Provided by: librheolef-dev_7.0-3_amd64 
NAME
minres -- conjugate gradient algorithm.
SYNOPSIS
template <class Matrix, class Vector, class Preconditioner, class Real>
int minres (const Matrix &A, Vector &x, const Vector &b, const Preconditioner &M,
const solver_option& sopt = solver_option);
EXAMPLE
The simplest call to minres has the folling form:
solver_option sopt;
sopt.max_iter = 100;
sopt.tol = 1e-7;
int status = minres (a, x, b, eye(), sopt);
DESCRIPTION
minres solves the symmetric positive definite linear system Ax=b using the Conjugate
Gradient method.
The return value indicates convergence within max_iter (input) iterations (0), or no
convergence within max_iter iterations (1). Upon successful return, output arguments have
the following values:
x approximate solution to Ax = b
sopt.n_iter
the number of iterations performed before the tolerance was reached
sopt.residue
the residual after the final iteration
NOTE
minres follows the algorithm described in "Solution of sparse indefinite systems of linear
equations", C. C. Paige and M. A. Saunders, SIAM J. Numer. Anal., 12(4), 1975. For more,
see http://www.stanford.edu/group/SOL/software.html and also the PhD "Iterative methods
for singular linear equations and least-squares problems", S.-C. T. Choi, Stanford
University, 2006, http://www.stanford.edu/group/SOL/dissertations/sou-cheng-choi-
thesis.pdf at page 60. The present implementation style is inspired from IML++ 1.2
iterative method library, http://math.nist.gov/iml++.
IMPLEMENTATION
template <class Matrix, class Vector, class Preconditioner>
int minres (const Matrix &A, Vector &x, const Vector &Mb, const Preconditioner &M,
const solver_option& sopt = solver_option())
{
// Size &max_iter, Real &tol, odiststream *p_derr = 0
typedef typename Vector::size_type Size;
typedef typename Vector::float_type Real;
std::string label = (sopt.label != "" ? sopt.label : "minres");
Vector b = M.solve(Mb);
Real norm_b = sqrt(fabs(dot(Mb,b)));
if (sopt.absolute_stopping || norm_b == Real(0.)) norm_b = 1;
Vector Mr = Mb - A*x;
Vector z = M.solve(Mr);
Real beta2 = dot(Mr, z);
Real norm_r = sqrt(fabs(beta2));
sopt.residue = norm_r/norm_b;
if (sopt.p_err) (*sopt.p_err) << "[" << label << "] #iteration residue" << std::endl
<< "[" << label << "] 0 " << sopt.residue << std::endl;
if (beta2 < 0 || sopt.residue <= sopt.tol) {
dis_warning_macro ("beta2 = " << beta2 << " < 0: stop");
return 0;
}
Real beta = sqrt(beta2);
Real eta = beta;
Vector Mv = Mr/beta;
Vector u = z/beta;
Real c_old = 1.;
Real s_old = 0.;
Real c = 1.;
Real s = 0.;
Vector u_old (x.ownership(), 0.);
Vector Mv_old (x.ownership(), 0.);
Vector w (x.ownership(), 0.);
Vector w_old (x.ownership(), 0.);
Vector w_old2 (x.ownership(), 0.);
for (sopt.n_iter = 1; sopt.n_iter <= sopt.max_iter; sopt.n_iter++) {
// Lanczos
Mr = A*u;
z = M.solve(Mr);
Real alpha = dot(Mr, u);
Mr = Mr - alpha*Mv - beta*Mv_old;
z = z - alpha*u - beta*u_old;
beta2 = dot(Mr, z);
if (beta2 < 0) {
dis_warning_macro ("minres: machine precision problem");
sopt.residue = norm_r/norm_b;
return 2;
}
Real beta_old = beta;
beta = sqrt(beta2);
// QR factorisation
Real c_old2 = c_old;
Real s_old2 = s_old;
c_old = c;
s_old = s;
Real rho0 = c_old*alpha - c_old2*s_old*beta_old;
Real rho2 = s_old*alpha + c_old2*c_old*beta_old;
Real rho1 = sqrt(sqr(rho0) + sqr(beta));
Real rho3 = s_old2 * beta_old;
// Givens rotation
c = rho0 / rho1;
s = beta / rho1;
// update
w_old2 = w_old;
w_old = w;
w = (u - rho2*w_old - rho3*w_old2)/rho1;
x += c*eta*w;
eta = -s*eta;
Mv_old = Mv;
u_old = u;
Mv = Mr/beta;
u = z/beta;
// check residue
norm_r *= s;
sopt.residue = norm_r/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;
}
COPYRIGHT
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.