def test_coarse_solver_opts(self):
# these tests are meant to test whether coarse solvers are correctly
# passed parameters
A = poisson((30, 30), format='csr')
b = rand(A.shape[0], 1)
# for each pair, the first entry should yield an SA solver that
# converges in fewer iterations for a basic Poisson problem
coarse_solver_pairs = [(('jacobi', {'iterations': 30}), 'jacobi')]
coarse_solver_pairs.append((('gauss_seidel', {'iterations': 30}),
'gauss_seidel'))
coarse_solver_pairs.append(('gauss_seidel', 'jacobi'))
coarse_solver_pairs.append(('cg', ('cg', {'tol': 10.0})))
coarse_solver_pairs.append(('pinv2', ('pinv2', {'cond': 1.0})))
for coarse1, coarse2 in coarse_solver_pairs:
r1 = []
r2 = []
sa1 = smoothed_aggregation_solver(A, coarse_solver=coarse1)
sa2 = smoothed_aggregation_solver(A, coarse_solver=coarse2)
x1 = sa1.solve(b, residuals=r1)
x2 = sa2.solve(b, residuals=r2)
del x1, x2
assert((len(r1) + 5) < len(r2))
def test_nonhermitian(self):
# problem data
data = load_example('helmholtz_2D')
A = data['A'].tocsr()
B = data['B']
numpy.random.seed(625)
x0 = scipy.rand(A.shape[0]) + 1.0j * scipy.rand(A.shape[0])
b = A * scipy.rand(A.shape[0]) + 1.0j * (A * scipy.rand(A.shape[0]))
# solver parameters
smooth = ('energy', {'krylov': 'gmres'})
SA_build_args = {'max_coarse': 25, 'coarse_solver': 'pinv2',
'symmetry': 'symmetric'}
SA_solve_args = {'cycle': 'V', 'maxiter': 20, 'tol': 1e-8}
strength = [('evolution', {'k': 2, 'epsilon': 2.0})]
smoother = ('gauss_seidel_nr', {'sweep': 'symmetric', 'iterations': 1})
# Construct solver with nonsymmetric parameters
sa = smoothed_aggregation_solver(A, B=B, smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
**SA_build_args)
residuals = []
# stand-alone solve
x = sa.solve(b, x0=x0, residuals=residuals, **SA_solve_args)
residuals = array(residuals)
avg_convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert(avg_convergence_ratio < 0.85)
# accelerated solve
residuals = []
x = sa.solve(b, x0=x0, residuals=residuals, accel='gmres',
**SA_solve_args)
del x
residuals = array(residuals)
avg_convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert(avg_convergence_ratio < 0.6)
# test that nonsymmetric parameters give the same result as symmetric
# parameters for the complex-symmetric matrix A
strength = 'symmetric'
SA_build_args['symmetry'] = 'nonsymmetric'
sa_nonsymm = smoothed_aggregation_solver(A, B=ones((A.shape[0], 1)),
smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
improve_candidates=None,
**SA_build_args)
SA_build_args['symmetry'] = 'symmetric'
sa_symm = smoothed_aggregation_solver(A, B=ones((A.shape[0], 1)),
smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
improve_candidates=None,
**SA_build_args)
for (symm_lvl, nonsymm_lvl) in zip(sa_nonsymm.levels, sa_symm.levels):
assert_array_almost_equal(symm_lvl.A.todense(),
nonsymm_lvl.A.todense())
def test_nonhermitian(self):
# problem data
data = load_example("helmholtz_2D")
A = data["A"].tocsr()
B = data["B"]
numpy.random.seed(625)
x0 = scipy.rand(A.shape[0]) + 1.0j * scipy.rand(A.shape[0])
b = A * scipy.rand(A.shape[0]) + 1.0j * (A * scipy.rand(A.shape[0]))
# solver parameters
smooth = ("energy", {"krylov": "gmres"})
SA_build_args = {"max_coarse": 25, "coarse_solver": "pinv2", "symmetry": "symmetric"}
SA_solve_args = {"cycle": "V", "maxiter": 20, "tol": 1e-8}
strength = [("evolution", {"k": 2, "epsilon": 2.0})]
smoother = ("gauss_seidel_nr", {"sweep": "symmetric", "iterations": 1})
# Construct solver with nonsymmetric parameters
sa = smoothed_aggregation_solver(
A, B=B, smooth=smooth, strength=strength, presmoother=smoother, postsmoother=smoother, **SA_build_args
)
residuals = []
# stand-alone solve
x = sa.solve(b, x0=x0, residuals=residuals, **SA_solve_args)
residuals = array(residuals)
avg_convergence_ratio = (residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert avg_convergence_ratio < 0.85
# accelerated solve
residuals = []
x = sa.solve(b, x0=x0, residuals=residuals, accel="gmres", **SA_solve_args)
residuals = array(residuals)
avg_convergence_ratio = (residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert avg_convergence_ratio < 0.6
# test that nonsymmetric parameters give the same result as symmetric
# parameters for the complex-symmetric matrix A
strength = "symmetric"
SA_build_args["symmetry"] = "nonsymmetric"
sa_nonsymm = smoothed_aggregation_solver(
A,
B=ones((A.shape[0], 1)),
smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
improve_candidates=None,
**SA_build_args
)
SA_build_args["symmetry"] = "symmetric"
sa_symm = smoothed_aggregation_solver(
A,
B=ones((A.shape[0], 1)),
smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
improve_candidates=None,
**SA_build_args
)
for (symm_lvl, nonsymm_lvl) in zip(sa_nonsymm.levels, sa_symm.levels):
assert_array_almost_equal(symm_lvl.A.todense(), nonsymm_lvl.A.todense())
def test_matrix_formats(self):
# Do dense, csr, bsr and csc versions of A all yield the same solver
A = poisson((7, 7), format="csr")
cases = [A.tobsr(blocksize=(1, 1))]
cases.append(A.tocsc())
cases.append(A.todense())
sa_old = smoothed_aggregation_solver(A, max_coarse=10)
for AA in cases:
sa_new = smoothed_aggregation_solver(AA, max_coarse=10)
assert abs(ravel(sa_old.levels[-1].A.todense() - sa_new.levels[-1].A.todense())).max() < 0.01
sa_old = sa_new
def test_improve_candidates(self):
# test improve_candidates for the Poisson problem and elasticity, where
# rho_scale is the amount that each successive improve_candidates
# option should improve convergence over the previous
# improve_candidates option.
improve_candidates_list = [None, [("block_gauss_seidel", {"iterations": 4, "sweep": "symmetric"})]]
# make tests repeatable
numpy.random.seed(0)
cases = []
A_elas, B_elas = linear_elasticity((60, 60), format="bsr")
# Matrix, Candidates, rho_scale
cases.append((poisson((61, 61), format="csr"), ones((61 * 61, 1)), 0.9))
cases.append((A_elas, B_elas, 0.9))
for (A, B, rho_scale) in cases:
last_rho = -1.0
x0 = rand(A.shape[0], 1)
b = rand(A.shape[0], 1)
for ic in improve_candidates_list:
ml = smoothed_aggregation_solver(A, B, max_coarse=10, improve_candidates=ic)
residuals = []
x_sol = ml.solve(b, x0=x0, maxiter=20, tol=1e-10, residuals=residuals)
rho = (residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
if last_rho == -1.0:
last_rho = rho
else:
# each successive improve_candidates option should be an
# improvement on the previous print "\nimprove_candidates
# Test: %1.3e, %1.3e,
# %d\n"%(rho,rho_scale*last_rho,A.shape[0])
assert rho < rho_scale * last_rho
last_rho = rho
def test_symmetry(self):
# Test that a basic V-cycle yields a symmetric linear operator. Common
# reasons for failure are problems with using the same rho for the
# pres/post-smoothers and using the same block_D_inv for
# pre/post-smoothers.
n = 500
A = poisson((n,), format='csr')
smoothers = [('gauss_seidel', {'sweep': 'symmetric'}),
('schwarz', {'sweep': 'symmetric'}),
('block_gauss_seidel', {'sweep': 'symmetric'}),
'jacobi', 'block_jacobi']
rng = np.arange(1, n + 1, dtype='float').reshape(-1, 1)
Bs = [np.ones((n, 1)), sp.hstack((np.ones((n, 1)), rng))]
# TODO:
# why does python 3 require significant=6 while python 2 passes
# why does python 3 yield a different dot() below than python 2
# only for: ('gauss_seidel', {'sweep': 'symmetric'})
for smoother in smoothers:
for B in Bs:
ml = smoothed_aggregation_solver(A, B, max_coarse=10,
presmoother=smoother,
postsmoother=smoother)
P = ml.aspreconditioner()
np.random.seed(0)
x = sp.rand(n,)
y = sp.rand(n,)
out = (np.dot(P * x, y), np.dot(x, P * y))
# print("smoother = %s %g %g" % (smoother, out[0], out[1]))
assert_approx_equal(out[0], out[1])
def test_symmetry(self):
# Test that a basic V-cycle yields a symmetric linear operator. Common
# reasons for failure are problems with using the same rho for the
# pres/post-smoothers and using the same block_D_inv for
# pre/post-smoothers.
n = 500
A = poisson((n,), format="csr")
smoothers = [
("gauss_seidel", {"sweep": "symmetric"}),
("schwarz", {"sweep": "symmetric"}),
("block_gauss_seidel", {"sweep": "symmetric"}),
"jacobi",
"block_jacobi",
]
rng = arange(1, n + 1, dtype="float").reshape(-1, 1)
Bs = [ones((n, 1)), hstack((ones((n, 1)), rng))]
for smoother in smoothers:
for B in Bs:
ml = smoothed_aggregation_solver(A, B, max_coarse=10, presmoother=smoother, postsmoother=smoother)
P = ml.aspreconditioner()
x = rand(n)
y = rand(n)
assert_approx_equal(dot(P * x, y), dot(x, P * y))
def test_DAD(self):
A = poisson((50, 50), format='csr')
x = rand(A.shape[0])
b = rand(A.shape[0])
D = diag_sparse(1.0 / sqrt(10 ** (12 * rand(A.shape[0]) - 6))).tocsr()
D_inv = diag_sparse(1.0 / D.data)
# DAD = D * A * D
B = ones((A.shape[0], 1))
# TODO force 2 level method and check that result is the same
kwargs = {'max_coarse': 1, 'max_levels': 2, 'coarse_solver': 'splu'}
sa = smoothed_aggregation_solver(D * A * D, D_inv * B, **kwargs)
residuals = []
x_sol = sa.solve(b, x0=x, maxiter=10, tol=1e-12, residuals=residuals)
del x_sol
avg_convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
# print "Diagonal Scaling Test: %1.3e, %1.3e" %
# (avg_convergence_ratio, 0.25)
assert(avg_convergence_ratio < 0.25)
def test_basic(self):
"""check that method converges at a reasonable rate"""
for A, B, c_factor, symmetry, smooth in self.cases:
A = csr_matrix(A)
ml = smoothed_aggregation_solver(A, B, symmetry=symmetry,
smooth=smooth, max_coarse=10)
numpy.random.seed(0) # make tests repeatable
x = rand(A.shape[0]) + 1.0j * rand(A.shape[0])
b = A * rand(A.shape[0])
residuals = []
x_sol = ml.solve(b, x0=x, maxiter=20, tol=1e-10,
residuals=residuals)
del x_sol
avg_convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
# print "Complex Test: %1.3e, %1.3e, %d, %1.3e" % \
# (avg_convergence_ratio, c_factor,
# len(ml.levels), ml.operator_complexity())
assert(avg_convergence_ratio < c_factor)
def run_cases(self, opts):
for A, B in self.cases:
ml = smoothed_aggregation_solver(A, B, max_coarse=5, **opts)
numpy.random.seed(0) # make tests repeatable
x = rand(A.shape[0])
b = A * rand(A.shape[0])
residuals = []
x_sol = ml.solve(b, x0=x, maxiter=30, tol=1e-10, residuals=residuals)
convergence_ratio = (residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert convergence_ratio < 0.9
def test_coarse_solver_opts(self):
# these tests are meant to test whether coarse solvers are correctly
# passed parameters
A = poisson((30, 30), format="csr")
b = rand(A.shape[0], 1)
# for each pair, the first entry should yield an SA solver that
# converges in fewer iterations for a basic Poisson problem
coarse_solver_pairs = [(("jacobi", {"iterations": 30}), "jacobi")]
coarse_solver_pairs.append((("gauss_seidel", {"iterations": 30}), "gauss_seidel"))
coarse_solver_pairs.append(("gauss_seidel", "jacobi"))
coarse_solver_pairs.append(("cg", ("cg", {"tol": 10.0})))
coarse_solver_pairs.append(("pinv2", ("pinv2", {"cond": 1.0})))
for coarse1, coarse2 in coarse_solver_pairs:
r1 = []
r2 = []
sa1 = smoothed_aggregation_solver(A, coarse_solver=coarse1)
sa2 = smoothed_aggregation_solver(A, coarse_solver=coarse2)
x1 = sa1.solve(b, residuals=r1)
x2 = sa2.solve(b, residuals=r2)
assert (len(r1) + 5) < len(r2)
def test_improve_candidates(self):
# test improve_candidates for the Poisson problem and elasticity, where
# rho_scale is the amount that each successive improve_candidates
# option should improve convergence over the previous
# improve_candidates option.
improve_candidates_list = [
None,
[('block_gauss_seidel', {
'iterations': 4,
'sweep': 'symmetric'
})]
]
# make tests repeatable
numpy.random.seed(0)
cases = []
A_elas, B_elas = linear_elasticity((60, 60), format='bsr')
# Matrix, Candidates, rho_scale
cases.append((poisson((61, 61), format='csr'), ones(
(61 * 61, 1)), 0.9))
cases.append((A_elas, B_elas, 0.9))
for (A, B, rho_scale) in cases:
last_rho = -1.0
x0 = rand(A.shape[0], 1)
b = rand(A.shape[0], 1)
for ic in improve_candidates_list:
ml = smoothed_aggregation_solver(A,
B,
max_coarse=10,
improve_candidates=ic)
residuals = []
x_sol = ml.solve(b,
x0=x0,
maxiter=20,
tol=1e-10,
residuals=residuals)
del x_sol
rho = (residuals[-1] / residuals[0])**(1.0 / len(residuals))
if last_rho == -1.0:
last_rho = rho
else:
# each successive improve_candidates option should be an
# improvement on the previous print "\nimprove_candidates
# Test: %1.3e, %1.3e,
# %d\n"%(rho,rho_scale*last_rho,A.shape[0])
assert (rho < rho_scale * last_rho)
last_rho = rho
def run_cases(self, opts):
for A, B in self.cases:
ml = smoothed_aggregation_solver(A, B, max_coarse=5, **opts)
np.random.seed(1883275855) # make tests repeatable
x = np.random.rand(A.shape[0])
b = A * np.random.rand(A.shape[0])
residuals = []
x_sol = ml.solve(b, x0=x, maxiter=30, tol=1e-10,
residuals=residuals)
del x_sol
convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert(convergence_ratio < 0.9)
def test_symmetry(self):
# Test that a basic V-cycle yields a symmetric linear operator. Common
# reasons for failure are problems with using the same rho for the
# pres/post-smoothers and using the same block_D_inv for
# pre/post-smoothers.
n = 500
A = poisson((n, ), format='csr')
smoothers = [('gauss_seidel',{'sweep':'symmetric'}), \
('schwarz',{'sweep':'symmetric'}), \
('block_gauss_seidel',{'sweep':'symmetric'}), \
'jacobi', 'block_jacobi']
Bs = [ones((n,1)), \
hstack( (ones((n,1)), arange(1,n+1,dtype='float').reshape(-1,1)) ) ]
for smoother in smoothers:
for B in Bs:
ml = smoothed_aggregation_solver(A, B, max_coarse=10, \
presmoother=smoother, postsmoother=smoother)
P = ml.aspreconditioner()
x = rand(n, )
y = rand(n, )
assert_approx_equal(dot(P * x, y), dot(x, P * y))
def test_basic(self):
"""Check that method converges at a reasonable rate."""
for A, B, c_factor, symmetry, smooth in self.cases:
ml = smoothed_aggregation_solver(A, B, symmetry=symmetry,
smooth=smooth, max_coarse=10)
np.random.seed(3009521727) # make tests repeatable
x = np.random.rand(A.shape[0])
b = A * np.random.rand(A.shape[0])
residuals = []
x_sol = ml.solve(b, x0=x, maxiter=20, tol=1e-10,
residuals=residuals)
del x_sol
avg_convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
# print "Real Test: %1.3e, %1.3e, %d, %1.3e" % \
# (avg_convergence_ratio, c_factor, len(ml.levels),
# ml.operator_complexity())
assert(avg_convergence_ratio < c_factor)
def test_symmetry(self):
# Test that a basic V-cycle yields a symmetric linear operator. Common
# reasons for failure are problems with using the same rho for the
# pres/post-smoothers and using the same block_D_inv for
# pre/post-smoothers.
n = 500
A = poisson( (n,), format='csr')
smoothers = [('gauss_seidel',{'sweep':'symmetric'}), \
('schwarz',{'sweep':'symmetric'}), \
('block_gauss_seidel',{'sweep':'symmetric'}), \
'jacobi', 'block_jacobi']
Bs = [ones((n,1)), \
hstack( (ones((n,1)), arange(1,n+1,dtype='float').reshape(-1,1)) ) ]
for smoother in smoothers:
for B in Bs:
ml = smoothed_aggregation_solver(A, B, max_coarse=10, \
presmoother=smoother, postsmoother=smoother)
P = ml.aspreconditioner()
x = rand(n,)
y = rand(n,)
assert_approx_equal( dot(P*x, y), dot(x, P*y) )
def test_symmetry(self):
# Test that a basic V-cycle yields a symmetric linear operator. Common
# reasons for failure are problems with using the same rho for the
# pres/post-smoothers and using the same block_D_inv for
# pre/post-smoothers.
n = 500
A = poisson((n, ), format='csr')
smoothers = [('gauss_seidel', {
'sweep': 'symmetric'
}), ('schwarz', {
'sweep': 'symmetric'
}), ('block_gauss_seidel', {
'sweep': 'symmetric'
}), 'jacobi', 'block_jacobi']
rng = np.arange(1, n + 1, dtype='float').reshape(-1, 1)
Bs = [np.ones((n, 1)), sp.hstack((np.ones((n, 1)), rng))]
# TODO:
# why does python 3 require significant=6 while python 2 passes
# why does python 3 yield a different dot() below than python 2
# only for: ('gauss_seidel', {'sweep': 'symmetric'})
for smoother in smoothers:
for B in Bs:
ml = smoothed_aggregation_solver(A,
B,
max_coarse=10,
presmoother=smoother,
postsmoother=smoother)
P = ml.aspreconditioner()
np.random.seed(0)
x = sp.rand(n, )
y = sp.rand(n, )
out = (np.dot(P * x, y), np.dot(x, P * y))
# print("smoother = %s %g %g" % (smoother, out[0], out[1]))
assert_approx_equal(out[0], out[1])
def test_nonsymmetric(self):
# problem data
data = load_example('recirc_flow')
A = data['A'].tocsr()
B = data['B']
numpy.random.seed(625)
x0 = scipy.rand(A.shape[0])
b = A * scipy.rand(A.shape[0])
# solver parameters
smooth = ('energy', {'krylov': 'gmres'})
SA_build_args = {
'max_coarse': 25,
'coarse_solver': 'pinv2',
'symmetry': 'nonsymmetric'
}
SA_solve_args = {'cycle': 'V', 'maxiter': 20, 'tol': 1e-8}
strength = [('evolution', {'k': 2, 'epsilon': 8.0})]
smoother = ('gauss_seidel_nr', {'sweep': 'symmetric', 'iterations': 1})
improve_candidates = [('gauss_seidel_nr', {
'sweep': 'symmetric',
'iterations': 4
}), None]
# Construct solver with nonsymmetric parameters
sa = smoothed_aggregation_solver(A,
B=B,
smooth=smooth,
improve_candidates=improve_candidates,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
**SA_build_args)
residuals = []
# stand-alone solve
x = sa.solve(b, x0=x0, residuals=residuals, **SA_solve_args)
residuals = array(residuals)
avg_convergence_ratio = (residuals[-1] /
residuals[0])**(1.0 / len(residuals))
assert (avg_convergence_ratio < 0.65)
# accelerated solve
residuals = []
x = sa.solve(b,
x0=x0,
residuals=residuals,
accel='gmres',
**SA_solve_args)
residuals = array(residuals)
avg_convergence_ratio = (residuals[-1] /
residuals[0])**(1.0 / len(residuals))
assert (avg_convergence_ratio < 0.45)
# test that nonsymmetric parameters give the same result as symmetric parameters
# for Poisson problem
A = poisson((15, 15), format='csr')
strength = 'symmetric'
SA_build_args['symmetry'] = 'nonsymmetric'
sa_nonsymm = smoothed_aggregation_solver(A, B=ones((A.shape[0],1)), smooth=smooth, \
strength=strength, presmoother=smoother, postsmoother=smoother, improve_candidates=None,**SA_build_args)
SA_build_args['symmetry'] = 'symmetric'
sa_symm = smoothed_aggregation_solver(A, B=ones((A.shape[0],1)), smooth=smooth, \
strength=strength, presmoother=smoother, postsmoother=smoother, improve_candidates=None,**SA_build_args)
for (symm_lvl, nonsymm_lvl) in zip(sa_nonsymm.levels, sa_symm.levels):
assert_array_almost_equal(symm_lvl.A.todense(),
nonsymm_lvl.A.todense())
else:
x0 = np.zeros(vec_size, 1)
# ----------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------- #
# Classical SA solver
# -------------------
start = time.clock()
ml_sa = smoothed_aggregation_solver(A,
B=bad_guy,
strength=strength_connection,
aggregate=aggregation,
smooth=interp_smooth,
max_levels=max_levels,
max_coarse=max_coarse,
presmoother=relaxation,
postsmoother=relaxation,
improve_candidates=improve_candidates,
coarse_solver=coarse_solver,
keep=keep_levels)
sa_sol = ml_sa.solve(b, x0, tol, residuals=sa_residuals)
end = time.clock()
sa_time = end - start
sa_conv_factors = np.zeros((len(sa_residuals) - 1, 1))
for i in range(0, len(sa_residuals) - 1):
sa_conv_factors[i] = sa_residuals[i] / sa_residuals[i - 1]
sa_eff_conv = (sa_residuals[-1] / sa_residuals[0])**(
1 / (ml_sa.cycle_complexity() * len(sa_residuals)))
def adaptive_pairwise_solver(A,
initial_targets=None,
symmetry='hermitian',
desired_convergence=0.5,
test_iterations=10,
test_cycle='V',
test_accel=None,
strength=None,
smooth=None,
aggregate=('drake', {
'levels': 2
}),
presmoother=('block_gauss_seidel', {
'sweep': 'symmetric'
}),
postsmoother=('block_gauss_seidel', {
'sweep': 'symmetric'
}),
max_levels=30,
max_coarse=100,
diagonal_dominance=False,
coarse_solver='pinv',
keep=False,
additive=False,
reconstruct=False,
max_hierarchies=10,
use_ritz=False,
improve_candidates=[('block_gauss_seidel', {
'sweep': 'symmetric',
'iterations': 4
})],
**kwargs):
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
elif v is None:
return None
else:
return v, {}
if isspmatrix_bsr(A):
warn("Only currently implemented for CSR matrices.")
if not (isspmatrix_csr(A) or isspmatrix_bsr(A)):
try:
A = csr_matrix(A)
warn("Implicit conversion of A to CSR", SparseEfficiencyWarning)
except:
raise TypeError('Argument A must have type csr_matrix or\
bsr_matrix, or be convertible to csr_matrix')
if (symmetry != 'symmetric') and (symmetry != 'hermitian') and\
(symmetry != 'nonsymmetric'):
raise ValueError('expected \'symmetric\', \'nonsymmetric\' or\
\'hermitian\' for the symmetry parameter ')
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
A = A.asfptype()
A.symmetry = symmetry
n = A.shape[0]
test_rhs = np.zeros((n, 1))
# SHOULD I START WITH CONSTANT VECTOR OR SMOOTHED RANDOM VECTOR?
# Right near nullspace candidates
if initial_targets is None:
initial_targets = np.kron(
np.ones((A.shape[0] / blocksize(A), 1), dtype=A.dtype),
np.eye(blocksize(A)))
else:
initial_targets = np.asarray(initial_targets, dtype=A.dtype)
if len(initial_targets.shape) == 1:
initial_targets = initial_targets.reshape(-1, 1)
if initial_targets.shape[0] != A.shape[0]:
raise ValueError(
'The near null-space modes initial_targets have incorrect \
dimensions for matrix A')
if initial_targets.shape[1] < blocksize(A):
raise ValueError(
'initial_targets.shape[1] must be >= the blocksize of A')
# Improve near nullspace candidates by relaxing on A B = 0
if improve_candidates is not None:
fn, temp_args = unpack_arg(improve_candidates[0])
else:
fn = None
if fn is not None:
b = np.zeros((A.shape[0], 1), dtype=A.dtype)
initial_targets = relaxation_as_linear_operator(
(fn, temp_args), A, b) * initial_targets
if A.symmetry == "nonsymmetric":
AH = A.H.asformat(A.format)
BH = relaxation_as_linear_operator((fn, temp_args), AH, b) * BH
# Empty set of solver hierarchies
solvers = multilevel_solver_set()
target = initial_targets
B = initial_targets
cf = 1.0
# Aggregation process on the finest level is the same each iteration.
# To prevent repeating processes, we compute it here and provide it to the
# sovler construction.
AggOp = get_aggregate(A,
strength=strength,
aggregate=aggregate,
diagonal_dominance=diagonal_dominance,
B=initial_targets)
if isinstance(aggregate, tuple):
aggregate = [('predefined', {'AggOp': AggOp}), aggregate]
elif isinstance(aggregate, list):
aggregate.insert(0, ('predefined', {'AggOp': AggOp}))
else:
raise TypeError("Aggregate variable must be list or tuple.")
# Continue adding hierarchies until desired convergence factor achieved,
# or maximum number of hierarchies constructed
it = 0
while (cf > desired_convergence) and (it < max_hierarchies):
# pdb.set_trace()
# Make target vector orthogonal and energy orthonormal and reconstruct hierarchy
if use_ritz and it > 0:
B = global_ritz_process(A, B, weak_tol=100)
reconstruct_hierarchy(solver_set=solvers,
A=A,
new_B=B,
symmetry=symmetry,
aggregate=aggregate,
presmoother=presmoother,
postsmoother=postsmoother,
smooth=smooth,
strength=strength,
max_levels=max_levels,
max_coarse=max_coarse,
coarse_solver=coarse_solver,
diagonal_dominance=diagonal_dominance,
keep=keep,
**kwargs)
print "Hierarchy reconstructed."
# Otherwise just add new hierarchy to solver set.
else:
solvers.add_hierarchy(
smoothed_aggregation_solver(
A,
B=B[:, -1],
symmetry=symmetry,
aggregate=aggregate,
presmoother=presmoother,
postsmoother=postsmoother,
smooth=smooth,
strength=strength,
max_levels=max_levels,
max_coarse=max_coarse,
diagonal_dominance=diagonal_dominance,
coarse_solver=coarse_solver,
improve_candidates=improve_candidates,
keep=keep,
**kwargs))
# Test for convergence factor using new hierarchy.
x0 = np.random.rand(n, 1)
residuals = []
target = solvers.solve(test_rhs,
x0=x0,
tol=1e-12,
maxiter=test_iterations,
cycle=test_cycle,
accel=test_accel,
residuals=residuals,
additive=additive)
cf = residuals[-1] / residuals[-2]
B = np.hstack((B, target))
it += 1
print "Added new hierarchy, convergence factor = ", cf
B = B[:, :-1]
# B2 = global_ritz_process(A, B, weak_tol=1.0)
angles = test_targets(A, B)
# angles = test_targets(A, B2)
# -------------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------------- #
# b = np.zeros((n,1))
# asa_residuals = []
# sol = solvers.solve(b, x0, tol=1e-8, residuals=asa_residuals, accel=None)
# asa_conv_factors = np.zeros((len(asa_residuals)-1,1))
# for i in range(0,len(asa_residuals)-1):
# asa_conv_factors[i] = asa_residuals[i]/asa_residuals[i-1]
# print "Original adaptive SA/AMG - ", np.mean(asa_conv_factors[1:])
# if reconstruct:
# reconstruct_hierarchy(solver_set=solvers, A=A, new_B=B2, symmetry=symmetry,
# aggregate=aggregate, presmoother=presmoother,
# postsmoother=postsmoother, smooth=smooth,
# strength=strength, max_levels=max_levels,
# max_coarse=max_coarse, coarse_solver=coarse_solver,
# diagonal_dominance=diagonal_dominance,
# keep=keep, **kwargs)
# print "Hierarchy reconstructed."
# asa_residuals2 = []
# sol = solvers.solve(b, x0, tol=1e-8, residuals=asa_residuals2, accel=None)
# asa_conv_factors2 = np.zeros((len(asa_residuals2)-1,1))
# for i in range(0,len(asa_residuals2)-1):
# asa_conv_factors2[i] = asa_residuals2[i]/asa_residuals2[i-1]
# print "Ritz adaptive SA/AMG - ", np.mean(asa_conv_factors2[1:])
# if reconstruct:
# reconstruct_hierarchy(solver_set=solvers, A=A, new_B=B[:,:-1], symmetry=symmetry,
# aggregate=aggregate, presmoother=presmoother,
# postsmoother=postsmoother, smooth=smooth,
# strength=strength, max_levels=max_levels,
# max_coarse=max_coarse, coarse_solver=coarse_solver,
# diagonal_dominance=diagonal_dominance,
# keep=keep, **kwargs)
# print "Hierarchy reconstructed."
# asa_residuals2 = []
# sol = solvers.solve(b, x0, tol=1e-8, residuals=asa_residuals2, accel=None)
# asa_conv_factors2 = np.zeros((len(asa_residuals2)-1,1))
# for i in range(0,len(asa_residuals2)-1):
# asa_conv_factors2[i] = asa_residuals2[i]/asa_residuals2[i-1]
# print "Original(-1) SA/AMG - ", np.mean(asa_conv_factors2[1:])
# if reconstruct:
# reconstruct_hierarchy(solver_set=solvers, A=A, new_B=B2[:,:-1], symmetry=symmetry,
# aggregate=aggregate, presmoother=presmoother,
# postsmoother=postsmoother, smooth=smooth,
# strength=strength, max_levels=max_levels,
# max_coarse=max_coarse, coarse_solver=coarse_solver,
# diagonal_dominance=diagonal_dominance,
# keep=keep, **kwargs)
# print "Hierarchy reconstructed."
# asa_residuals2 = []
# sol = solvers.solve(b, x0, tol=1e-8, residuals=asa_residuals2, accel=None)
# asa_conv_factors2 = np.zeros((len(asa_residuals2)-1,1))
# for i in range(0,len(asa_residuals2)-1):
# asa_conv_factors2[i] = asa_residuals2[i]/asa_residuals2[i-1]
# print "Ritz(-1) SA/AMG - ", np.mean(asa_conv_factors2[1:])
# pdb.set_trace()
# -------------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------------- #
return solvers
def reconstruct_hierarchy(solver_set, A, new_B, symmetry, aggregate,
presmoother, postsmoother, smooth, strength,
max_levels, max_coarse, coarse_solver,
diagonal_dominance, keep, **kwargs):
if not isinstance(solver_set, multilevel_solver_set):
raise TypeError("Must pass in multilevel solver set.")
if not isinstance(new_B, np.ndarray):
raise ValueError(
"Target vectors must be ndarray of size nxb, for b hierarchies and problem size n."
)
num_solvers = solver_set.num_hierarchies
num_badguys = new_B.shape[1]
n = new_B.shape[0]
if (n != A.shape[0]):
raise ValueError("Target vectors must have same size as matrix.")
# If less target vectors provided than solvers in set, remove solvers without target
# vector to reconstruct.
diff = num_solvers - num_badguys
if (diff > 0):
print "Less target vectors provided than hierachies in solver. Removing hierarchies."
for i in range(0, diff):
solver_set.remove_hierarchy(num_solvers - 1)
num_solvers -= 1
# Reconstruct each solver in hierarchy using new target vectors
print "Reconstructing hierarchy."
for i in range(0, num_solvers):
solver_set.replace_hierarchy(hierarchy=smoothed_aggregation_solver(
A,
B=new_B[:, i],
symmetry=symmetry,
aggregate=aggregate,
presmoother=presmoother,
postsmoother=postsmoother,
smooth=smooth,
strength=strength,
max_levels=max_levels,
max_coarse=max_coarse,
coarse_solver=coarse_solver,
diagonal_dominance=diagonal_dominance,
improve_candidates=None,
keep=keep,
**kwargs),
ind=i)
# If more bad guys are provided than stored in initial hierarchy
if diff < 0:
print "More target vectors provided than hierachies in solver. Adding hierarchies."
for i in range(num_solvers, num_badguys):
solver_set.add_hierarchy(hierarchy=smoothed_aggregation_solver(
A,
B=new_B[:, i],
symmetry=symmetry,
aggregate=aggregate,
presmoother=presmoother,
postsmoother=postsmoother,
smooth=smooth,
strength=strength,
max_levels=max_levels,
max_coarse=max_coarse,
coarse_solver=coarse_solver,
diagonal_dominance=diagonal_dominance,
improve_candidates=None,
keep=keep,
**kwargs))
def test_nonhermitian(self):
# problem data
data = load_example('helmholtz_2D')
A = data['A'].tocsr()
B = data['B']
np.random.seed(625)
x0 = scipy.rand(A.shape[0]) + 1.0j * scipy.rand(A.shape[0])
b = A * scipy.rand(A.shape[0]) + 1.0j * (A * scipy.rand(A.shape[0]))
# solver parameters
smooth = ('energy', {'krylov': 'gmres'})
SA_build_args = {
'max_coarse': 25,
'coarse_solver': 'pinv2',
'symmetry': 'symmetric'
}
SA_solve_args = {'cycle': 'V', 'maxiter': 20, 'tol': 1e-8}
strength = [('evolution', {'k': 2, 'epsilon': 2.0})]
smoother = ('gauss_seidel_nr', {'sweep': 'symmetric', 'iterations': 1})
# Construct solver with nonsymmetric parameters
sa = smoothed_aggregation_solver(A,
B=B,
smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
**SA_build_args)
residuals = []
# stand-alone solve
x = sa.solve(b, x0=x0, residuals=residuals, **SA_solve_args)
residuals = np.array(residuals)
avg_convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert (avg_convergence_ratio < 0.85)
# accelerated solve
residuals = []
x = sa.solve(b,
x0=x0,
residuals=residuals,
accel='gmres',
**SA_solve_args)
del x
residuals = np.array(residuals)
avg_convergence_ratio =\
(residuals[-1] / residuals[0]) ** (1.0 / len(residuals))
assert (avg_convergence_ratio < 0.7)
# test that nonsymmetric parameters give the same result as symmetric
# parameters for the complex-symmetric matrix A
strength = 'symmetric'
SA_build_args['symmetry'] = 'nonsymmetric'
sa_nonsymm = smoothed_aggregation_solver(A,
B=np.ones((A.shape[0], 1)),
smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
improve_candidates=None,
**SA_build_args)
SA_build_args['symmetry'] = 'symmetric'
sa_symm = smoothed_aggregation_solver(A,
B=np.ones((A.shape[0], 1)),
smooth=smooth,
strength=strength,
presmoother=smoother,
postsmoother=smoother,
improve_candidates=None,
**SA_build_args)
for (symm_lvl, nonsymm_lvl) in zip(sa_nonsymm.levels, sa_symm.levels):
assert_array_almost_equal(symm_lvl.A.todense(),
nonsymm_lvl.A.todense())
sa_residuals = []
# ----------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------- #
# Classical smoothed aggregation solver
# --------------------------
# Form classical smoothed aggregation multilevel solver object
start = time.clock()
ml_sa = smoothed_aggregation_solver(A,
B=None,
symmetry='symmetric',
strength=strength_connection,
aggregate=aggregation,
smooth=interp_smooth1,
max_levels=max_levels,
max_coarse=max_coarse,
presmoother=relaxation,
postsmoother=relaxation,
improve_candidates=improve_candidates,
keep=keep)
sol = ml_sa.solve(b, x0, tol, residuals=sa_residuals)
# setup = setup_complexity(sa=ml_sa, strength=strength_connection, smooth=interp_smooth1,
# improve_candidates=improve_candidates, aggregate=aggregation,
# presmoother=relaxation, postsmoother=relaxation, keep=keep,
# max_levels=max_levels, max_coarse=max_coarse, coarse_solver=coarse_solver,
# symmetry='symmetric')
# cycle = cycle_complexity(solver=ml_sa, presmoothing=relaxation, postsmoothing=relaxation, cycle='V')
