Provided by: librheolef-dev_7.2-2_amd64 bug


       gmres - generalized minimum residual algorithm (rheolef-7.2)


       template <class Matrix, class Vector, class Preconditioner,
                 class SmallMatrix, class SmallVector>
       int gmres (const Matrix &A, Vector &x, const Vector &b, const Preconditioner &M,
                  SmallMatrix &H, const SmallVector& V, const solver_option& sopt = solver_option())


           solver_option sopt;
           sopt.tol = 1e-7;
           sopt.max_iter = 100;
           size_t m = sopt.krylov_dimension = 6;
           Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic> H(m+1,m+1);
           Eigen::Matrix<T,Eigen::Dynamic,1>              V(m);
           int status = gmres (A, x, b, ilut(a), H, V, sopt);


       This function solves the unsymmetric linear system A*x=b with the generalized minimum
       residual algorithm. The gmres function follows the algorithm described on p. 20 in

           R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato,
           J. Dongarra, V. Eijkhout, R. Pozo, C. Romine and H. Van der Vorst,
           Templates for the solution of linear systems: building blocks for iterative methods,
           SIAM, 1994.

        The fourth argument of gmres is a preconditionner: here, the ilut(5) one is used, for

       Next, H specifies a matrix to hold the coefficients of the upper Hessenberg matrix
       constructed by the gmres iterations, m specifies the number of iterations for each
       restart. We have used here the eigen dense matrix and vector types for the H and V
       vectors, with sizes related to the Krylov space dimension m. Finally, the solver_option(4)
       variable sopt transmits the stopping criterion with sopt.tol and sopt.max_iter.

       On return, the sopt.residue and sopt.n_iter indicate the reached residue and the number of
       iterations effectively performed. The return status is zero when the prescribed tolerance
       tol has been obtained, and non-zero otherwise. Also, the x variable contains the
       approximate solution. See also the solver_option(4) for more controls upon the stopping


       gmres requires two matrices as input: A and H. The A matrix, which will typically be
       sparse, corresponds to the matrix involved in the linear system A*x=b. Conversely, the H
       matrix, which will typically be dense, corresponds to the upper Hessenberg matrix that is
       constructed during the gmres iterations. Within gmres, H is used in a different way than
       A, so its class must supply different functionality. That is, A is only accessed though
       its matrix-vector and transpose-matrix-vector multiplication functions. On the other hand,
       gmres solves a dense upper triangular linear system of equations on H. Therefore, the
       class to which H belongs must provide H(i,j) accessors.

       It is important to remember that we use the convention that indices are 0-based. That is
       H(0,0) is the first component of the matrix. Also, the type of the matrix must be
       compatible with the type of single vector entry. That is, operations such as H(i,j)*x(j)
       must be able to be carried out.


       This documentation has been generated from file linalg/lib/gmres.h

       The present template implementation is inspired from the IML++ 1.2 iterative method

       template <class Matrix, class Vector, class Preconditioner,
                 class SmallMatrix, class SmallVector>
       int gmres (const Matrix &A, Vector &x, const Vector &b, const Preconditioner &M,
                  SmallMatrix &H, const SmallVector& V, const solver_option& sopt = solver_option())

         typedef typename Vector::size_type  Size;
         typedef typename Vector::float_type Real;
         std::string label = (sopt.label != "" ? sopt.label : "gmres");
         Size m = sopt.krylov_dimension;
         Vector w;
         SmallVector s(m+1), cs(m+1), sn(m+1);
         Real residue;
         Real norm_b = norm(M.solve(b));
         Vector r = M.solve(b - A * x);
         Real beta = norm(r);
         if (sopt.p_err) (*sopt.p_err) << "[" << label << "] # norm_b=" << norm_b << std::endl
                                       << "[" << label << "] #iteration residue" << std::endl;
         if (sopt.absolute_stopping || norm_b == Real(0)) norm_b = 1;
         sopt.n_iter  = 0;
         sopt.residue = norm(r)/norm_b;
         if (sopt.residue <= sopt.tol) return 0;
         std::vector<Vector> v (m+1);
         for (sopt.n_iter = 1; sopt.n_iter <= sopt.max_iter; ) {
           v[0] = r/beta;
           for (Size i = 0; i < m+1; i++) s(i) = 0; // std::numeric_limits<Float>::max();
           s(0) = beta;
           for (Size i = 0; i < m && sopt.n_iter <= sopt.max_iter; i++, sopt.n_iter++) {
             w = M.solve(A * v[i]);
             for (Size k = 0; k <= i; k++) {
               H(k, i) = dot(w, v[k]);
               w -= H(k, i) * v[k];
             H(i+1, i) = norm(w);
             v[i+1] = w/H(i+1,i);
             for (Size k = 0; k < i; k++) {
               details::apply_plane_rotation (H(k,i), H(k+1,i), cs(k), sn(k));
             details::generate_plane_rotation (H(i,i), H(i+1,i), cs(i), sn(i));
             details::apply_plane_rotation (H(i,i), H(i+1,i), cs(i), sn(i));
             details::apply_plane_rotation (s(i), s(i+1), cs(i), sn(i));
             sopt.residue = abs(s(i+1))/norm_b;
             if (sopt.p_err) (*sopt.p_err) << "[" << label << "] " << sopt.n_iter << " " << sopt.residue << std::endl;
             if (sopt.residue <= sopt.tol) {
               details::update (x, i, H, s, v);
               return 0;
           details::update (x, m - 1, H, s, v);
           r = M.solve(b - A * x);
           beta = norm(r);
           sopt.residue = beta/norm_b;
           if (sopt.p_err) (*sopt.p_err) << "[" << label << "] " << sopt.n_iter << " " << sopt.residue << std::endl;
           if (sopt.residue < sopt.tol) return 0;
         return 1;

       template <class SmallMatrix, class SmallVector, class Vector, class Vector2, class Size>
       void update (Vector& x, Size k, const SmallMatrix& h, const SmallVector& s, Vector2& v) {
         SmallVector y = s;
         // back solve:
         for (int i = k; i >= 0; i--) {
           y(i) /= h(i,i);
           for (int j = i - 1; j >= 0; j--)
             y(j) -= h(j,i) * y(i);
         for (Size j = 0; j <= k; j++) {
           x += v[j] * y(j);
       template<class Real>
       void generate_plane_rotation (const Real& dx, const Real& dy, Real& cs, Real& sn) {
         if (dy == Real(0)) {
           cs = 1.0;
           sn = 0.0;
         } else if (abs(dy) > abs(dx)) {
           Real temp = dx / dy;
           sn = 1.0 / sqrt( 1.0 + temp*temp );
           cs = temp * sn;
         } else {
           Real temp = dy / dx;
           cs = 1.0 / sqrt( 1.0 + temp*temp );
           sn = temp * cs;
       template<class Real>
       void apply_plane_rotation (Real& dx, Real& dy, const Real& cs, const Real& sn) {
         Real temp  =  cs * dx + sn * dy;
         dy = -sn * dx + cs * dy;
         dx = temp;


       Pierre  Saramito  <>


       Copyright   (C)  2000-2018  Pierre  Saramito  <> GPLv3+: GNU GPL
       version 3 or later  <>.  This  is  free  software:  you
       are free to change and redistribute it.  There is NO WARRANTY, to the extent permitted by