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//*****************************************************************
// Iterative template routine -- CG
//
// CG solves the symmetric positive definite linear
// system Ax=b using the Conjugate Gradient method.
//
// CG follows the algorithm described on p. 15 in the
// SIAM Templates book.
//
// 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
// max_iter -- the number of iterations performed before the
// tolerance was reached
// tol -- the residual after the final iteration
//
//*****************************************************************
template < class Matrix, class Vector, class Preconditioner, class Real >
int CG(const Matrix &A, Vector &x, const Vector &b, const Preconditioner &M, int &max_iter, Real &tol)
{
Real resid;
Vector p, z, q;
Vector alpha(1), beta(1), rho(1), rho_1(1);
Real normb = norm(b);
Vector r = b - A*x;
if (normb == 0.0)
normb = 1;
if ((resid = norm(r) / normb) <= tol) {
tol = resid;
max_iter = 0;
return 0;
}
for (int i = 1; i <= max_iter; i++) {
z = M.solve(r);
rho(0) = dot(r, z);
if (i == 1)
p = z;
else {
beta(0) = rho(0) / rho_1(0);
p = z + beta(0) * p;
}
q = A*p;
alpha(0) = rho(0) / dot(p, q);
x += alpha(0) * p;
r -= alpha(0) * q;
if ((resid = norm(r) / normb) <= tol) {
tol = resid;
max_iter = i;
return 0;
}
rho_1(0) = rho(0);
}
tol = resid;
return 1;
}
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