def test_aspreconditioner(self):
from pyamg import smoothed_aggregation_solver
from scipy.sparse.linalg import cg
from pyamg.krylov import fgmres
A = poisson((50, 50), format='csr')
b = rand(A.shape[0])
ml = smoothed_aggregation_solver(A)
for cycle in ['V', 'W', 'F']:
M = ml.aspreconditioner(cycle=cycle)
x, info = cg(A, b, tol=1e-8, maxiter=30, M=M)
# cg satisfies convergence in the preconditioner norm
assert (precon_norm(b - A * x, ml) < 1e-8 * precon_norm(b, ml))
for cycle in ['AMLI']:
M = ml.aspreconditioner(cycle=cycle)
x, info = fgmres(A, b, tol=1e-8, maxiter=30, M=M)
# fgmres satisfies convergence in the 2-norm
assert (norm(b - A * x) < 1e-8 * norm(b))
def test_aspreconditioner(self):
from pyamg import smoothed_aggregation_solver
from scipy.sparse.linalg import cg
from pyamg.krylov import fgmres
A = poisson((50, 50), format='csr')
b = rand(A.shape[0])
ml = smoothed_aggregation_solver(A)
for cycle in ['V', 'W', 'F']:
M = ml.aspreconditioner(cycle=cycle)
x, info = cg(A, b, tol=1e-8, maxiter=30, M=M)
# cg satisfies convergence in the preconditioner norm
assert(precon_norm(b - A*x, ml) < 1e-8*precon_norm(b, ml))
for cycle in ['AMLI']:
M = ml.aspreconditioner(cycle=cycle)
x, info = fgmres(A, b, tol=1e-8, maxiter=30, M=M)
# fgmres satisfies convergence in the 2-norm
assert(norm(b - A*x) < 1e-8*norm(b))