cg - conjugate gradient algorithm (rheolef-7.2)

Pierre Saramito <Pierre.Saramito@imag.fr>

Copyright (C) 2000-2018 Pierre Saramito <Pierre.Saramito@imag.fr> GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>. This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law. rheolef Version 7.2 cg(5rheolef)

This function solves the symmetricpositivedefinite linear system A*x=b with the conjugate gradient method. The cg function follows the algorithm described on p. 15 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 cg is a preconditionner: here, the eye(5) one is a do-nothing preconditionner, for simplicity. 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 criterion.

solver_option sopt; sopt.max_iter = 100; sopt.tol = 1e-7; int status = cg (A, x, b, eye(), sopt);

This documentation has been generated from file linalg/lib/cg.h The present template implementation is inspired from the IML++ 1.2 iterative method library, http://math.nist.gov/iml++ template <class Matrix, class Vector, class Vector2, class Preconditioner> int cg (const Matrix &A, Vector &x, const Vector2 &Mb, const Preconditioner &M, 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 : "cg"); Vector b = M.solve(Mb); Real norm2_b = dot(Mb,b); if (sopt.absolute_stopping || norm2_b == Real(0)) norm2_b = 1; Vector Mr = Mb - A*x; Real norm2_r = 0; if (sopt.p_err) (*sopt.p_err) << "[" << label << "] #iteration residue" << std::endl; Vector p; for (sopt.n_iter = 0; sopt.n_iter <= sopt.max_iter; sopt.n_iter++) { Vector r = M.solve(Mr); Real prev_norm2_r = norm2_r; norm2_r = dot(Mr, r); sopt.residue = sqrt(norm2_r/norm2_b); if (sopt.p_err) (*sopt.p_err) << "[" << label << "] " << sopt.n_iter << " " << sopt.residue << std::endl; if (sopt.residue <= sopt.tol) return 0; if (sopt.n_iter == 0) { p = r; } else { Real beta = norm2_r/prev_norm2_r; p = r + beta*p; } Vector Mq = A*p; Real alpha = norm2_r/dot(Mq, p); x += alpha*p; Mr -= alpha*Mq; } return 1; }

cg - conjugate gradient algorithm (rheolef-7.2)

template <class Matrix, class Vector, class Vector2, class Preconditioner> int cg (const Matrix &A, Vector &x, const Vector2 &Mb, const Preconditioner &M, const solver_option& sopt = solver_option())