // Otherwise GMRES failed convergence
    oomph_info << "\nGMRES did not converge to required tolerance! "
               << "\nReturning with normalised residual norm: " << resid
               << "\nafter " << Max_iter << " iterations.\n"
               << std::endl;
 
    // Throw an error if requested
    if (Throw_error_after_max_iter)
    {
      std::string err = "Solver failed to converge and you requested an error";
      err += " on convergence failures.";
      throw OomphLibError(
        err, OOMPH_EXCEPTION_LOCATION, OOMPH_CURRENT_FUNCTION);
    }
 
    // Finish using the solver
    return;
  } // End of solve_helper
 
 
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
  /// ////////////////////////////////////////////////////////////////////
 
 
  //======================================================================
  /// The FGMRES method, i.e. the flexible variant of the GMRES
  /// method which allows for nonconstant preconditioners [see Saad Y,
  /// "Iterative methods for sparse linear systems", p.287]. Note, FGMRES
  /// can only cater to right preconditioning; if the user tries to switch
  /// to left preconditioning they will be notified of this
  //======================================================================
  template<typename MATRIX>
  class HelmholtzFGMRESMG : public virtual HelmholtzGMRESMG<MATRIX>
  {
  public:
    /// Constructor (empty)
    HelmholtzFGMRESMG() : HelmholtzGMRESMG<MATRIX>()
    {
      // Can only use RHS preconditioning
      this->Preconditioner_LHS = false;
    };
 
    /// Destructor (cleanup storage)
    virtual ~HelmholtzFGMRESMG()
    {
      // Call the clean up function in the base class GMRES
      this->clean_up_memory();
    }
 
    /// Broken copy constructor
    HelmholtzFGMRESMG(const HelmholtzFGMRESMG&) = delete;
 
    /// Broken assignment operator
    void operator=(const HelmholtzFGMRESMG&) = delete;
 
    /// Overloaded function to let the user know that left
    /// preconditioning is not possible with FGMRES, only right preconditioning
    void set_preconditioner_LHS()
    {
      // Create an output stream
      std::ostringstream error_message_stream;
 
      // Create an error message
      error_message_stream << "FGMRES cannot use left preconditioning. It is "
                           << "only capable of using right preconditioning."
                           << std::endl;
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    } // End of set_preconditioner_LHS
 
    /// Solver: Takes pointer to problem and returns the results vector
    /// which contains the solution of the linear system defined by
    /// the problem's fully assembled Jacobian and residual vector.
    void solve(Problem* const& problem_pt, DoubleVector& result)
    {
#ifdef OOMPH_HAS_MPI
      // Make sure that this is running in serial. Can't guarantee it'll
      // work when the problem is distributed over several processors
      if (MPI_Helpers::communicator_pt()->nproc() > 1)
      {
        // Throw a warning
        OomphLibWarning("Can't guarantee the MG solver will work in parallel!",
                        OOMPH_CURRENT_FUNCTION,
                        OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      // Find # of degrees of freedom (variables)
      unsigned n_dof = problem_pt->ndof();
 
      // Initialise timer
      double t_start = TimingHelpers::timer();
 
      // We're not re-solving
      this->Resolving = false;
 
      // Get rid of any previously stored data
      this->clean_up_memory();
 
      // Grab the communicator from the MGProblem object and assign it
      this->set_comm_pt(problem_pt->communicator_pt());
 
      // Setup the distribution
      LinearAlgebraDistribution dist(
        problem_pt->communicator_pt(), n_dof, false);
 
      // Build the internal distribution in this way because both the
      // IterativeLinearSolver and BlockPreconditioner class have base-
      // class subobjects of type oomph::DistributableLinearAlgebraObject
      IterativeLinearSolver::build_distribution(dist);
 
      // Get Jacobian matrix in format specified by template parameter
      // and nonlinear residual vector
      MATRIX* matrix_pt = new MATRIX;
      DoubleVector f;
      if (dynamic_cast<DistributableLinearAlgebraObject*>(matrix_pt) != 0)
      {
        if (dynamic_cast<CRDoubleMatrix*>(matrix_pt) != 0)
        {
          dynamic_cast<CRDoubleMatrix*>(matrix_pt)->build(
            IterativeLinearSolver::distribution_pt());
          f.build(IterativeLinearSolver::distribution_pt(), 0.0);
        }
      }
 
      // Get the Jacobian and residuals vector
      problem_pt->get_jacobian(f, *matrix_pt);
 
      // We've made the matrix, we can delete it...
      this->Matrix_can_be_deleted = true;
 
      // Replace the current matrix used in Preconditioner by the new matrix
      this->set_matrix_pt(matrix_pt);
 
      // The preconditioner works with one mesh; set it! Since we only use
      // the block preconditioner on the finest level, we use the mesh from
      // that level
      this->set_nmesh(1);
 
      // Elements in actual pml layer are trivially wrapped versions of
      // their bulk counterparts. Technically they are different elements
      // so we have to allow different element types
      bool allow_different_element_types_in_mesh = true;
      this->set_mesh(
        0, problem_pt->mesh_pt(), allow_different_element_types_in_mesh);
 
      // Set up the generic block look up scheme
      this->block_setup();
 
      // Extract the number of blocks.
      unsigned nblock_types = this->nblock_types();
 
#ifdef PARANOID
      // PARANOID check - there must only be two block types
      if (nblock_types != 2)
      {
        // Create the error message
        std::stringstream tmp;
        tmp << "There are supposed to be two block types.\nYours has "
            << nblock_types << std::endl;
 
        // Throw an error
        throw OomphLibError(
          tmp.str(), OOMPH_CURRENT_FUNCTION, OOMPH_EXCEPTION_LOCATION);
      }
#endif
 
      // Resize the storage for the system matrices
      this->Matrices_storage_pt.resize(2, 0);
 
      // Loop over the rows of the block matrix
      for (unsigned i = 0; i < nblock_types; i++)
      {
        // Fix the column index
        unsigned j = 0;
 
        // Create new CRDoubleMatrix objects
        this->Matrices_storage_pt[i] = new CRDoubleMatrix;
 
        // Extract the required blocks, i.e. the first column
        this->get_block(i, j, *(this->Matrices_storage_pt[i]));
      }
 
      // Doc time for setup
      double t_end = TimingHelpers::timer();
      this->Jacobian_setup_time = t_end - t_start;
 
      if (this->Doc_time)
      {
        oomph_info << "\nTime for setup of block Jacobian [sec]: "
                   << this->Jacobian_setup_time << std::endl;
      }
 
      // Call linear algebra-style solver
      // If the result distribution is wrong, then redistribute
      // before the solve and return to original distribution
      // afterwards
      if ((!(*result.distribution_pt() ==
             *IterativeLinearSolver::distribution_pt())) &&
          (result.built()))
      {
        // Make a distribution object
        LinearAlgebraDistribution temp_global_dist(result.distribution_pt());
 
        // Build the result vector distribution
        result.build(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Solve the problem
        solve_helper(matrix_pt, f, result);
 
        // Redistribute the vector
        result.redistribute(&temp_global_dist);
      }
      // Otherwise just solve
      else
      {
        // Solve
        solve_helper(matrix_pt, f, result);
      }
 
      // Kill matrix unless it's still required for resolve
      if (!(this->Enable_resolve))
      {
        // Clean up anything left in memory
        this->clean_up_memory();
      }
    } // End of solve
 
  private:
    /// General interface to solve function
    void solve_helper(DoubleMatrixBase* const& matrix_pt,
                      const DoubleVector& rhs,
                      DoubleVector& solution);
 
    /// Helper function to update the result vector
    void update(const unsigned& k,
                const Vector<Vector<std::complex<double>>>& hessenberg,
                const Vector<std::complex<double>>& s,
                const Vector<Vector<DoubleVector>>& z_m,
                Vector<DoubleVector>& x)
    {
      // Make a local copy of s
      Vector<std::complex<double>> y(s);
 
      //-----------------------------------------------------------------
      // The Hessenberg matrix should be an upper triangular matrix at
      // this point (although from its storage it would appear to be a
      // lower triangular matrix since the indexing has been reversed)
      // so finding the minimiser of J(y)=min||s-R_m*y|| where R_m is
      // the matrix R in the QR factorisation of the Hessenberg matrix.
      // Therefore, to obtain y we simply need to use a backwards
      // substitution. Note: The implementation here may appear to be
      // somewhat confusing as the indexing in the Hessenberg matrix is
      // reversed. This implementation of a backwards substitution does
      // not run along the columns of the triangular matrix but rather
      // up the rows.
      //-----------------------------------------------------------------
 
      // The outer loop is a loop over the columns of the Hessenberg matrix
      // since the indexing is reversed
      for (int i = int(k); i >= 0; i--)
      {
        // Divide the i-th entry of y by the i-th diagonal entry of H
        y[i] /= hessenberg[i][i];
 
        // The inner loop is a loop over the rows of the Hessenberg matrix
        for (int j = i - 1; j >= 0; j--)
        {
          // Update the j-th entry of y
          y[j] -= hessenberg[i][j] * y[i];
        }
      } // for (int i=int(k);i>=0;i--)
 
      // Calculate the number of entries in x (simply use the real part as
      // both the real and imaginary part should have the same length)
      unsigned n_row = x[0].nrow();
 
      // Build a temporary vector with entries initialised to 0.0
      Vector<DoubleVector> block_update(2);
 
      // Build the distributions
      for (unsigned dof_type = 0; dof_type < 2; dof_type++)
      {
        // Build the (dof_type)-th vector of block_update
        block_update[dof_type].build(x[0].distribution_pt(), 0.0);
      }
 
      // Get access to the underlying values
      double* block_update_r_pt = block_update[0].values_pt();
 
      // Get access to the underlying values
      double* block_update_c_pt = block_update[1].values_pt();
 
      // Calculate x=Vy
      for (unsigned j = 0; j <= k; j++)
      {
        // Get access to j-th column of Z_m
        const double* z_mj_r_pt = z_m[j][0].values_pt();
 
        // Get access to j-th column of Z_m
        const double* z_mj_c_pt = z_m[j][1].values_pt();
 
        // Loop over the entries in x and update them
        for (unsigned i = 0; i < n_row; i++)
        {
          // Update the real part of the i-th entry in x
          block_update_r_pt[i] +=
            (z_mj_r_pt[i] * y[j].real()) - (z_mj_c_pt[i] * y[j].imag());
 
          // Update the imaginary part of the i-th entry in x
          block_update_c_pt[i] +=
            (z_mj_c_pt[i] * y[j].real()) + (z_mj_r_pt[i] * y[j].imag());
        }
      } // for (unsigned j=0;j<=k;j++)
 
      // Use the update: x_m=x_0+inv(M)Vy [see Saad Y,"Iterative methods for
      // sparse linear systems", p.284]
      for (unsigned dof_type = 0; dof_type < 2; dof_type++)
      {
        // Update the solution
        x[dof_type] += block_update[dof_type];
      }
    } // End of update
  };
 
 
  /// ///////////////////////////////////////////////////////////////////
  /// ///////////////////////////////////////////////////////////////////
  /// ///////////////////////////////////////////////////////////////////
 
 
  //=============================================================================
  /// Linear-algebra-type solver: Takes pointer to a matrix and rhs vector
  /// and returns the solution of the linear system.
  /// based on the algorithm presented in Templates for the
  /// Solution of Linear Systems: Building Blocks for Iterative Methods,
  /// Barrett, Berry et al, SIAM, 2006 and the implementation in the IML++
  /// library : http://math.nist.gov/iml++/
  //=============================================================================
  template<typename MATRIX>
  void HelmholtzFGMRESMG<MATRIX>::solve_helper(
    DoubleMatrixBase* const& matrix_pt,
    const DoubleVector& rhs,
    DoubleVector& solution)
  {
    // Set the number of dof types (real and imaginary for this solver)
    unsigned n_dof_types = this->ndof_types();
 
#ifdef PARANOID
    // This only works for 2 dof types
    if (n_dof_types != 2)
    {
      // Create an output stream
      std::stringstream error_message_stream;
 
      // Create the error message
      error_message_stream << "This preconditioner only works for problems "
                           << "with 2 dof types\nYours has " << n_dof_types;
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
#endif
 
    // Get the number of dofs (note, the total number of dofs in the problem
    // is 2*n_row but if the constituent vectors and matrices were stored in
    // complex objects there would only be (n_row) rows so we use that)
    unsigned n_row = this->Matrices_storage_pt[0]->nrow();
 
    // Make sure Max_iter isn't greater than n_dof. The user cannot use this
    // many iterations when using Krylov subspace methods
    if (this->Max_iter > n_row)
    {
      // Create an output string stream
      std::ostringstream error_message_stream;
 
      // Create the error message
      error_message_stream << "The maximum number of iterations cannot exceed "
                           << "the number of rows in the problem."
                           << "\nMaximum number of iterations: "
                           << this->Max_iter << "\nNumber of rows: " << n_row
                           << std::endl;
 
      // Throw the error message
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
 
#ifdef PARANOID
    // Loop over the real and imaginary parts
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // PARANOID check that if the matrix is distributable then it should not
      // be then it should not be distributed
      if (dynamic_cast<DistributableLinearAlgebraObject*>(
            this->Matrices_storage_pt[dof_type]) != 0)
      {
        if (dynamic_cast<DistributableLinearAlgebraObject*>(
              this->Matrices_storage_pt[dof_type])
              ->distributed())
        {
          std::ostringstream error_message_stream;
          error_message_stream << "The matrix must not be distributed.";
          throw OomphLibError(error_message_stream.str(),
                              OOMPH_CURRENT_FUNCTION,
                              OOMPH_EXCEPTION_LOCATION);
        }
      }
    }
    // PARANOID check that this rhs distribution is setup
    if (!rhs.built())
    {
      std::ostringstream error_message_stream;
      error_message_stream << "The rhs vector distribution must be setup.";
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
    // PARANOID check that the rhs has the right number of global rows
    if (rhs.nrow() != 2 * n_row)
    {
      std::ostringstream error_message_stream;
      error_message_stream << "RHS does not have the same dimension as the"
                           << " linear system";
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
    // PARANOID check that the rhs is not distributed
    if (rhs.distribution_pt()->distributed())
    {
      std::ostringstream error_message_stream;
      error_message_stream << "The rhs vector must not be distributed.";
      throw OomphLibError(error_message_stream.str(),
                          OOMPH_CURRENT_FUNCTION,
                          OOMPH_EXCEPTION_LOCATION);
    }
    // PARANOID check that if the result is setup it matches the distribution
    // of the rhs
    if (solution.built())
    {
      if (!(*rhs.distribution_pt() == *solution.distribution_pt()))
      {
        std::ostringstream error_message_stream;
        error_message_stream << "If the result distribution is setup then it "
                             << "must be the same as the rhs distribution";
        throw OomphLibError(error_message_stream.str(),
                            OOMPH_CURRENT_FUNCTION,
                            OOMPH_EXCEPTION_LOCATION);
      }
    } // if (solution[dof_type].built())
#endif
 
    // Set up the solution distribution if it's not already distributed
    if (!solution.built())
    {
      // Build the distribution
      solution.build(IterativeLinearSolver::distribution_pt(), 0.0);
    }
    // Otherwise initialise all entries to zero
    else
    {
      // Initialise the entries in the k-th vector in solution to zero
      solution.initialise(0.0);
    }
 
    // Create a vector of DoubleVectors (this is a distributed vector so we have
    // to create two separate DoubleVector objects to cope with the arithmetic)
    Vector<DoubleVector> block_solution(n_dof_types);
 
    // Create a vector of DoubleVectors (this is a distributed vector so we have
    // to create two separate DoubleVector objects to cope with the arithmetic)
    Vector<DoubleVector> block_rhs(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      block_solution[dof_type].build(this->block_distribution_pt(dof_type),
                                     0.0);
 
      // Build the distribution of the k-th constituent vector
      block_rhs[dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // Grab the solution vector in block form
    this->get_block_vectors(solution, block_solution);
 
    // Grab the RHS vector in block form
    this->get_block_vectors(rhs, block_rhs);
 
    // Start the solver timer
    double t_start = TimingHelpers::timer();
 
    // Storage for the relative residual
    double resid;
 
    // Initialise vectors (since these are not distributed vectors we template
    // one vector by the type std::complex<double> instead of using two vectors,
    // each templated by the type double
 
    // Vector, s, used in the minimisation problem: J(y)=min||s-R_m*y||
    // [see Saad Y."Iterative methods for sparse linear systems", p.176.]
    Vector<std::complex<double>> s(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Vector to store the value of cos(theta) when using the Givens rotation
    Vector<std::complex<double>> cs(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Vector to store the value of sin(theta) when using the Givens rotation
    Vector<std::complex<double>> sn(n_row + 1, std::complex<double>(0.0, 0.0));
 
    // Create a vector of DoubleVectors (this is a distributed vector so we have
    // to create two separate DoubleVector objects to cope with the arithmetic)
    Vector<DoubleVector> block_w(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      block_w[dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // Set up the preconditioner only if we're not re-solving
    if (!(this->Resolving))
    {
      // Only set up the preconditioner before solve if required
      if (this->Setup_preconditioner_before_solve)
      {
        // Set up the preconditioner from the Jacobian matrix
        double t_start_prec = TimingHelpers::timer();
 
        // Use the setup function in the Preconditioner class
        this->preconditioner_pt()->setup(dynamic_cast<MATRIX*>(matrix_pt));
 
        // Doc time for setup of preconditioner
        double t_end_prec = TimingHelpers::timer();
        this->Preconditioner_setup_time = t_end_prec - t_start_prec;
 
        // If time documentation is enabled
        if (this->Doc_time)
        {
          // Output the time taken
          oomph_info << "Time for setup of preconditioner [sec]: "
                     << this->Preconditioner_setup_time << std::endl;
        }
      }
    }
    else
    {
      // If time documentation is enabled
      if (this->Doc_time)
      {
        // Notify the user
        oomph_info << "Setup of preconditioner is bypassed in resolve mode"
                   << std::endl;
      }
    } // if (!Resolving) else
 
    // Create a vector of DoubleVectors to store the RHS of b-Jx=Mr. We assume
    // both x=0 and that a preconditioner is not applied by which we deduce b=r
    Vector<DoubleVector> block_r(n_dof_types);
 
    // Build the distribution of both vectors
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the distribution of the k-th constituent vector
      block_r[dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // Store the value of b (the RHS vector) in r
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Copy the entries of rhs into r
      block_r[dof_type] = block_rhs[dof_type];
    }
 
    // Calculate the norm of the real part of r
    double norm_r = block_r[0].norm();
 
    // Calculate the norm of the imaginary part of r
    double norm_c = block_r[1].norm();
 
    // Compute norm(r)
    double normb = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
    // Set the value of beta (the initial residual)
    double beta = normb;
 
    // Compute the initial relative residual. If the entries of the RHS vector
    // are all zero then set normb equal to one. This is because we divide the
    // value of the norm at later stages by normb and dividing by zero is not
    // definied
    if (normb == 0.0)
    {
      // Set the value of normb
      normb = 1.0;
    }
 
    // Calculate the ratio between the initial norm and the current norm.
    // Since we haven't completed an iteration yet this will simply be one
    // unless normb was zero, in which case resid will have value zero
    resid = beta / normb;
 
    // If required, will document convergence history to screen or file (if
    // stream open)
    if (this->Doc_convergence_history)
    {
      // If an output file which is open isn't provided then output to screen
      if (!(this->Output_file_stream.is_open()))
      {
        // Output the residual value to the screen
        oomph_info << 0 << " " << resid << std::endl;
      }
      // Otherwise, output to file
      else
      {
        // Output the residual value to file
        this->Output_file_stream << 0 << " " << resid << std::endl;
      }
    } // if (Doc_convergence_history)
 
    // If the GMRES algorithm converges immediately
    if (resid <= this->Tolerance)
    {
      // If time documentation is enabled
      if (this->Doc_time)
      {
        // Notify the user
        oomph_info << "FGMRES converged immediately. Normalised residual norm: "
                   << resid << std::endl;
      }
 
      // Finish running the solver
      return;
    } // if (resid<=Tolerance)
 
    // Initialise a vector of orthogonal basis vectors
    Vector<Vector<DoubleVector>> block_v;
 
    // Resize the number of vectors needed
    block_v.resize(n_row + 1);
 
    // Create a vector of DoubleVectors (stores the preconditioned vectors)
    Vector<Vector<DoubleVector>> block_z;
 
    // Resize the number of vectors needed
    block_z.resize(n_row + 1);
 
    // Resize each Vector of DoubleVectors to store the real and imaginary
    // part of a given vector
    for (unsigned i = 0; i < n_row + 1; i++)
    {
      // Create space for two DoubleVector objects in each Vector
      block_v[i].resize(n_dof_types);
 
      // Create space for two DoubleVector objects in each Vector
      block_z[i].resize(n_dof_types);
    }
 
    // Initialise the upper hessenberg matrix. Since we are not using
    // distributed vectors here, the algebra is best done using entries
    // of the type std::complex<double>. NOTE: For implementation purposes
    // the upper Hessenberg matrix indices are swapped so the matrix is
    // effectively transposed
    Vector<Vector<std::complex<double>>> hessenberg(n_row + 1);
 
    // Build the zeroth basis vector
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // Build the k-th part of the zeroth vector. Here k=0 and k=1 correspond
      // to the real and imaginary part of the zeroth vector, respectively
      block_v[0][dof_type].build(this->block_distribution_pt(dof_type), 0.0);
    }
 
    // Loop over the real and imaginary parts of v
    for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
    {
      // For fast access
      double* block_v0_pt = block_v[0][dof_type].values_pt();
 
      // For fast access
      const double* block_r_pt = block_r[dof_type].values_pt();
 
      // Set the zeroth basis vector v[0] to r/beta
      for (unsigned i = 0; i < n_row; i++)
      {
        // Assign the i-th entry of the zeroth basis vector
        block_v0_pt[i] = block_r_pt[i] / beta;
      }
    } // for (unsigned k=0;k<n_dof_types;k++)
 
    // Set the first entry in the minimisation problem RHS vector (is meant
    // to the vector beta*e_1 initially, where e_1 is the unit vector with
    // one in its first entry)
    s[0] = beta;
 
    // Compute the next step of the iterative scheme
    for (unsigned j = 0; j < this->Max_iter; j++)
    {
      // Resize the next column of the upper hessenberg matrix
      hessenberg[j].resize(j + 2, std::complex<double>(0.0, 0.0));
 
      // Calculate w_j=Jz_j where z_j=M^{-1}v_j (RHS prec.)
      {
        // Create a temporary vector
        DoubleVector vj(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Create a temporary vector
        DoubleVector zj(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Create a temporary vector
        DoubleVector w(IterativeLinearSolver::distribution_pt(), 0.0);
 
        // Create two DoubleVectors
        for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
        {
          // Build the k-th part of the j-th preconditioning result vector
          block_z[j][dof_type].build(this->block_distribution_pt(dof_type),
                                     0.0);
        }
 
        // Copy the real and imaginary part of v[j] into one vector, vj
        this->return_block_vectors(block_v[j], vj);
 
        // Calculate z_j=M^{-1}v_j by saad p270
        this->preconditioner_pt()->preconditioner_solve(vj, zj);
 
        // Copy zj into z[j][0] and z[j][1]
        this->get_block_vectors(zj, block_z[j]);
 
        // Calculate w_j=J*(M^{-1}v_j). Note, we cannot use inbuilt complex
        // matrix algebra here as we're using distributed vectors
        this->complex_matrix_multiplication(
          this->Matrices_storage_pt, block_z[j], block_w);
      } // Calculate w=JM^{-1}v (RHS prec.)
 
      // For fast access
      double* block_w_r_pt = block_w[0].values_pt();
 
      // For fast access
      double* block_w_c_pt = block_w[1].values_pt();
 
      // Loop over all of the entries on and above the principal subdiagonal of
      // the Hessenberg matrix in the j-th column (remembering that
      // the indices of the upper Hessenberg matrix are swapped for the purpose
      // of implementation)
      for (unsigned i = 0; i < j + 1; i++)
      {
        // For fast access
        const double* block_vi_r_pt = block_v[i][0].values_pt();
 
        // For fast access
        const double* block_vi_c_pt = block_v[i][1].values_pt();
 
        // Loop over the entries of v and w
        for (unsigned k = 0; k < n_row; k++)
        {
          // Store the appropriate entry in v as a complex value
          std::complex<double> complex_v(block_vi_r_pt[k], block_vi_c_pt[k]);
 
          // Store the appropriate entry in w as a complex value
          std::complex<double> complex_w(block_w_r_pt[k], block_w_c_pt[k]);
 
          // Update the value of H(i,j) noting we're computing a complex
          // inner product here (the ordering is very important here!)
          hessenberg[j][i] += std::conj(complex_v) * complex_w;
        }
 
        // Orthonormalise w against all previous orthogonal vectors, v_i by
        // looping over its entries and updating them
        for (unsigned k = 0; k < n_row; k++)
        {
          // Update the real part of the k-th entry of w
          block_w_r_pt[k] -= (hessenberg[j][i].real() * block_vi_r_pt[k] -
                              hessenberg[j][i].imag() * block_vi_c_pt[k]);
 
          // Update the imaginary part of the k-th entry of w
          block_w_c_pt[k] -= (hessenberg[j][i].real() * block_vi_c_pt[k] +
                              hessenberg[j][i].imag() * block_vi_r_pt[k]);
        }
      } // for (unsigned i=0;i<j+1;i++)
 
      // Calculate the norm of the real part of w
      norm_r = block_w[0].norm();
 
      // Calculate the norm of the imaginary part of w
      norm_c = block_w[1].norm();
 
      // Calculate the norm of the vector w using norm_r and norm_c and assign
      // its value to the appropriate entry in the Hessenberg matrix
      hessenberg[j][j + 1] = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
      // Build the (j+1)-th basis vector
      for (unsigned dof_type = 0; dof_type < n_dof_types; dof_type++)
      {
        // Build the k-th part of the zeroth vector. Here k=0 and k=1 correspond
        // to the real and imaginary part of the zeroth vector, respectively
        block_v[j + 1][dof_type].build(this->block_distribution_pt(dof_type),
                                       0.0);
      }
 
      // Check if the value of hessenberg[j][j+1] is zero. If it
      // isn't then we update the next entries in v
      if (hessenberg[j][j + 1] != 0.0)
      {
        // For fast access
        double* block_v_r_pt = block_v[j + 1][0].values_pt();
 
        // For fast access
        double* block_v_c_pt = block_v[j + 1][1].values_pt();
 
        // For fast access
        const double* block_w_r_pt = block_w[0].values_pt();
 
        // For fast access
        const double* block_w_c_pt = block_w[1].values_pt();
 
        // Notice, the value of H(j,j+1), as calculated above, is clearly a real
        // number. As such, calculating the division
        //                      v_{j+1}=w_{j}/h_{j+1,j},
        // here is simple, i.e. we don't need to worry about cross terms in the
        // algebra. To avoid computing h_{j+1,j} several times we precompute it
        double h_subdiag_val = hessenberg[j][j + 1].real();
 
        // Loop over the entries of the new orthogonal vector and set its values
        for (unsigned k = 0; k < n_row; k++)
        {
          // The i-th entry of the real component is given by
          block_v_r_pt[k] = block_w_r_pt[k] / h_subdiag_val;
 
          // Similarly, the i-th entry of the imaginary component is given by
          block_v_c_pt[k] = block_w_c_pt[k] / h_subdiag_val;
        }
      }
      // Otherwise, we have to jump to the next part of the algorithm; if
      // the value of hessenberg[j][j+1] is zero then the norm of the latest
      // orthogonal vector is zero. This is only possible if the entries
      // in w are all zero. As a result, the Krylov space of A and r_0 has
      // been spanned by the previously calculated orthogonal vectors
      else
      {
        // Book says "Set m=j and jump to step 11" (p.172)...
        // Do something here!
        oomph_info << "Subdiagonal Hessenberg entry is zero. "
                   << "Do something here..." << std::endl;
      } // if (hessenberg[j][j+1]!=0.0)
 
      // Loop over the entries in the Hessenberg matrix and calculate the
      // entries of the Givens rotation matrices
      for (unsigned k = 0; k < j; k++)
      {
        // Apply the plane rotation to all of the previous entries in the
        // (j)-th column (remembering the indexing is reversed)
        this->apply_plane_rotation(
          hessenberg[j][k], hessenberg[j][k + 1], cs[k], sn[k]);
      }
 
      // Now calculate the entries of the latest Givens rotation matrix
      this->generate_plane_rotation(
        hessenberg[j][j], hessenberg[j][j + 1], cs[j], sn[j]);
 
      // Apply the plane rotation using the newly calculated entries
      this->apply_plane_rotation(
        hessenberg[j][j], hessenberg[j][j + 1], cs[j], sn[j]);
 
      // Apply a plane rotation to the corresponding entry in the vector
      // s used in the minimisation problem, J(y)=min||s-R_m*y||
      this->apply_plane_rotation(s[j], s[j + 1], cs[j], sn[j]);
 
      // Compute current residual using equation (6.42) in Saad Y, "Iterative
      // methods for sparse linear systems", p.177.]. Note, since s has complex
      // entries we have to use std::abs instead of std::fabs
      beta = std::abs(s[j + 1]);
 
      // Compute the relative residual
      resid = beta / normb;
 
      // If required will document convergence history to screen or file (if
      // stream open)
      if (this->Doc_convergence_history)
      {
        // If an output file which is open isn't provided then output to screen
        if (!(this->Output_file_stream.is_open()))
        {
          // Output the residual value to the screen
          oomph_info << j + 1 << " " << resid << std::endl;
        }
        // Otherwise, output to file
        else
        {
          // Output the residual value to file
          this->Output_file_stream << j + 1 << " " << resid << std::endl;
        }
      } // if (Doc_convergence_history)
 
      // If the required tolerance has been met
      if (resid < this->Tolerance)
      {
        // Store the number of iterations taken
        this->Iterations = j + 1;
 
        // Update the result vector using the result, x=x_0+V_m*y (where V_m
        // is given by v here)
        update(j, hessenberg, s, block_z, block_solution);
 
        // Copy the vectors in block_solution to solution
        this->return_block_vectors(block_solution, solution);
 
        // If time documentation was enabled
        if (this->Doc_time)
        {
          // Output the current normalised residual norm
          oomph_info << "\nFGMRES converged (1). Normalised residual norm: "
                     << resid << std::endl;
 
          // Output the number of iterations it took for convergence
          oomph_info << "Number of iterations to convergence: " << j + 1 << "\n"
                     << std::endl;
        }
 
        // Stop the timer
        double t_end = TimingHelpers::timer();
 
        // Calculate the time taken to calculate the solution
        this->Solution_time = t_end - t_start;
 
        // If time documentation was enabled
        if (this->Doc_time)
        {
          // Output the time taken to solve the problem using GMRES
          oomph_info << "Time for solve with FGMRES [sec]: "
                     << this->Solution_time << std::endl;
        }
 
        // As we've met the tolerance for the solver and everything that should
        // be documented, has been, finish using the solver
        return;
      } // if (resid<Tolerance)
    } // for (unsigned j=0;j<Max_iter;j++)
 
    // Store the number of iterations taken
    this->Iterations = this->Max_iter;
 
    // Only update if we actually did something
    if (this->Max_iter > 0)
    {
      // Update the result vector using the result, x=x_0+V_m*y (where V_m
      // is given by v here)
      update(this->Max_iter - 1, hessenberg, s, block_z, block_solution);
 
      // Copy the vectors in block_solution to solution
      this->return_block_vectors(block_solution, solution);
    }
 
    // Compute the current residual
    beta = 0.0;
 
    // Get access to the values pointer (real)
    norm_r = block_r[0].norm();
 
    // Get access to the values pointer (imaginary)
    norm_c = block_r[1].norm();
 
    // Calculate the full norm
    beta = sqrt(pow(norm_r, 2.0) + pow(norm_c, 2.0));
 
    // Calculate the relative residual
    resid = beta / normb;
 
    // If the relative residual lies within tolerance
    if (resid < this->Tolerance)
    {
      // If time documentation is enabled
      if (this->Doc_time)
      {
        // Notify the user
        oomph_info << "\nFGMRES converged (2). Normalised residual norm: "
                   << resid << "\nNumber of iterations to convergence: "
                   << this->Iterations << "\n"
                   << std::endl;
      }
 
      // End the timer
      double t_end = TimingHelpers::timer();
 
      // Calculate the time taken for the solver
      this->Solution_time = t_end - t_start;
 
      // If time documentation is enabled
      if (this->Doc_time)
      {
        oomph_info << "Time for solve with FGMRES [sec]: "
                   << this->Solution_time << std::endl;
      }
      return;
    }
 
    // Otherwise GMRES failed convergence
    oomph_info << "\nFGMRES did not converge to required tolerance! "
               << "\nReturning with normalised residual norm: " << resid
               << "\nafter " << this->Max_iter << " iterations.\n"
               << std::endl;
 
    // Throw an error if requested
    if (this->Throw_error_after_max_iter)
    {
      std::string err = "Solver failed to converge and you requested an error";
      err += " on convergence failures.";
      throw OomphLibError(
        err, OOMPH_EXCEPTION_LOCATION, OOMPH_CURRENT_FUNCTION);
    }
 
    // Finish using the solver
    return;
  } // End of solve_helper
} // End of namespace oomph
 
#endif