def edgeAMG(Anode,Acurl,D):
nodalAMG = smoothed_aggregation_solver(Anode,max_coarse=10,keep=True)
##
# construct multilevel structure
levels = []
levels.append( multilevel_solver.level() )
levels[-1].A = Acurl
levels[-1].D = D
for i in range(1,len(nodalAMG.levels)):
A = levels[-1].A
Pnode = nodalAMG.levels[i-1].AggOp
P = findPEdge(D, Pnode)
R = P.T
levels[-1].P = P
levels[-1].R = R
levels.append( multilevel_solver.level() )
A = R*A*P
D = csr_matrix(dia_matrix((1.0/((P.T*P).diagonal()),0),shape=(P.shape[1],P.shape[1]))*(P.T*D*Pnode))
levels[-1].A = A
levels[-1].D = D
edgeML = multilevel_solver(levels)
for i in range(0,len(edgeML.levels)):
edgeML.levels[i].presmoother = setup_hiptmair(levels[i])
edgeML.levels[i].postsmoother = setup_hiptmair(levels[i])
return edgeML
def edgeAMG(Anode, Acurl, D):
nodalAMG = smoothed_aggregation_solver(Anode, max_coarse=10, keep=True)
##
# construct multilevel structure
levels = []
levels.append(multilevel_solver.level())
levels[-1].A = Acurl
levels[-1].D = D
for i in range(1, len(nodalAMG.levels)):
A = levels[-1].A
Pnode = nodalAMG.levels[i - 1].AggOp
P = findPEdge(D, Pnode)
R = P.T
levels[-1].P = P
levels[-1].R = R
levels.append(multilevel_solver.level())
A = R * A * P
D = csr_matrix(
dia_matrix((1.0 / ((P.T * P).diagonal()), 0),
shape=(P.shape[1], P.shape[1])) * (P.T * D * Pnode))
levels[-1].A = A
levels[-1].D = D
edgeML = multilevel_solver(levels)
for i in range(0, len(edgeML.levels)):
edgeML.levels[i].presmoother = setup_hiptmair(levels[i])
edgeML.levels[i].postsmoother = setup_hiptmair(levels[i])
return edgeML
def test_cycle_complexity(self):
# four levels
levels = []
levels.append(multilevel_solver.level())
levels[0].A = csr_matrix(ones((10, 10)))
levels[0].P = csr_matrix(ones((10, 5)))
levels.append(multilevel_solver.level())
levels[1].A = csr_matrix(ones((5, 5)))
levels[1].P = csr_matrix(ones((5, 3)))
levels.append(multilevel_solver.level())
levels[2].A = csr_matrix(ones((3, 3)))
levels[2].P = csr_matrix(ones((3, 2)))
levels.append(multilevel_solver.level())
levels[3].A = csr_matrix(ones((2, 2)))
# one level hierarchy
mg = multilevel_solver(levels[:1])
assert_equal(mg.cycle_complexity(cycle='V'), 100.0 / 100.0) # 1
assert_equal(mg.cycle_complexity(cycle='W'), 100.0 / 100.0) # 1
assert_equal(mg.cycle_complexity(cycle='AMLI'), 100.0 / 100.0) # 1
assert_equal(mg.cycle_complexity(cycle='F'), 100.0 / 100.0) # 1
# two level hierarchy
mg = multilevel_solver(levels[:2])
change_smoothers(mg, 'gauss_seidel', 'gauss_seidel')
assert_equal(mg.cycle_complexity(cycle='V'), 4.0)
assert_equal(mg.cycle_complexity(cycle='W'), 4.0)
assert_equal(mg.cycle_complexity(cycle='AMLI'), 4.0)
assert_equal(mg.cycle_complexity(cycle='F'), 4.0)
# three level hierarchy
mg = multilevel_solver(levels[:3])
change_smoothers(mg, ('gauss_seidel', {
'iterations': 2
}), 'gauss_seidel')
assert_equal(mg.cycle_complexity(cycle='V'), 6.3)
assert_equal(mg.cycle_complexity(cycle='W'), 7.6)
assert_equal(mg.cycle_complexity(cycle='AMLI'), 7.6)
assert_equal(mg.cycle_complexity(cycle='F'), 7.6)
# four level hierarchy
mg = multilevel_solver(levels[:4])
change_smoothers(mg, ('gauss_seidel', {
'sweep': 'symmetric'
}), 'gauss_seidel')
assert_equal(mg.cycle_complexity(cycle='V'), 6.78)
assert_equal(mg.cycle_complexity(cycle='W'), 9.52)
assert_equal(mg.cycle_complexity(cycle='AMLI'), 9.52)
assert_equal(mg.cycle_complexity(cycle='F'), 9.04)
def extend_hierarchy(levels, CF, l, maxp, theta_a, keep):
"""Extend the multigrid hierarchy."""
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
A = levels[-1].A
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'CR':
splitting = CR(A, **kwargs)
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
# rs_C = classical_strength_of_connection(A, theta=0.25)
# rs_splitting = split.RS(rs_C)
# rs_P = direct_interpolation(A.copy(), rs_C.copy(), rs_splitting.copy())
#
# rs_P_sparsity = rs_P.copy()
# rs_P_sparsity.data[:] = 1
#
# rs_fine = np.where(rs_splitting == 0)[0]
# rs_coarse = np.where(rs_splitting == 1)[0]
# rs_A_fc = A[rs_fine][:, rs_coarse]
# rs_W = rs_P[rs_fine]
# my_rs_P, my_rs_W = my_direct_interpolation(rs_A_fc, A, rs_W, rs_coarse, rs_fine)
#
# my_rs_P_sparsity = my_rs_P.copy()
# my_rs_P_sparsity.data[:] = 1
#
# rs_A_sparsity = A[:, rs_coarse].copy()
# rs_A_sparsity.data[:] = 1
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
P = truncation_interpolation(A, splitting, l, maxp, theta_a)
# P = optimal_interpolation(A, splitting)
# P = rs_P
# Generate the restriction matrix that maps from the fine-grid to the
# coarse-grid
R = P.T.tocsr()
# Store relevant information for this level
if keep:
levels[-1].splitting = splitting # C/F splitting
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels.append(multilevel_solver.level())
# Form next level through Galerkin product
A = R * A * P
levels[-1].A = A
def test_cycle_complexity(self):
# four levels
levels = []
levels.append(multilevel_solver.level())
levels[0].A = csr_matrix(ones((10, 10)))
levels[0].P = csr_matrix(ones((10, 5)))
levels.append(multilevel_solver.level())
levels[1].A = csr_matrix(ones((5, 5)))
levels[1].P = csr_matrix(ones((5, 3)))
levels.append(multilevel_solver.level())
levels[2].A = csr_matrix(ones((3, 3)))
levels[2].P = csr_matrix(ones((3, 2)))
levels.append(multilevel_solver.level())
levels[3].A = csr_matrix(ones((2, 2)))
# one level hierarchy
mg = multilevel_solver(levels[:1])
assert_equal(mg.cycle_complexity(cycle='V'), 100.0/100.0) # 1
assert_equal(mg.cycle_complexity(cycle='W'), 100.0/100.0) # 1
assert_equal(mg.cycle_complexity(cycle='AMLI'), 100.0/100.0) # 1
assert_equal(mg.cycle_complexity(cycle='F'), 100.0/100.0) # 1
# two level hierarchy
mg = multilevel_solver(levels[:2])
change_smoothers(mg, 'gauss_seidel', 'gauss_seidel')
assert_equal(mg.cycle_complexity(cycle='V'), 4.0)
assert_equal(mg.cycle_complexity(cycle='W'), 4.0)
assert_equal(mg.cycle_complexity(cycle='AMLI'), 4.0)
assert_equal(mg.cycle_complexity(cycle='F'), 4.0)
# three level hierarchy
mg = multilevel_solver(levels[:3])
change_smoothers(mg, ('gauss_seidel', {'iterations':2}) , 'gauss_seidel')
assert_equal(mg.cycle_complexity(cycle='V'), 6.3)
assert_equal(mg.cycle_complexity(cycle='W'), 7.6)
assert_equal(mg.cycle_complexity(cycle='AMLI'), 7.6)
assert_equal(mg.cycle_complexity(cycle='F'), 7.6)
# four level hierarchy
mg = multilevel_solver(levels[:4])
change_smoothers(mg, ('gauss_seidel', {'sweep':'symmetric'}), 'gauss_seidel')
assert_equal(mg.cycle_complexity(cycle='V'), 6.78)
assert_equal(mg.cycle_complexity(cycle='W'), 9.52)
assert_equal(mg.cycle_complexity(cycle='AMLI'), 9.52)
assert_equal(mg.cycle_complexity(cycle='F'), 9.04)
def test_cycle_complexity(self):
# four levels
levels = []
levels.append(multilevel_solver.level())
levels[0].A = csr_matrix(ones((10, 10)))
levels[0].P = csr_matrix(ones((10, 5)))
levels.append(multilevel_solver.level())
levels[1].A = csr_matrix(ones((5, 5)))
levels[1].P = csr_matrix(ones((5, 3)))
levels.append(multilevel_solver.level())
levels[2].A = csr_matrix(ones((3, 3)))
levels[2].P = csr_matrix(ones((3, 2)))
levels.append(multilevel_solver.level())
levels[3].A = csr_matrix(ones((2, 2)))
# one level hierarchy
mg = multilevel_solver(levels[:1])
assert_equal(mg.cycle_complexity(cycle='V'), 100.0 / 100.0) # 1
assert_equal(mg.cycle_complexity(cycle='W'), 100.0 / 100.0) # 1
assert_equal(mg.cycle_complexity(cycle='AMLI'), 100.0 / 100.0) # 1
assert_equal(mg.cycle_complexity(cycle='F'), 100.0 / 100.0) # 1
# two level hierarchy
mg = multilevel_solver(levels[:2])
assert_equal(mg.cycle_complexity(cycle='V'), 225.0 / 100.0) # 2,1
assert_equal(mg.cycle_complexity(cycle='W'), 225.0 / 100.0) # 2,1
assert_equal(mg.cycle_complexity(cycle='AMLI'), 225.0 / 100.0) # 2,1
assert_equal(mg.cycle_complexity(cycle='F'), 225.0 / 100.0) # 2,1
# three level hierarchy
mg = multilevel_solver(levels[:3])
assert_equal(mg.cycle_complexity(cycle='V'), 259.0 / 100.0) # 2,2,1
assert_equal(mg.cycle_complexity(cycle='W'), 318.0 / 100.0) # 2,4,2
assert_equal(mg.cycle_complexity(cycle='AMLI'), 318.0 / 100.0) # 2,4,2
assert_equal(mg.cycle_complexity(cycle='F'), 318.0 / 100.0) # 2,4,2
# four level hierarchy
mg = multilevel_solver(levels[:4])
assert_equal(mg.cycle_complexity(cycle='V'), 272.0 / 100.0) # 2,2,2,1
assert_equal(mg.cycle_complexity(cycle='W'), 388.0 / 100.0) # 2,4,8,4
assert_equal(mg.cycle_complexity(cycle='AMLI'),
388.0 / 100.0) # 2,4,8,4
assert_equal(mg.cycle_complexity(cycle='F'), 366.0 / 100.0) # 2,4,6,3
def test_cycle_complexity(self):
# four levels
levels = []
levels.append(multilevel_solver.level())
levels[0].A = csr_matrix(ones((10, 10)))
levels[0].P = csr_matrix(ones((10, 5)))
levels.append(multilevel_solver.level())
levels[1].A = csr_matrix(ones((5, 5)))
levels[1].P = csr_matrix(ones((5, 3)))
levels.append(multilevel_solver.level())
levels[2].A = csr_matrix(ones((3, 3)))
levels[2].P = csr_matrix(ones((3, 2)))
levels.append(multilevel_solver.level())
levels[3].A = csr_matrix(ones((2, 2)))
# one level hierarchy
mg = multilevel_solver(levels[:1])
assert_equal(mg.cycle_complexity(cycle='V'), 100.0/100.0) # 1
assert_equal(mg.cycle_complexity(cycle='W'), 100.0/100.0) # 1
assert_equal(mg.cycle_complexity(cycle='AMLI'), 100.0/100.0) # 1
assert_equal(mg.cycle_complexity(cycle='F'), 100.0/100.0) # 1
# two level hierarchy
mg = multilevel_solver(levels[:2])
assert_equal(mg.cycle_complexity(cycle='V'), 225.0/100.0) # 2,1
assert_equal(mg.cycle_complexity(cycle='W'), 225.0/100.0) # 2,1
assert_equal(mg.cycle_complexity(cycle='AMLI'), 225.0/100.0) # 2,1
assert_equal(mg.cycle_complexity(cycle='F'), 225.0/100.0) # 2,1
# three level hierarchy
mg = multilevel_solver(levels[:3])
assert_equal(mg.cycle_complexity(cycle='V'), 259.0/100.0) # 2,2,1
assert_equal(mg.cycle_complexity(cycle='W'), 318.0/100.0) # 2,4,2
assert_equal(mg.cycle_complexity(cycle='AMLI'), 318.0/100.0) # 2,4,2
assert_equal(mg.cycle_complexity(cycle='F'), 318.0/100.0) # 2,4,2
# four level hierarchy
mg = multilevel_solver(levels[:4])
assert_equal(mg.cycle_complexity(cycle='V'), 272.0/100.0) # 2,2,2,1
assert_equal(mg.cycle_complexity(cycle='W'), 388.0/100.0) # 2,4,8,4
assert_equal(mg.cycle_complexity(cycle='AMLI'), 388.0/100.0) # 2,4,8,4
assert_equal(mg.cycle_complexity(cycle='F'), 366.0/100.0) # 2,4,6,3
def rootnode_solver(A, B=None, BH=None,
symmetry='hermitian', strength='symmetric',
aggregate='standard', smooth='energy',
presmoother=('block_gauss_seidel',
{'sweep': 'symmetric'}),
postsmoother=('block_gauss_seidel',
{'sweep': 'symmetric'}),
improve_candidates=('block_gauss_seidel',
{'sweep': 'symmetric',
'iterations': 4}),
max_levels = 10, max_coarse = 10,
diagonal_dominance=False, keep=False, **kwargs):
"""
Create a multilevel solver using root-node based Smoothed Aggregation (SA).
See the notes below, for the major differences with the classical-style
smoothed aggregation solver in aggregation.smoothed_aggregation_solver.
Parameters
----------
A : {csr_matrix, bsr_matrix}
Sparse NxN matrix in CSR or BSR format
B : {None, array_like}
Right near-nullspace candidates stored in the columns of an NxK array.
K must be >= the blocksize of A (see reference [2]). The default value
B=None is equivalent to choosing the constant over each block-variable,
B=np.kron(np.ones((A.shape[0]/blocksize(A), 1)), np.eye(blocksize(A)))
BH : {None, array_like}
Left near-nullspace candidates stored in the columns of an NxK array.
BH is only used if symmetry is 'nonsymmetric'. K must be >= the
blocksize of A (see reference [2]). The default value B=None is
equivalent to choosing the constant over each block-variable,
B=np.kron(np.ones((A.shape[0]/blocksize(A), 1)), np.eye(blocksize(A)))
symmetry : {string}
'symmetric' refers to both real and complex symmetric
'hermitian' refers to both complex Hermitian and real Hermitian
'nonsymmetric' i.e. nonsymmetric in a hermitian sense
Note that for the strictly real case, symmetric and hermitian are
the same
Note that this flag does not denote definiteness of the operator.
strength : {list} : default
['symmetric', 'classical', 'evolution', 'algebraic_distance', 'affinity',
('predefined', {'C' : csr_matrix}), None]
Method used to determine the strength of connection between unknowns of
the linear system. Method-specific parameters may be passed in using a
tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If strength=None,
all nonzero entries of the matrix are considered strong.
See notes below for varying this parameter on a per level basis. Also,
see notes below for using a predefined strength matrix on each level.
aggregate : {list} : default ['standard', 'lloyd', 'naive',
('predefined', {'AggOp' : csr_matrix})]
Method used to aggregate nodes. See notes below for varying this
parameter on a per level basis. Also, see notes below for using a
predefined aggregation on each level.
smooth : {list} : default ['energy', None]
Method used to smooth the tentative prolongator. Method-specific
parameters may be passed in using a tuple, e.g. smooth=
('energy',{'krylov' : 'gmres'}). Only 'energy' and None are valid
prolongation smoothing options. See notes below for varying this
parameter on a per level basis.
presmoother : {tuple, string, list} : default ('block_gauss_seidel',
{'sweep':'symmetric'})
Defines the presmoother for the multilevel cycling. The default block
Gauss-Seidel option defaults to point-wise Gauss-Seidel, if the matrix
is CSR or is a BSR matrix with blocksize of 1. See notes below for
varying this parameter on a per level basis.
postsmoother : {tuple, string, list}
Same as presmoother, except defines the postsmoother.
improve_candidates : {tuple, string, list} : default
[('block_gauss_seidel',
{'sweep': 'symmetric', 'iterations': 4}), None]
The ith entry defines the method used to improve the candidates B on
level i. If the list is shorter than max_levels, then the last entry
will define the method for all levels lower. If tuple or string, then
this single relaxation descriptor defines improve_candidates on all
levels.
The list elements are relaxation descriptors of the form used for
presmoother and postsmoother. A value of None implies no action on B.
max_levels : {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse : {integer} : default 500
Maximum number of variables permitted on the coarse grid.
diagonal_dominance : {bool, tuple} : default False
If True (or the first tuple entry is True), then avoid coarsening
diagonally dominant rows. The second tuple entry requires a
dictionary, where the key value 'theta' is used to tune the diagonal
dominance threshold.
keep : {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C),
tentative prolongation (T), aggregation (AggOp), and arrays
storing the C-points (Cpts) and F-points (Fpts) are kept at
each level.
Other Parameters
----------------
cycle_type : ['V','W','F']
Structrure of multigrid cycle
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
setup_complexity : bool
For a detailed, more accurate setup complexity, pass in
'setup_complexity' = True. This will slow down performance, but
increase accuracy of complexity count.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
See Also
--------
multilevel_solver, aggregation.smoothed_aggregation_solver,
classical.ruge_stuben_solver
Notes
-----
- Root-node style SA differs from classical SA primarily by preserving
and identity block in the interpolation operator, P. Each aggregate
has a "root-node" or "center-node" associated with it, and this
root-node is injected from the coarse grid to the fine grid. The
injection corresponds to the identity block.
- Only smooth={'energy', None} is supported for prolongation
smoothing. See reference [2] below for more details on why the
'energy' prolongation smoother is the natural counterpart to
root-node style SA.
- The additional parameters are passed through as arguments to
multilevel_solver. Refer to pyamg.multilevel_solver for additional
documentation.
- At each level, four steps are executed in order to define the coarser
level operator.
1. Matrix A is given and used to derive a strength matrix, C.
2. Based on the strength matrix, indices are grouped or aggregated.
3. The aggregates define coarse nodes and a tentative prolongation
operator T is defined by injection
4. The tentative prolongation operator is smoothed by a relaxation
scheme to improve the quality and extent of interpolation from the
aggregates to fine nodes.
- The parameters smooth, strength, aggregate, presmoother, postsmoother
can be varied on a per level basis. For different methods on
different levels, use a list as input so that the i-th entry defines
the method at the i-th level. If there are more levels in the
hierarchy than list entries, the last entry will define the method
for all levels lower.
Examples are:
smooth=[('jacobi', {'omega':1.0}), None, 'jacobi']
presmoother=[('block_gauss_seidel', {'sweep':symmetric}), 'sor']
aggregate=['standard', 'naive']
strength=[('symmetric', {'theta':0.25}),
('symmetric', {'theta':0.08})]
- Predefined strength of connection and aggregation schemes can be
specified. These options are best used together, but aggregation can
be predefined while strength of connection is not.
For predefined strength of connection, use a list consisting of
tuples of the form ('predefined', {'C' : C0}), where C0 is a
csr_matrix and each degree-of-freedom in C0 represents a supernode.
For instance to predefine a three-level hierarchy, use
[('predefined', {'C' : C0}), ('predefined', {'C' : C1}) ].
Similarly for predefined aggregation, use a list of tuples. For
instance to predefine a three-level hierarchy, use [('predefined',
{'AggOp' : Agg0}), ('predefined', {'AggOp' : Agg1}) ], where the
dimensions of A, Agg0 and Agg1 are compatible, i.e. Agg0.shape[1] ==
A.shape[0] and Agg1.shape[1] == Agg0.shape[0]. Each AggOp is a
csr_matrix.
Because this is a root-nodes solver, if a member of the predefined
aggregation list is predefined, it must be of the form
('predefined', {'AggOp' : Agg, 'Cnodes' : Cnodes}).
Examples
--------
>>> from pyamg import rootnode_solver
>>> from pyamg.gallery import poisson
>>> from scipy.sparse.linalg import cg
>>> import numpy as np
>>> A = poisson((100, 100), format='csr') # matrix
>>> b = np.ones((A.shape[0])) # RHS
>>> ml = rootnode_solver(A) # AMG solver
>>> M = ml.aspreconditioner(cycle='V') # preconditioner
>>> x, info = cg(A, b, tol=1e-8, maxiter=30, M=M) # solve with CG
References
----------
.. [1] Vanek, P. and Mandel, J. and Brezina, M.,
"Algebraic Multigrid by Smoothed Aggregation for
Second and Fourth Order Elliptic Problems",
Computing, vol. 56, no. 3, pp. 179--196, 1996.
http://citeseer.ist.psu.edu/vanek96algebraic.html
.. [2] Olson, L. and Schroder, J. and Tuminaro, R.,
"A general interpolation strategy for algebraic
multigrid using energy minimization", SIAM Journal
on Scientific Computing (SISC), vol. 33, pp.
966--991, 2011.
"""
if ('setup_complexity' in kwargs):
if kwargs['setup_complexity'] == True:
mat_mat_complexity.__detailed__ = True
del kwargs['setup_complexity']
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, \
bsr_matrix, or be convertible to csr_matrix')
A = A.asfptype()
if (symmetry != 'symmetric') and (symmetry != 'hermitian') and \
(symmetry != 'nonsymmetric'):
raise ValueError('expected \'symmetric\', \'nonsymmetric\' \
or \'hermitian\' for the symmetry parameter ')
A.symmetry = symmetry
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
# Right near nullspace candidates use constant for each variable as default
if B is None:
B = np.kron(np.ones((int(A.shape[0]/blocksize(A)), 1), dtype=A.dtype),
np.eye(blocksize(A)))
else:
B = np.asarray(B, dtype=A.dtype)
if len(B.shape) == 1:
B = B.reshape(-1, 1)
if B.shape[0] != A.shape[0]:
raise ValueError('The near null-space modes B have incorrect \
dimensions for matrix A')
if B.shape[1] < blocksize(A):
raise ValueError('B.shape[1] must be >= the blocksize of A')
# Left near nullspace candidates
if A.symmetry == 'nonsymmetric':
if BH is None:
BH = B.copy()
else:
BH = np.asarray(BH, dtype=A.dtype)
if len(BH.shape) == 1:
BH = BH.reshape(-1, 1)
if BH.shape[1] != B.shape[1]:
raise ValueError('The number of left and right near \
null-space modes B and BH, must be equal')
if BH.shape[0] != A.shape[0]:
raise ValueError('The near null-space modes BH have \
incorrect dimensions for matrix A')
# Levelize the user parameters, so that they become lists describing the
# desired user option on each level.
max_levels, max_coarse, strength =\
levelize_strength_or_aggregation(strength, max_levels, max_coarse)
max_levels, max_coarse, aggregate =\
levelize_strength_or_aggregation(aggregate, max_levels, max_coarse)
improve_candidates =\
levelize_smooth_or_improve_candidates(improve_candidates, max_levels)
smooth = levelize_smooth_or_improve_candidates(smooth, max_levels)
# Construct multilevel structure
levels = []
levels.append(multilevel_solver.level())
levels[-1].A = A # matrix
# Append near nullspace candidates
levels[-1].B = B # right candidates
if A.symmetry == 'nonsymmetric':
levels[-1].BH = BH # left candidates
while len(levels) < max_levels and \
int(levels[-1].A.shape[0]/blocksize(levels[-1].A)) > max_coarse:
extend_hierarchy(levels, strength, aggregate, smooth,
improve_candidates, diagonal_dominance, keep)
# Construct and return multilevel hierarchy
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def extend_hierarchy(levels, strength, CF, keep):
""" helper function for local methods """
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
A = levels[-1].A
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
C = evolution_strength_of_connection(A, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn is None:
C = A
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'RS':
splitting = split.RS(C)
elif fn == 'PMIS':
splitting = split.PMIS(C)
elif fn == 'PMISc':
splitting = split.PMISc(C)
elif fn == 'CLJP':
splitting = split.CLJP(C)
elif fn == 'CLJPc':
splitting = split.CLJPc(C)
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
P = direct_interpolation(A, C, splitting)
# Generate the restriction matrix that maps from the fine-grid to the
# coarse-grid
R = P.T.tocsr()
# Store relevant information for this level
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].splitting = splitting # C/F splitting
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels.append(multilevel_solver.level())
# Form next level through Galerkin product
A = R * A * P
levels[-1].A = A
def extend_hierarchy(levels, strength, aggregate, smooth, improve_candidates,
diagonal_dominance=False, keep=True, test_ind=0):
"""Service routine to implement the strength of connection, aggregation,
tentative prolongation construction, and prolongation smoothing. Called by
smoothed_aggregation_solver.
"""
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
A = levels[-1].A
B = levels[-1].B
if A.symmetry == "nonsymmetric":
AH = A.H.asformat(A.format)
BH = levels[-1].BH
# Improve near nullspace candidates by relaxing on A B = 0
fn, kwargs = unpack_arg(improve_candidates[len(levels)-1])
if fn is not None:
b = np.zeros((A.shape[0], 1), dtype=A.dtype)
B = relaxation_as_linear_operator((fn, kwargs), A, b) * B
levels[-1].B = B
if A.symmetry == "nonsymmetric":
BH = relaxation_as_linear_operator((fn, kwargs), AH, b) * BH
levels[-1].BH = BH
# Compute the strength-of-connection matrix C, where larger
# C[i, j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength[len(levels)-1])
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
if 'B' in kwargs:
C = evolution_strength_of_connection(A, **kwargs)
else:
C = evolution_strength_of_connection(A, B, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'predefined':
C = kwargs['C'].tocsr()
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn is None:
C = A.tocsr()
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
# Avoid coarsening diagonally dominant rows
flag, kwargs = unpack_arg(diagonal_dominance)
if flag:
C = eliminate_diag_dom_nodes(A, C, **kwargs)
# Compute the aggregation matrix AggOp (i.e., the nodal coarsening of A).
# AggOp is a boolean matrix, where the sparsity pattern for the k-th column
# denotes the fine-grid nodes agglomerated into k-th coarse-grid node.
fn, kwargs = unpack_arg(aggregate[len(levels)-1])
if fn == 'standard':
AggOp, Cnodes = standard_aggregation(C, **kwargs)
elif fn == 'naive':
AggOp, Cnodes = naive_aggregation(C, **kwargs)
elif fn == 'lloyd':
AggOp, Cnodes = lloyd_aggregation(C, **kwargs)
elif fn == 'pairwise':
AggOp, Cnodes = pairwise_aggregation(A, B, **kwargs)
elif fn == 'predefined':
AggOp = kwargs['AggOp'].tocsr()
Cnodes = kwargs['Cnodes']
else:
raise ValueError('unrecognized aggregation method %s' % str(fn))
# ----------------------------------------------------------------------------- #
# ------------------- New ideal interpolation constructed -------------------- #
# ----------------------------------------------------------------------------- #
# pdb.set_trace()
# splitting = CR(A)
# Cpts = [i for i in range(0,AggOp.shape[0]) if splitting[i]==1]
# Compute prolongation operator.
if test_ind==0:
T = new_ideal_interpolation(A=A, AggOp=AggOp, Cnodes=Cnodes, B=B[:, 0:blocksize(A)], SOC=C)
else:
T = py_ideal_interpolation(A=A, AggOp=AggOp, Cnodes=Cnodes, B=B[:, 0:blocksize(A)], SOC=C)
print "\nSize of sparsity pattern - ", T.nnz
# Smooth the tentative prolongator, so that it's accuracy is greatly
# improved for algebraically smooth error.
# fn, kwargs = unpack_arg(smooth[len(levels)-1])
# if fn == 'jacobi':
# P = jacobi_prolongation_smoother(A, T, C, B, **kwargs)
# elif fn == 'richardson':
# P = richardson_prolongation_smoother(A, T, **kwargs)
# elif fn == 'energy':
# P = energy_prolongation_smoother(A, T, C, B, None, (False, {}),
# **kwargs)
# elif fn is None:
# P = T
# else:
# raise ValueError('unrecognized prolongation smoother method %s' %
# str(fn))
P = T
# ----------------------------------------------------------------------------- #
# ----------------------------------------------------------------------------- #
# Compute the restriction matrix R, which interpolates from the fine-grid
# to the coarse-grid. If A is nonsymmetric, then R must be constructed
# based on A.H. Otherwise R = P.H or P.T.
symmetry = A.symmetry
if symmetry == 'hermitian':
# symmetrically scale out the diagonal, include scaling in P, R
A = P.H * A * P
[dum, Dinv, dum] = symmetric_rescaling(A,copy=False)
P = bsr_matrix(P * diags(Dinv,offsets=0,format='csr'), blocksize=A.blocksize)
del dum
R = P.H
elif symmetry == 'symmetric':
# symmetrically scale out the diagonal, include scaling in P, R
A = P.T * A * P
[dum, Dinv, dum] = symmetric_rescaling(A,copy=False)
P = bsr_matrix(P * diags(Dinv,offsets=0,format='csr'), blocksize=A.blocksize)
del dum
R = P.T
elif symmetry == 'nonsymmetric':
raise TypeError('New ideal interpolation not implemented for non-symmetric matrix.')
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].AggOp = AggOp # aggregation operator
levels[-1].Fpts = [i for i in range(0,AggOp.shape[0]) if i not in Cnodes]
levels[-1].P = P # smoothed prolongator
levels[-1].R = R # restriction operator
levels[-1].Cpts = Cnodes # Cpts (i.e., rootnodes)
levels.append(multilevel_solver.level())
A.symmetry = symmetry
levels[-1].A = A
levels[-1].B = R*B # right near nullspace candidates
test = A.tocsr()
print "\nSize of coarse operator - ", test.nnz
if A.symmetry == "nonsymmetric":
levels[-1].BH = BH # left near nullspace candidates
def extend_hierarchy(levels, strength, aggregate, smooth, improve_candidates,
diagonal_dominance=False, keep=True):
"""Service routine to implement the strength of connection, aggregation,
tentative prolongation construction, and prolongation smoothing. Called by
smoothed_aggregation_solver.
"""
A = levels[-1].A
B = levels[-1].B
if A.symmetry == "nonsymmetric":
AH = A.H.asformat(A.format)
BH = levels[-1].BH
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength[len(levels)-1])
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
if 'B' in kwargs:
C = evolution_strength_of_connection(A, **kwargs)
else:
C = evolution_strength_of_connection(A, B, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'predefined':
C = kwargs['C'].tocsr()
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A.tocsr()
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
levels[-1].complexity['strength'] = kwargs['cost'][0]
# Avoid coarsening diagonally dominant rows
flag, kwargs = unpack_arg(diagonal_dominance)
if flag:
C = eliminate_diag_dom_nodes(A, C, **kwargs)
levels[-1].complexity['diag_dom'] = kwargs['cost'][0]
# Compute the aggregation matrix AggOp (i.e., the nodal coarsening of A).
# AggOp is a boolean matrix, where the sparsity pattern for the k-th column
# denotes the fine-grid nodes agglomerated into k-th coarse-grid node.
fn, kwargs = unpack_arg(aggregate[len(levels)-1])
if fn == 'standard':
AggOp = standard_aggregation(C, **kwargs)[0]
elif fn == 'naive':
AggOp = naive_aggregation(C, **kwargs)[0]
elif fn == 'lloyd':
AggOp = lloyd_aggregation(C, **kwargs)[0]
elif fn == 'predefined':
AggOp = kwargs['AggOp'].tocsr()
else:
raise ValueError('unrecognized aggregation method %s' % str(fn))
levels[-1].complexity['aggregation'] = kwargs['cost'][0] * (float(C.nnz)/A.nnz)
# Improve near nullspace candidates by relaxing on A B = 0
temp_cost = [0.0]
fn, kwargs = unpack_arg(improve_candidates[len(levels)-1], cost=False)
if fn is not None:
b = np.zeros((A.shape[0], 1), dtype=A.dtype)
B = relaxation_as_linear_operator((fn, kwargs), A, b, temp_cost) * B
levels[-1].B = B
if A.symmetry == "nonsymmetric":
BH = relaxation_as_linear_operator((fn, kwargs), AH, b, temp_cost) * BH
levels[-1].BH = BH
levels[-1].complexity['candidates'] = temp_cost[0] * B.shape[1]
# Compute the tentative prolongator, T, which is a tentative interpolation
# matrix from the coarse-grid to the fine-grid. T exactly interpolates
# B_fine = T B_coarse. Orthogonalization complexity ~ 2nk^2, k=B.shape[1].
temp_cost=[0.0]
T, B = fit_candidates(AggOp, B, cost=temp_cost)
if A.symmetry == "nonsymmetric":
TH, BH = fit_candidates(AggOp, BH, cost=temp_cost)
levels[-1].complexity['tentative'] = temp_cost[0]/A.nnz
# Smooth the tentative prolongator, so that it's accuracy is greatly
# improved for algebraically smooth error.
fn, kwargs = unpack_arg(smooth[len(levels)-1])
if fn == 'jacobi':
P = jacobi_prolongation_smoother(A, T, C, B, **kwargs)
elif fn == 'richardson':
P = richardson_prolongation_smoother(A, T, **kwargs)
elif fn == 'energy':
P = energy_prolongation_smoother(A, T, C, B, None, (False, {}),
**kwargs)
elif fn is None:
P = T
else:
raise ValueError('unrecognized prolongation smoother method %s' %
str(fn))
levels[-1].complexity['smooth_P'] = kwargs['cost'][0]
# Compute the restriction matrix, R, which interpolates from the fine-grid
# to the coarse-grid. If A is nonsymmetric, then R must be constructed
# based on A.H. Otherwise R = P.H or P.T.
symmetry = A.symmetry
if symmetry == 'hermitian':
R = P.H
elif symmetry == 'symmetric':
R = P.T
elif symmetry == 'nonsymmetric':
fn, kwargs = unpack_arg(smooth[len(levels)-1])
if fn == 'jacobi':
R = jacobi_prolongation_smoother(AH, TH, C, BH, **kwargs).H
elif fn == 'richardson':
R = richardson_prolongation_smoother(AH, TH, **kwargs).H
elif fn == 'energy':
R = energy_prolongation_smoother(AH, TH, C, BH, None, (False, {}),
**kwargs)
R = R.H
elif fn is None:
R = T.H
else:
raise ValueError('unrecognized prolongation smoother method %s' %
str(fn))
levels[-1].complexity['smooth_R'] = kwargs['cost'][0]
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].AggOp = AggOp # aggregation operator
levels[-1].T = T # tentative prolongator
levels[-1].P = P # smoothed prolongator
levels[-1].R = R # restriction operator
# Form coarse grid operator, get complexity
levels[-1].complexity['RAP'] = mat_mat_complexity(R,A) / float(A.nnz)
RA = R * A
levels[-1].complexity['RAP'] += mat_mat_complexity(RA,P) / float(A.nnz)
A = RA * P # Galerkin operator, Ac = RAP
A.symmetry = symmetry
levels.append(multilevel_solver.level())
levels[-1].A = A
levels[-1].B = B # right near nullspace candidates
if A.symmetry == "nonsymmetric":
levels[-1].BH = BH # left near nullspace candidates
def smoothed_aggregation_solver(A,
B=None,
BH=None,
symmetry='hermitian',
strength='symmetric',
aggregate='standard',
smooth=('jacobi', {
'omega': 4.0 / 3.0
}),
presmoother=('block_gauss_seidel', {
'sweep': 'symmetric'
}),
postsmoother=('block_gauss_seidel', {
'sweep': 'symmetric'
}),
improve_candidates=[('block_gauss_seidel', {
'sweep': 'symmetric',
'iterations': 4
}), None],
max_levels=10,
max_coarse=10,
diagonal_dominance=False,
keep=False,
**kwargs):
"""Create a multilevel solver using classical-style Smoothed Aggregation (SA).
Parameters
----------
A : csr_matrix, bsr_matrix
Sparse NxN matrix in CSR or BSR format
B : None, array_like
Right near-nullspace candidates stored in the columns of an NxK array.
The default value B=None is equivalent to B=ones((N,1))
BH : None, array_like
Left near-nullspace candidates stored in the columns of an NxK array.
BH is only used if symmetry is 'nonsymmetric'.
The default value B=None is equivalent to BH=B.copy()
symmetry : string
'symmetric' refers to both real and complex symmetric
'hermitian' refers to both complex Hermitian and real Hermitian
'nonsymmetric' i.e. nonsymmetric in a hermitian sense
Note, in the strictly real case, symmetric and hermitian are the same.
Note, this flag does not denote definiteness of the operator.
strength : string or list
Method used to determine the strength of connection between unknowns of
the linear system. Method-specific parameters may be passed in using a
tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If strength=None,
all nonzero entries of the matrix are considered strong.
Choose from 'symmetric', 'classical', 'evolution', 'algebraic_distance',
'affinity', ('predefined', {'C' : csr_matrix}), None
aggregate : string or list
Method used to aggregate nodes.
Choose from 'standard', 'lloyd', 'naive',
('predefined', {'AggOp' : csr_matrix})
smooth : list
Method used to smooth the tentative prolongator. Method-specific
parameters may be passed in using a tuple, e.g. smooth=
('jacobi',{'filter' : True }).
Choose from 'jacobi', 'richardson', 'energy', None
presmoother : tuple, string, list
Defines the presmoother for the multilevel cycling. The default block
Gauss-Seidel option defaults to point-wise Gauss-Seidel, if the matrix
is CSR or is a BSR matrix with blocksize of 1.
postsmoother : tuple, string, list
Same as presmoother, except defines the postsmoother.
improve_candidates : tuple, string, list
The ith entry defines the method used to improve the candidates B on
level i. If the list is shorter than max_levels, then the last entry
will define the method for all levels lower. If tuple or string, then
this single relaxation descriptor defines improve_candidates on all
levels.
The list elements are relaxation descriptors of the form used for
presmoother and postsmoother. A value of None implies no action on B.
max_levels : integer
Maximum number of levels to be used in the multilevel solver.
max_coarse : integer
Maximum number of variables permitted on the coarse grid.
diagonal_dominance : bool, tuple
If True (or the first tuple entry is True), then avoid coarsening
diagonally dominant rows. The second tuple entry requires a
dictionary, where the key value 'theta' is used to tune the diagonal
dominance threshold.
keep : bool
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C),
tentative prolongation (T), and aggregation (AggOp) are kept.
Other Parameters
----------------
cycle_type : ['V','W','F']
Structrure of multigrid cycle
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
See Also
--------
multilevel_solver, classical.ruge_stuben_solver,
aggregation.smoothed_aggregation_solver
Notes
-----
- This method implements classical-style SA, not root-node style SA
(see aggregation.rootnode_solver).
- The additional parameters are passed through as arguments to
multilevel_solver. Refer to pyamg.multilevel_solver for additional
documentation.
- At each level, four steps are executed in order to define the coarser
level operator.
1. Matrix A is given and used to derive a strength matrix, C.
2. Based on the strength matrix, indices are grouped or aggregated.
3. The aggregates define coarse nodes and a tentative prolongation
operator T is defined by injection
4. The tentative prolongation operator is smoothed by a relaxation
scheme to improve the quality and extent of interpolation from the
aggregates to fine nodes.
- The parameters smooth, strength, aggregate, presmoother, postsmoother
can be varied on a per level basis. For different methods on
different levels, use a list as input so that the i-th entry defines
the method at the i-th level. If there are more levels in the
hierarchy than list entries, the last entry will define the method
for all levels lower.
Examples are:
smooth=[('jacobi', {'omega':1.0}), None, 'jacobi']
presmoother=[('block_gauss_seidel', {'sweep':symmetric}), 'sor']
aggregate=['standard', 'naive']
strength=[('symmetric', {'theta':0.25}), ('symmetric', {'theta':0.08})]
- Predefined strength of connection and aggregation schemes can be
specified. These options are best used together, but aggregation can
be predefined while strength of connection is not.
For predefined strength of connection, use a list consisting of
tuples of the form ('predefined', {'C' : C0}), where C0 is a
csr_matrix and each degree-of-freedom in C0 represents a supernode.
For instance to predefine a three-level hierarchy, use
[('predefined', {'C' : C0}), ('predefined', {'C' : C1}) ].
Similarly for predefined aggregation, use a list of tuples. For
instance to predefine a three-level hierarchy, use [('predefined',
{'AggOp' : Agg0}), ('predefined', {'AggOp' : Agg1}) ], where the
dimensions of A, Agg0 and Agg1 are compatible, i.e. Agg0.shape[1] ==
A.shape[0] and Agg1.shape[1] == Agg0.shape[0]. Each AggOp is a
csr_matrix.
Examples
--------
>>> from pyamg import smoothed_aggregation_solver
>>> from pyamg.gallery import poisson
>>> from scipy.sparse.linalg import cg
>>> import numpy as np
>>> A = poisson((100,100), format='csr') # matrix
>>> b = np.ones((A.shape[0])) # RHS
>>> ml = smoothed_aggregation_solver(A) # AMG solver
>>> M = ml.aspreconditioner(cycle='V') # preconditioner
>>> x,info = cg(A, b, tol=1e-8, maxiter=30, M=M) # solve with CG
References
----------
.. [1996VaMaBr] Vanek, P. and Mandel, J. and Brezina, M.,
"Algebraic Multigrid by Smoothed Aggregation for
Second and Fourth Order Elliptic Problems",
Computing, vol. 56, no. 3, pp. 179--196, 1996.
http://citeseer.ist.psu.edu/vanek96algebraic.html
"""
if not (isspmatrix_csr(A) or isspmatrix_bsr(A)):
try:
A = csr_matrix(A)
warn("Implicit conversion of A to CSR", SparseEfficiencyWarning)
except BaseException:
raise TypeError(
'Argument A must have type csr_matrix or bsr_matrix, or be convertible to csr_matrix'
)
A = A.asfptype()
if (symmetry != 'symmetric') and (symmetry != 'hermitian') and\
(symmetry != 'nonsymmetric'):
raise ValueError(
'expected \'symmetric\', \'nonsymmetric\' or \'hermitian\' for the symmetry parameter '
)
A.symmetry = symmetry
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
# Right near nullspace candidates use constant for each variable as default
if B is None:
B = np.kron(
np.ones((int(A.shape[0] / blocksize(A)), 1), dtype=A.dtype),
np.eye(blocksize(A), dtype=A.dtype))
else:
B = np.asarray(B, dtype=A.dtype)
if len(B.shape) == 1:
B = B.reshape(-1, 1)
if B.shape[0] != A.shape[0]:
raise ValueError(
'The near null-space modes B have incorrect dimensions for matrix A'
)
if B.shape[1] < blocksize(A):
warn(
'Having less target vectors, B.shape[1], than blocksize of A can degrade convergence factors.'
)
# Left near nullspace candidates
if A.symmetry == 'nonsymmetric':
if BH is None:
BH = B.copy()
else:
BH = np.asarray(BH, dtype=A.dtype)
if len(BH.shape) == 1:
BH = BH.reshape(-1, 1)
if BH.shape[1] != B.shape[1]:
raise ValueError(
'The number of left and right near null-space modes B and BH, must be equal'
)
if BH.shape[0] != A.shape[0]:
raise ValueError(
'The near null-space modes BH have incorrect dimensions for matrix A'
)
# Levelize the user parameters, so that they become lists describing the
# desired user option on each level.
max_levels, max_coarse, strength =\
levelize_strength_or_aggregation(strength, max_levels, max_coarse)
max_levels, max_coarse, aggregate =\
levelize_strength_or_aggregation(aggregate, max_levels, max_coarse)
improve_candidates =\
levelize_smooth_or_improve_candidates(improve_candidates, max_levels)
smooth = levelize_smooth_or_improve_candidates(smooth, max_levels)
# Construct multilevel structure
levels = []
levels.append(multilevel_solver.level())
levels[-1].A = A # matrix
# Append near nullspace candidates
levels[-1].B = B # right candidates
if A.symmetry == 'nonsymmetric':
levels[-1].BH = BH # left candidates
while len(levels) < max_levels and\
int(levels[-1].A.shape[0]/blocksize(levels[-1].A)) > max_coarse:
extend_hierarchy(levels, strength, aggregate, smooth,
improve_candidates, diagonal_dominance, keep)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def extend_hierarchy(levels, strength, CF, interp, keep):
""" helper function for local methods """
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
A = levels[-1].A
block_starts = levels[-1].block_starts
verts = levels[-1].verts
# If this is a system, apply the unknown approach by coarsening and generating interpolation based on each diagonal block of A
if (block_starts):
A_diag = extract_diagonal_blocks(A, block_starts)
else:
A_diag = [A]
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
C_diag = []
P_diag = []
splitting = []
next_lvl_block_starts = [0]
block_cnt = 0
for mat in A_diag:
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C_diag.append( symmetric_strength_of_connection(mat, **kwargs) )
elif fn == 'classical':
C_diag.append( classical_strength_of_connection(mat, **kwargs) )
elif fn == 'distance':
C_diag.append( distance_strength_of_connection(mat, **kwargs) )
elif (fn == 'ode') or (fn == 'evolution'):
C_diag.append( evolution_strength_of_connection(mat, **kwargs) )
elif fn == 'energy_based':
C_diag.append( energy_based_strength_of_connection(mat, **kwargs) )
elif fn == 'algebraic_distance':
C_diag.append( algebraic_distance(mat, **kwargs) )
elif fn == 'affinity':
C_diag.append( affinity_distance(mat, **kwargs) )
elif fn is None:
C_diag.append( mat )
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'RS':
splitting.append( split.RS(C_diag[-1]) )
elif fn == 'PMIS':
splitting.append( split.PMIS(C_diag[-1]) )
elif fn == 'PMISc':
splitting.append( split.PMISc(C_diag[-1]) )
elif fn == 'CLJP':
splitting.append( split.CLJP(C_diag[-1]) )
elif fn == 'CLJPc':
splitting.append( split.CLJPc(C_diag[-1]) )
elif fn == 'Shifted2DCoarsening':
splitting.append( split.Shifted2DCoarsening(C_diag[-1]) )
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
fn, kwargs = unpack_arg(interp)
if fn == 'standard':
P_diag.append( standard_interpolation(mat, C_diag[-1], splitting[-1]) )
elif fn == 'direct':
P_diag.append( direct_interpolation(mat, C_diag[-1], splitting[-1]) )
else:
raise ValueError('unknown interpolation method (%s)' % interp)
next_lvl_block_starts.append( next_lvl_block_starts[-1] + P_diag[-1].shape[1])
block_cnt = block_cnt + 1
P = block_diag(P_diag)
# Generate the restriction matrix that maps from the fine-grid to the
# coarse-grid
R = P.T.tocsr()
# Store relevant information for this level
splitting = numpy.concatenate(splitting)
if keep:
C = block_diag(C_diag)
levels[-1].C = C # strength of connection matrix
levels[-1].splitting = splitting # C/F splitting
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels.append(multilevel_solver.level())
# Form next level through Galerkin product
# !!! For systems, how do I propogate the block structure information down to the next grid? Especially if the blocks are different sizes? !!!
A = R * A * P
levels[-1].A = A
if (block_starts):
levels[-1].block_starts = next_lvl_block_starts
else:
levels[-1].block_starts = None
# If called for, output a visualization of the C/F splitting
if (verts.any()):
new_verts = numpy.empty([P.shape[1], 2])
cnt = 0
for i in range(len(splitting)):
if (splitting[i]):
new_verts[cnt] = verts[i]
cnt = cnt + 1
levels[-1].verts = new_verts
else:
levels[-1].verts = numpy.zeros(1)
def ruge_stuben_solver(A,
strength=('classical', {
'theta': 0.25
}),
CF='RS',
presmoother=('gauss_seidel', {
'sweep': 'symmetric'
}),
postsmoother=('gauss_seidel', {
'sweep': 'symmetric'
}),
max_levels=10,
max_coarse=500,
keep=False,
**kwargs):
"""Create a multilevel solver using Classical AMG (Ruge-Stuben AMG)
Parameters
----------
A : csr_matrix
Square matrix in CSR format
strength : ['symmetric', 'classical', 'evolution', None]
Method used to determine the strength of connection between unknowns
of the linear system. Method-specific parameters may be passed in
using a tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If
strength=None, all nonzero entries of the matrix are considered strong.
CF : {string} : default 'RS'
Method used for coarse grid selection (C/F splitting)
Supported methods are RS, PMIS, PMISc, CLJP, and CLJPc
presmoother : {string or dict}
Method used for presmoothing at each level. Method-specific parameters
may be passed in using a tuple, e.g.
presmoother=('gauss_seidel',{'sweep':'symmetric}), the default.
postsmoother : {string or dict}
Postsmoothing method with the same usage as presmoother
max_levels: {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse: {integer} : default 500
Maximum number of variables permitted on the coarse grid.
keep: {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C) and
tentative prolongation (T) are kept.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
Examples
--------
>>> from pyamg.gallery import poisson
>>> from pyamg import ruge_stuben_solver
>>> A = poisson((10,),format='csr')
>>> ml = ruge_stuben_solver(A,max_coarse=3)
Notes
-----
"coarse_solver" is an optional argument and is the solver used at the
coarsest grid. The default is a pseudo-inverse. Most simply,
coarse_solver can be one of ['splu', 'lu', 'cholesky, 'pinv',
'gauss_seidel', ... ]. Additionally, coarse_solver may be a tuple
(fn, args), where fn is a string such as ['splu', 'lu', ...] or a callable
function, and args is a dictionary of arguments to be passed to fn.
References
----------
.. [1] Trottenberg, U., Oosterlee, C. W., and Schuller, A.,
"Multigrid" San Diego: Academic Press, 2001. Appendix A
See Also
--------
aggregation.smoothed_aggregation_solver, multilevel_solver,
aggregation.rootnode_solver
"""
levels = [multilevel_solver.level()]
# convert A to csr
if not isspmatrix_csr(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 be convertible to csr_matrix')
# preprocess A
A = A.asfptype()
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
levels[-1].A = A
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
extend_hierarchy(levels, strength, CF, keep)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def extend_hierarchy(levels, strength, CF, interpolation, restriction, keep):
""" helper function for local methods """
A = levels[-1].A
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
C = evolution_strength_of_connection(A, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
levels[-1].complexity['strength'] = kwargs['cost'][0]
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'RS':
splitting = split.RS(C, **kwargs)
elif fn == 'PMIS':
splitting = split.PMIS(C, **kwargs)
elif fn == 'PMISc':
splitting = split.PMISc(C, **kwargs)
elif fn == 'CLJP':
splitting = split.CLJP(C, **kwargs)
elif fn == 'CLJPc':
splitting = split.CLJPc(C, **kwargs)
elif fn == 'CR':
splitting = CR(C, **kwargs)
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
levels[-1].complexity['CF'] = kwargs['cost'][0]
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
fn, kwargs = unpack_arg(interpolation)
if fn == 'standard':
P = standard_interpolation(A, C, splitting, **kwargs)
elif fn == 'distance_two':
P = distance_two_interpolation(A, C, splitting, **kwargs)
elif fn == 'direct':
P = direct_interpolation(A, C, splitting, **kwargs)
elif fn == 'one_point':
P = one_point_interpolation(A, C, splitting, **kwargs)
elif fn == 'injection':
P = injection_interpolation(A, splitting, **kwargs)
else:
raise ValueError('unknown interpolation method (%s)' % interpolation)
levels[-1].complexity['interpolate'] = kwargs['cost'][0]
# Generate the restriction matrix that maps from the fine-grid to the
# coarse-grid. Must make sure transpose matrices remain in CSR or BSR
fn, kwargs = unpack_arg(restriction)
if isspmatrix_csr(A):
if restriction == 'galerkin':
R = P.T.tocsr()
elif fn == 'standard':
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'distance_two':
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'direct':
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R = one_point_interpolation(A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'injection': # Don't need A^T or C^T here
R = injection_interpolation(A, splitting, **kwargs)
R = R.T.tocsr()
else:
raise ValueError('unknown interpolation method (%s)' %
interpolation)
else:
if restriction == 'galerkin':
R = P.T.tobsr()
elif fn == 'standard':
temp_A = A.T.tobsr()
temp_C = C.T.tocsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'distance_two':
temp_A = A.T.tobsr()
temp_C = C.T.tocsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'direct':
temp_A = A.T.tobsr()
temp_C = C.T.tocsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R = one_point_interpolation(A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'injection': # Don't need A^T or C^T here
R = injection_interpolation(A, splitting, **kwargs)
R = R.T.tobsr()
else:
raise ValueError('unknown interpolation method (%s)' %
interpolation)
levels[-1].complexity['restriction'] = kwargs['cost'][0]
# Store relevant information for this level
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels[-1].splitting = splitting # C/F splitting
# Form coarse grid operator, get complexity
levels[-1].complexity['RAP'] = mat_mat_complexity(R, A) / float(A.nnz)
RA = R * A
levels[-1].complexity['RAP'] += mat_mat_complexity(RA, P) / float(A.nnz)
A = RA * P # Galerkin operator, Ac = RAP
# Make sure coarse-grid operator is in correct sparse format
if (isspmatrix_csr(P) and (not isspmatrix_csr(A))):
A = A.tocsr()
elif (isspmatrix_bsr(P) and (not isspmatrix_bsr(A))):
A = A.tobsr()
# Form next level through Galerkin product
levels.append(multilevel_solver.level())
levels[-1].A = A
def extend_hierarchy(levels, strength, aggregate, smooth, improve_candidates,
diagonal_dominance=False, keep=True):
"""Service routine to implement the strength of connection, aggregation,
tentative prolongation construction, and prolongation smoothing. Called by
smoothed_aggregation_solver.
"""
A = levels[-1].A
B = levels[-1].B
if A.symmetry == "nonsymmetric":
AH = A.H.asformat(A.format)
BH = levels[-1].BH
# Compute the strength-of-connection matrix C, where larger
# C[i, j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength[len(levels)-1])
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
if 'B' in kwargs:
C = evolution_strength_of_connection(A, **kwargs)
else:
C = evolution_strength_of_connection(A, B, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'predefined':
C = kwargs['C'].tocsr()
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A.tocsr()
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
levels[-1].complexity['strength'] = kwargs['cost'][0]
# Avoid coarsening diagonally dominant rows
flag, kwargs = unpack_arg(diagonal_dominance)
if flag:
C = eliminate_diag_dom_nodes(A, C, **kwargs)
levels[-1].complexity['diag_dom'] = kwargs['cost'][0]
# Compute the aggregation matrix AggOp (i.e., the nodal coarsening of A).
# AggOp is a boolean matrix, where the sparsity pattern for the k-th column
# denotes the fine-grid nodes agglomerated into k-th coarse-grid node.
fn, kwargs = unpack_arg(aggregate[len(levels)-1])
if fn == 'standard':
AggOp, Cnodes = standard_aggregation(C, **kwargs)
elif fn == 'naive':
AggOp, Cnodes = naive_aggregation(C, **kwargs)
elif fn == 'lloyd':
AggOp, Cnodes = lloyd_aggregation(C, **kwargs)
elif fn == 'predefined':
AggOp = kwargs['AggOp'].tocsr()
Cnodes = kwargs['Cnodes']
else:
raise ValueError('unrecognized aggregation method %s' % str(fn))
levels[-1].complexity['aggregation'] = kwargs['cost'][0] * (float(C.nnz)/A.nnz)
# Improve near nullspace candidates by relaxing on A B = 0
temp_cost = [0.0]
fn, kwargs = unpack_arg(improve_candidates[len(levels)-1],cost=False)
if fn is not None:
b = np.zeros((A.shape[0], 1), dtype=A.dtype)
B = relaxation_as_linear_operator((fn, kwargs), A, b, temp_cost) * B
levels[-1].B = B
if A.symmetry == "nonsymmetric":
BH = relaxation_as_linear_operator((fn, kwargs), AH, b, temp_cost) * BH
levels[-1].BH = BH
levels[-1].complexity['candidates'] = temp_cost[0] * B.shape[1]
# Compute the tentative prolongator, T, which is a tentative interpolation
# matrix from the coarse-grid to the fine-grid. T exactly interpolates
# B_fine[:, 0:blocksize(A)] = T B_coarse[:, 0:blocksize(A)].
# Orthogonalization complexity ~ 2nk^2, k = blocksize(A).
temp_cost=[0.0]
T, dummy = fit_candidates(AggOp, B[:, 0:blocksize(A)], cost=temp_cost)
del dummy
if A.symmetry == "nonsymmetric":
TH, dummyH = fit_candidates(AggOp, BH[:, 0:blocksize(A)], cost=temp_cost)
del dummyH
levels[-1].complexity['tentative'] = temp_cost[0]/A.nnz
# Create necessary root node matrices
Cpt_params = (True, get_Cpt_params(A, Cnodes, AggOp, T))
T = scale_T(T, Cpt_params[1]['P_I'], Cpt_params[1]['I_F'])
levels[-1].complexity['tentative'] += T.nnz / float(A.nnz)
if A.symmetry == "nonsymmetric":
TH = scale_T(TH, Cpt_params[1]['P_I'], Cpt_params[1]['I_F'])
levels[-1].complexity['tentative'] += TH.nnz / float(A.nnz)
# Set coarse grid near nullspace modes as injected fine grid near
# null-space modes
B = Cpt_params[1]['P_I'].T*levels[-1].B
if A.symmetry == "nonsymmetric":
BH = Cpt_params[1]['P_I'].T*levels[-1].BH
# Smooth the tentative prolongator, so that it's accuracy is greatly
# improved for algebraically smooth error.
fn, kwargs = unpack_arg(smooth[len(levels)-1])
if fn == 'energy':
P = energy_prolongation_smoother(A, T, C, B, levels[-1].B,
Cpt_params=Cpt_params, **kwargs)
elif fn is None:
P = T
else:
raise ValueError('unrecognized prolongation smoother \
method %s' % str(fn))
levels[-1].complexity['smooth_P'] = kwargs['cost'][0]
# Compute the restriction matrix R, which interpolates from the fine-grid
# to the coarse-grid. If A is nonsymmetric, then R must be constructed
# based on A.H. Otherwise R = P.H or P.T.
symmetry = A.symmetry
if symmetry == 'hermitian':
R = P.H
elif symmetry == 'symmetric':
R = P.T
elif symmetry == 'nonsymmetric':
fn, kwargs = unpack_arg(smooth[len(levels)-1])
if fn == 'energy':
R = energy_prolongation_smoother(AH, TH, C, BH, levels[-1].BH,
Cpt_params=Cpt_params, **kwargs)
R = R.H
levels[-1].complexity['smooth_R'] = kwargs['cost'][0]
elif fn is None:
R = T.H
else:
raise ValueError('unrecognized prolongation smoother \
method %s' % str(fn))
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].AggOp = AggOp # aggregation operator
levels[-1].T = T # tentative prolongator
levels[-1].Fpts = Cpt_params[1]['Fpts'] # Fpts
levels[-1].P_I = Cpt_params[1]['P_I'] # Injection operator
levels[-1].I_F = Cpt_params[1]['I_F'] # Identity on F-pts
levels[-1].I_C = Cpt_params[1]['I_C'] # Identity on C-pts
levels[-1].P = P # smoothed prolongator
levels[-1].R = R # restriction operator
levels[-1].Cpts = Cpt_params[1]['Cpts'] # Cpts (i.e., rootnodes)
# Form coarse grid operator, get complexity
levels[-1].complexity['RAP'] = mat_mat_complexity(R,A) / float(A.nnz)
RA = R * A
levels[-1].complexity['RAP'] += mat_mat_complexity(RA,P) / float(A.nnz)
A = RA * P # Galerkin operator, Ac = RAP
A.symmetry = symmetry
levels.append(multilevel_solver.level())
levels[-1].A = A
levels[-1].B = B # right near nullspace candidates
if A.symmetry == "nonsymmetric":
levels[-1].BH = BH # left near nullspace candidates
def ruge_stuben_solver(A,
strength=('classical', {
'theta': 0.25
}),
CF='RS',
interpolation='direct',
restriction='galerkin',
presmoother=('gauss_seidel', {
'sweep': 'symmetric'
}),
postsmoother=('gauss_seidel', {
'sweep': 'symmetric'
}),
max_levels=10,
max_coarse=10,
keep=False,
**kwargs):
"""Create a multilevel solver using Classical AMG (Ruge-Stuben AMG)
Parameters
----------
A : csr_matrix
Square matrix in CSR format
strength : ['symmetric', 'classical', 'evolution', 'distance',
'algebraic_distance','affinity', 'energy_based', None]
Method used to determine the strength of connection between unknowns
of the linear system. Method-specific parameters may be passed in
using a tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If
strength=None, all nonzero entries of the matrix are considered strong.
CF : {string} : default 'RS'
Method used for coarse grid selection (C/F splitting)
Supported methods are RS, PMIS, PMISc, CLJP, CLJPc, and CR.
interpolation : {string} : default 'direct'
Method for interpolation. Options include 'direct', 'standard', 'injection',
'one_point', and 'distance_two'.
restriction : {string or dict} : default 'galerkin'
'Galerkin' means set R := P^T for a Galerkin coarse-grid operator. Can also specify
an interpolation method as above, to build the restriciton operator based on A^T.
presmoother : {string or dict}
Method used for presmoothing at each level. Method-specific parameters
may be passed in using a tuple, e.g.
presmoother=('gauss_seidel',{'sweep':'symmetric}), the default.
postsmoother : {string or dict}
Postsmoothing method with the same usage as presmoother
max_levels: {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse: {integer} : default 500
Maximum number of variables permitted on the coarse grid.
keep: {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C) and
tentative prolongation (T) are kept.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
Other Parameters
----------------
cycle_type : ['V','W','F']
Structrure of multigrid cycle
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
setup_complexity : bool
For a detailed, more accurate setup complexity, pass in
'setup_complexity' = True. This will slow down performance, but
increase accuracy of complexity count.
Examples
--------
>>> from pyamg.gallery import poisson
>>> from pyamg import ruge_stuben_solver
>>> A = poisson((10,),format='csr')
>>> ml = ruge_stuben_solver(A,max_coarse=3)
Notes
-----
Standard interpolation is generally considered more robust than
direct, but direct is the currently the default until our new
implementation of standard has been more rigorously tested.
"coarse_solver" is an optional argument and is the solver used at the
coarsest grid. The default is a pseudo-inverse. Most simply,
coarse_solver can be one of ['splu', 'lu', 'cholesky, 'pinv',
'gauss_seidel', ... ]. Additionally, coarse_solver may be a tuple
(fn, args), where fn is a string such as ['splu', 'lu', ...] or a callable
function, and args is a dictionary of arguments to be passed to fn.
References
----------
.. [1] Trottenberg, U., Oosterlee, C. W., and Schuller, A.,
"Multigrid" San Diego: Academic Press, 2001. Appendix A
See Also
--------
aggregation.smoothed_aggregation_solver, multilevel_solver,
aggregation.rootnode_solver
"""
if ('setup_complexity' in kwargs):
if kwargs['setup_complexity'] == True:
mat_mat_complexity.__detailed__ = True
del kwargs['setup_complexity']
# Convert A to csr
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, bsr_matrix, \
or be convertible to csr_matrix')
# if isspmatrix_bsr(A):
# warn("Classical AMG is often more effective on CSR matrices.")
# preprocess A
A = A.asfptype()
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
levels = [multilevel_solver.level()]
levels[-1].A = A
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
extend_hierarchy(levels, strength, CF, interpolation, restriction,
keep)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def extend_hierarchy(levels, strength, aggregate, smooth, improve_candidates, diagonal_dominance=False, keep=True):
"""Service routine to implement the strength of connection, aggregation,
tentative prolongation construction, and prolongation smoothing. Called by
smoothed_aggregation_solver.
"""
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
A = levels[-1].A
B = levels[-1].B
if A.symmetry == "nonsymmetric":
AH = A.H.asformat(A.format)
BH = levels[-1].BH
# Compute the strength-of-connection matrix C, where larger
# C[i, j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength[len(levels) - 1])
if fn == "symmetric":
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == "classical":
C = classical_strength_of_connection(A, **kwargs)
elif fn == "distance":
C = distance_strength_of_connection(A, **kwargs)
elif (fn == "ode") or (fn == "evolution"):
if "B" in kwargs:
C = evolution_strength_of_connection(A, **kwargs)
else:
C = evolution_strength_of_connection(A, B, **kwargs)
elif fn == "energy_based":
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == "predefined":
C = kwargs["C"].tocsr()
elif fn == "algebraic_distance":
C = algebraic_distance(A, **kwargs)
elif fn is None:
C = A.tocsr()
else:
raise ValueError("unrecognized strength of connection method: %s" % str(fn))
# Avoid coarsening diagonally dominant rows
flag, kwargs = unpack_arg(diagonal_dominance)
if flag:
C = eliminate_diag_dom_nodes(A, C, **kwargs)
# Compute the aggregation matrix AggOp (i.e., the nodal coarsening of A).
# AggOp is a boolean matrix, where the sparsity pattern for the k-th column
# denotes the fine-grid nodes agglomerated into k-th coarse-grid node.
fn, kwargs = unpack_arg(aggregate[len(levels) - 1])
if fn == "standard":
AggOp, Cnodes = standard_aggregation(C, **kwargs)
elif fn == "naive":
AggOp, Cnodes = naive_aggregation(C, **kwargs)
elif fn == "lloyd":
AggOp, Cnodes = lloyd_aggregation(C, **kwargs)
elif fn == "predefined":
AggOp = kwargs["AggOp"].tocsr()
Cnodes = kwargs["Cnodes"]
else:
raise ValueError("unrecognized aggregation method %s" % str(fn))
# Improve near nullspace candidates by relaxing on A B = 0
fn, kwargs = unpack_arg(improve_candidates[len(levels) - 1])
if fn is not None:
b = np.zeros((A.shape[0], 1), dtype=A.dtype)
B = relaxation_as_linear_operator((fn, kwargs), A, b) * B
levels[-1].B = B
if A.symmetry == "nonsymmetric":
BH = relaxation_as_linear_operator((fn, kwargs), AH, b) * BH
levels[-1].BH = BH
# Compute the tentative prolongator, T, which is a tentative interpolation
# matrix from the coarse-grid to the fine-grid. T exactly interpolates
# B_fine[:, 0:blocksize(A)] = T B_coarse[:, 0:blocksize(A)].
T, dummy = fit_candidates(AggOp, B[:, 0 : blocksize(A)])
del dummy
if A.symmetry == "nonsymmetric":
TH, dummyH = fit_candidates(AggOp, BH[:, 0 : blocksize(A)])
del dummyH
# Create necessary root node matrices
Cpt_params = (True, get_Cpt_params(A, Cnodes, AggOp, T))
T = scale_T(T, Cpt_params[1]["P_I"], Cpt_params[1]["I_F"])
if A.symmetry == "nonsymmetric":
TH = scale_T(TH, Cpt_params[1]["P_I"], Cpt_params[1]["I_F"])
# Set coarse grid near nullspace modes as injected fine grid near
# null-space modes
B = Cpt_params[1]["P_I"].T * levels[-1].B
if A.symmetry == "nonsymmetric":
BH = Cpt_params[1]["P_I"].T * levels[-1].BH
# Smooth the tentative prolongator, so that it's accuracy is greatly
# improved for algebraically smooth error.
fn, kwargs = unpack_arg(smooth[len(levels) - 1])
if fn == "energy":
P = energy_prolongation_smoother(A, T, C, B, levels[-1].B, Cpt_params=Cpt_params, **kwargs)
elif fn is None:
P = T
else:
raise ValueError(
"unrecognized prolongation smoother \
method %s"
% str(fn)
)
# Compute the restriction matrix R, which interpolates from the fine-grid
# to the coarse-grid. If A is nonsymmetric, then R must be constructed
# based on A.H. Otherwise R = P.H or P.T.
symmetry = A.symmetry
if symmetry == "hermitian":
R = P.H
elif symmetry == "symmetric":
R = P.T
elif symmetry == "nonsymmetric":
fn, kwargs = unpack_arg(smooth[len(levels) - 1])
if fn == "energy":
R = energy_prolongation_smoother(AH, TH, C, BH, levels[-1].BH, Cpt_params=Cpt_params, **kwargs)
R = R.H
elif fn is None:
R = T.H
else:
raise ValueError(
"unrecognized prolongation smoother \
method %s"
% str(fn)
)
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].AggOp = AggOp # aggregation operator
levels[-1].T = T # tentative prolongator
levels[-1].Fpts = Cpt_params[1]["Fpts"] # Fpts
levels[-1].P_I = Cpt_params[1]["P_I"] # Injection operator
levels[-1].I_F = Cpt_params[1]["I_F"] # Identity on F-pts
levels[-1].I_C = Cpt_params[1]["I_C"] # Identity on C-pts
levels[-1].P = P # smoothed prolongator
levels[-1].R = R # restriction operator
levels[-1].Cpts = Cpt_params[1]["Cpts"] # Cpts (i.e., rootnodes)
levels.append(multilevel_solver.level())
A = R * A * P # Galerkin operator
A.symmetry = symmetry
levels[-1].A = A
levels[-1].B = B # right near nullspace candidates
if A.symmetry == "nonsymmetric":
levels[-1].BH = BH # left near nullspace candidates
def extend_hierarchy(levels, strength, CF, interpolation, restriction, keep):
""" helper function for local methods """
A = levels[-1].A
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
C = evolution_strength_of_connection(A, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
levels[-1].complexity['strength'] = kwargs['cost'][0]
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'RS':
splitting = split.RS(C, **kwargs)
elif fn == 'PMIS':
splitting = split.PMIS(C, **kwargs)
elif fn == 'PMISc':
splitting = split.PMISc(C, **kwargs)
elif fn == 'CLJP':
splitting = split.CLJP(C, **kwargs)
elif fn == 'CLJPc':
splitting = split.CLJPc(C, **kwargs)
elif fn == 'CR':
splitting = CR(C, **kwargs)
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
levels[-1].complexity['CF'] = kwargs['cost'][0]
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
fn, kwargs = unpack_arg(interpolation)
if fn == 'standard':
P = standard_interpolation(A, C, splitting, **kwargs)
elif fn == 'distance_two':
P = distance_two_interpolation(A, C, splitting, **kwargs)
elif fn == 'direct':
P = direct_interpolation(A, C, splitting, **kwargs)
elif fn == 'one_point':
P = one_point_interpolation(A, C, splitting, **kwargs)
elif fn == 'injection':
P = injection_interpolation(A, splitting, **kwargs)
else:
raise ValueError('unknown interpolation method (%s)' % interpolation)
levels[-1].complexity['interpolate'] = kwargs['cost'][0]
# Generate the restriction matrix that maps from the fine-grid to the
# coarse-grid. Must make sure transpose matrices remain in CSR or BSR
fn, kwargs = unpack_arg(restriction)
if isspmatrix_csr(A):
if restriction == 'galerkin':
R = P.T.tocsr()
elif fn == 'standard':
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'distance_two':
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'direct':
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R = one_point_interpolation(A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'injection': # Don't need A^T or C^T here
R = injection_interpolation(A, splitting, **kwargs)
R = R.T.tocsr()
else:
raise ValueError('unknown interpolation method (%s)' % interpolation)
else:
if restriction == 'galerkin':
R = P.T.tobsr()
elif fn == 'standard':
temp_A = A.T.tobsr()
temp_C = C.T.tocsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'distance_two':
temp_A = A.T.tobsr()
temp_C = C.T.tocsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'direct':
temp_A = A.T.tobsr()
temp_C = C.T.tocsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R = one_point_interpolation(A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
elif fn == 'injection': # Don't need A^T or C^T here
R = injection_interpolation(A, splitting, **kwargs)
R = R.T.tobsr()
else:
raise ValueError('unknown interpolation method (%s)' % interpolation)
levels[-1].complexity['restriction'] = kwargs['cost'][0]
# Store relevant information for this level
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels[-1].splitting = splitting # C/F splitting
# Form coarse grid operator, get complexity
levels[-1].complexity['RAP'] = mat_mat_complexity(R,A) / float(A.nnz)
RA = R * A
levels[-1].complexity['RAP'] += mat_mat_complexity(RA,P) / float(A.nnz)
A = RA * P # Galerkin operator, Ac = RAP
# Make sure coarse-grid operator is in correct sparse format
if (isspmatrix_csr(P) and (not isspmatrix_csr(A))):
A = A.tocsr()
elif (isspmatrix_bsr(P) and (not isspmatrix_bsr(A))):
A = A.tobsr()
# Form next level through Galerkin product
levels.append(multilevel_solver.level())
levels[-1].A = A
def extend_hierarchy(levels, strength, CF, keep):
""" helper function for local methods """
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
A = levels[-1].A
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
C = evolution_strength_of_connection(A, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn is None:
C = A
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'RS':
splitting = split.RS(C)
elif fn == 'PMIS':
splitting = split.PMIS(C)
elif fn == 'PMISc':
splitting = split.PMISc(C)
elif fn == 'CLJP':
splitting = split.CLJP(C)
elif fn == 'CLJPc':
splitting = split.CLJPc(C)
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
P = direct_interpolation(A, C, splitting)
# Generate the restriction matrix that maps from the fine-grid to the
# coarse-grid
R = P.T.tocsr()
# Store relevant information for this level
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].splitting = splitting # C/F splitting
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels.append(multilevel_solver.level())
# Form next level through Galerkin product
A = R * A * P
levels[-1].A = A
def ruge_stuben_solver(A,
strength=('classical', {'theta': 0.25}),
CF='RS',
interpolation='direct',
restriction='galerkin',
presmoother=('gauss_seidel', {'sweep': 'symmetric'}),
postsmoother=('gauss_seidel', {'sweep': 'symmetric'}),
max_levels=10, max_coarse=10, keep=False, **kwargs):
"""Create a multilevel solver using Classical AMG (Ruge-Stuben AMG)
Parameters
----------
A : csr_matrix
Square matrix in CSR format
strength : ['symmetric', 'classical', 'evolution', 'distance',
'algebraic_distance','affinity', 'energy_based', None]
Method used to determine the strength of connection between unknowns
of the linear system. Method-specific parameters may be passed in
using a tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If
strength=None, all nonzero entries of the matrix are considered strong.
CF : {string} : default 'RS'
Method used for coarse grid selection (C/F splitting)
Supported methods are RS, PMIS, PMISc, CLJP, CLJPc, and CR.
interpolation : {string} : default 'direct'
Method for interpolation. Options include 'direct', 'standard', 'injection',
'one_point', and 'distance_two'.
restriction : {string or dict} : default 'galerkin'
'Galerkin' means set R := P^T for a Galerkin coarse-grid operator. Can also specify
an interpolation method as above, to build the restriciton operator based on A^T.
presmoother : {string or dict}
Method used for presmoothing at each level. Method-specific parameters
may be passed in using a tuple, e.g.
presmoother=('gauss_seidel',{'sweep':'symmetric}), the default.
postsmoother : {string or dict}
Postsmoothing method with the same usage as presmoother
max_levels: {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse: {integer} : default 500
Maximum number of variables permitted on the coarse grid.
keep: {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C) and
tentative prolongation (T) are kept.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
Other Parameters
----------------
cycle_type : ['V','W','F']
Structrure of multigrid cycle
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
setup_complexity : bool
For a detailed, more accurate setup complexity, pass in
'setup_complexity' = True. This will slow down performance, but
increase accuracy of complexity count.
Examples
--------
>>> from pyamg.gallery import poisson
>>> from pyamg import ruge_stuben_solver
>>> A = poisson((10,),format='csr')
>>> ml = ruge_stuben_solver(A,max_coarse=3)
Notes
-----
Standard interpolation is generally considered more robust than
direct, but direct is the currently the default until our new
implementation of standard has been more rigorously tested.
"coarse_solver" is an optional argument and is the solver used at the
coarsest grid. The default is a pseudo-inverse. Most simply,
coarse_solver can be one of ['splu', 'lu', 'cholesky, 'pinv',
'gauss_seidel', ... ]. Additionally, coarse_solver may be a tuple
(fn, args), where fn is a string such as ['splu', 'lu', ...] or a callable
function, and args is a dictionary of arguments to be passed to fn.
References
----------
.. [1] Trottenberg, U., Oosterlee, C. W., and Schuller, A.,
"Multigrid" San Diego: Academic Press, 2001. Appendix A
See Also
--------
aggregation.smoothed_aggregation_solver, multilevel_solver,
aggregation.rootnode_solver
"""
if ('setup_complexity' in kwargs):
if kwargs['setup_complexity'] == True:
mat_mat_complexity.__detailed__ = True
del kwargs['setup_complexity']
# Convert A to csr
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, bsr_matrix, \
or be convertible to csr_matrix')
# if isspmatrix_bsr(A):
# warn("Classical AMG is often more effective on CSR matrices.")
# preprocess A
A = A.asfptype()
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
levels = [multilevel_solver.level()]
levels[-1].A = A
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
extend_hierarchy(levels, strength, CF, interpolation, restriction, keep)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def smoothed_aggregation_helmholtz_solver(A,
planewaves,
use_constant=(True, {
'last_level': 0
}),
symmetry='symmetric',
strength='symmetric',
aggregate='standard',
smooth=('energy', {
'krylov': 'gmres'
}),
presmoother=('gauss_seidel_nr', {
'sweep': 'symmetric'
}),
postsmoother=('gauss_seidel_nr', {
'sweep': 'symmetric'
}),
improve_candidates='default',
max_levels=10,
max_coarse=100,
**kwargs):
"""
Create a multilevel solver using Smoothed Aggregation (SA) for a 2D Helmholtz operator
Parameters
----------
A : {csr_matrix, bsr_matrix}
Sparse NxN matrix in CSR or BSR format
planewaves : { list }
[pw_0, pw_1, ..., pw_n], where the k-th tuple pw_k is of the form (fn,
args). fn is a callable and args is a dictionary of arguments for fn.
This k-th tuple is used to define any new planewaves (i.e., new coarse
grid basis functions) to be appended to the existing B_k at that level.
The function fn must return functions defined on the finest level,
i.e., a collection of vector(s) of length A.shape[0]. These vectors
are then restricted to the appropriate level, where they enrich the
coarse space.
Instead of a tuple, None can be used to stipulate no introduction
of planewaves at that level. If len(planewaves) < max_levels, the
last entry is used to define coarser level planewaves.
use_constant : {tuple}
Tuple of the form (bool, {'last_level':int}). The boolean denotes
whether to introduce the constant in B at level 0. 'last_level' denotes
the final level to use the constant in B. That is, if 'last_level' is 1,
then the vector in B corresponding to the constant on level 0 is dropped
from B at level 2.
This is important, because using constant based interpolation beyond
the Nyquist rate will result in poor solver performance.
symmetry : {string}
'symmetric' refers to both real and complex symmetric
'hermitian' refers to both complex Hermitian and real Hermitian
'nonsymmetric' i.e. nonsymmetric in a hermitian sense
Note that for the strictly real case, symmetric and hermitian are the same
Note that this flag does not denote definiteness of the operator.
strength : ['symmetric', 'classical', 'evolution', ('predefined', {'C' : csr_matrix}), None]
Method used to determine the strength of connection between unknowns of
the linear system. Method-specific parameters may be passed in using a
tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If strength=None,
all nonzero entries of the matrix are considered strong.
See notes below for varying this parameter on a per level basis. Also,
see notes below for using a predefined strength matrix on each level.
aggregate : ['standard', 'lloyd', 'naive', ('predefined', {'AggOp' : csr_matrix})]
Method used to aggregate nodes. See notes below for varying this
parameter on a per level basis. Also, see notes below for using a
predefined aggregation on each level.
smooth : ['jacobi', 'richardson', 'energy', None]
Method used to smooth the tentative prolongator. Method-specific
parameters may be passed in using a tuple, e.g. smooth=
('jacobi',{'filter' : True }). See notes below for varying this
parameter on a per level basis.
presmoother : {tuple, string, list} : default ('block_gauss_seidel', {'sweep':'symmetric'})
Defines the presmoother for the multilevel cycling. The default block
Gauss-Seidel option defaults to point-wise Gauss-Seidel, if the matrix
is CSR or is a BSR matrix with blocksize of 1. See notes below for
varying this parameter on a per level basis.
postsmoother : {tuple, string, list}
Same as presmoother, except defines the postsmoother.
improve_candidates : {list} : default [('block_gauss_seidel', {'sweep':'symmetric'}), None]
The ith entry defines the method used to improve the candidates B on
level i. If the list is shorter than max_levels, then the last entry
will define the method for all levels lower.
The list elements are relaxation descriptors of the form used for
presmoother and postsmoother. A value of None implies no action on B.
max_levels : {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse : {integer} : default 500
Maximum number of variables permitted on the coarse grid.
Other Parameters
----------------
coarse_solver : ['splu','lu', ... ]
Solver used at the coarsest level of the MG hierarchy
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
See Also
--------
multilevel_solver, smoothed_aggregation_solver
Notes
-----
- The additional parameters are passed through as arguments to
multilevel_solver. Refer to pyamg.multilevel_solver for additional
documentation.
- The parameters smooth, strength, aggregate, presmoother, postsmoother can
be varied on a per level basis. For different methods on different
levels, use a list as input so that the ith entry defines the method at
the ith level. If there are more levels in the hierarchy than list
entries, the last entry will define the method for all levels lower.
Examples are:
smooth=[('jacobi', {'omega':1.0}), None, 'jacobi']
presmoother=[('block_gauss_seidel', {'sweep':symmetric}), 'sor']
aggregate=['standard', 'naive']
strength=[('symmetric', {'theta':0.25}), ('symmetric',{'theta':0.08})]
- Predefined strength of connection and aggregation schemes can be
specified. These options are best used together, but aggregation can be
predefined while strength of connection is not.
For predefined strength of connection, use a list consisting of tuples of
the form ('predefined', {'C' : C0}), where C0 is a csr_matrix and each
degree-of-freedom in C0 represents a supernode. For instance to
predefine a three-level hierarchy, use [('predefined', {'C' : C0}),
('predefined', {'C' : C1}) ].
Similarly for predefined aggregation, use a list of tuples. For instance
to predefine a three-level hierarchy, use [('predefined', {'AggOp' :
Agg0}), ('predefined', {'AggOp' : Agg1}) ], where the dimensions of A,
Agg0 and Agg1 are compatible, i.e. Agg0.shape[1] == A.shape[0] and
Agg1.shape[1] == Agg0.shape[0]. Each AggOp is a csr_matrix.
Examples
--------
>>> from pyamg import smoothed_aggregation_helmholtz_solver, poisson
>>> from scipy.sparse.linalg import cg
>>> from scipy import rand
>>> A = poisson((100,100), format='csr') # matrix
>>> b = rand(A.shape[0]) # random RHS
>>> ml = smoothed_aggregation_solver(A) # AMG solver
>>> M = ml.aspreconditioner(cycle='V') # preconditioner
>>> x,info = cg(A, b, tol=1e-8, maxiter=30, M=M) # solve with CG
References
----------
.. [1] L. N. Olson and J. B. Schroder. Smoothed Aggregation for Helmholtz
Problems. Numerical Linear Algebra with Applications. pp. 361--386. 17
(2010).
"""
if not (isspmatrix_csr(A) or isspmatrix_bsr(A)):
raise TypeError('argument A must have type csr_matrix or bsr_matrix')
A = A.asfptype()
if (symmetry != 'symmetric') and (symmetry != 'hermitian') and (
symmetry != 'nonsymmetric'):
raise ValueError(
'expected \'symmetric\', \'nonsymmetric\' or \'hermitian\' for the symmetry parameter '
)
A.symmetry = symmetry
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
##
# Preprocess and extend planewaves to length max_levels
planewaves = preprocess_planewaves(planewaves, max_levels)
# Check that the user has defined functions for B at each level
use_const, args = unpack_arg(use_constant)
first_planewave_level = -1
for pw in planewaves:
first_planewave_level += 1
if pw is not None:
break
##
if (use_const == False) and (planewaves[0] == None):
raise ValueError('No functions defined for B on the finest level, ' + \
'either use_constant must be true, or planewaves must be defined for level 0')
elif (use_const
== True) and (args['last_level'] < first_planewave_level - 1):
raise ValueError('Some levels have no function(s) defined for B. ' + \
'Change use_constant and/or planewave arguments.')
##
# Levelize the user parameters, so that they become lists describing the
# desired user option on each level.
max_levels, max_coarse, strength =\
levelize_strength_or_aggregation(strength, max_levels, max_coarse)
max_levels, max_coarse, aggregate =\
levelize_strength_or_aggregation(aggregate, max_levels, max_coarse)
improve_candidates = levelize_smooth_or_improve_candidates(
improve_candidates, max_levels)
smooth = levelize_smooth_or_improve_candidates(smooth, max_levels)
##
# Start first level
levels = []
levels.append(multilevel_solver.level())
levels[-1].A = A # matrix
levels[-1].B = numpy.zeros(
(A.shape[0], 0)) # place-holder for near-nullspace candidates
zeros_0 = numpy.zeros((levels[0].A.shape[0], ), dtype=A.dtype)
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
A = levels[0].A
A_l = levels[-1].A
zeros_l = numpy.zeros((levels[-1].A.shape[0], ), dtype=A.dtype)
##
# Generate additions to n-th level candidates
if planewaves[len(levels) - 1] != None:
fn, args = unpack_arg(planewaves[len(levels) - 1])
Bcoarse2 = numpy.array(fn(**args))
##
# As in alpha-SA, relax the candidates before restriction
if improve_candidates[0] is not None:
Bcoarse2 = relaxation_as_linear_operator(
improve_candidates[0], A, zeros_0) * Bcoarse2
##
# Restrict Bcoarse2 to current level
for i in range(len(levels) - 1):
Bcoarse2 = levels[i].R * Bcoarse2
# relax after restriction
if improve_candidates[len(levels) - 1] is not None:
Bcoarse2 = relaxation_as_linear_operator(
improve_candidates[len(levels) - 1], A_l,
zeros_l) * Bcoarse2
else:
Bcoarse2 = numpy.zeros((A_l.shape[0], 0), dtype=A.dtype)
##
# Deal with the use of constant in interpolation
use_const, args = unpack_arg(use_constant)
if use_const and len(levels) == 1:
# If level 0, and the constant is to be used in interpolation
levels[0].B = numpy.hstack((numpy.ones((A.shape[0], 1),
dtype=A.dtype), Bcoarse2))
elif use_const and args['last_level'] == len(levels) - 2:
# If the previous level was the last level to use the constant, then remove the
# coarse grid function based on the constant from B
levels[-1].B = numpy.hstack((levels[-1].B[:, 1:], Bcoarse2))
else:
levels[-1].B = numpy.hstack((levels[-1].B, Bcoarse2))
##
# Create and Append new level
extend_hierarchy(levels,
strength,
aggregate,
smooth, [None for i in range(max_levels)],
keep=True)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def extend_hierarchy(levels, strength, CF, interp, restrict, filter_operator,
coarse_grid_P, coarse_grid_R, keep):
""" helper function for local methods """
# Filter operator. Need to keep original matrix on fineest level for
# computing residuals
if (filter_operator is not None) and (filter_operator[1] != 0):
if len(levels) == 1:
A = deepcopy(levels[-1].A)
else:
A = levels[-1].A
filter_matrix_rows(A,
filter_operator[1],
diagonal=True,
lump=filter_operator[0])
else:
A = levels[-1].A
# Check if matrix was filtered to be diagonal --> coarsest grid
if A.nnz == A.shape[0]:
return 1
# Zero initial complexities for strength, splitting and interpolation
levels[-1].complexity['CF'] = 0.0
levels[-1].complexity['strength'] = 0.0
levels[-1].complexity['interpolate'] = 0.0
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
C = evolution_strength_of_connection(A, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
levels[-1].complexity['strength'] += kwargs['cost'][0] * A.nnz / float(
A.nnz)
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'RS':
splitting = RS(C, **kwargs)
elif fn == 'PMIS':
splitting = PMIS(C, **kwargs)
elif fn == 'PMISc':
splitting = PMISc(C, **kwargs)
elif fn == 'CLJP':
splitting = CLJP(C, **kwargs)
elif fn == 'CLJPc':
splitting = CLJPc(C, **kwargs)
elif fn == 'CR':
splitting = CR(C, **kwargs)
elif fn == 'weighted_matching':
splitting, soc = weighted_matching(C, **kwargs)
if soc is not None:
C = soc
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
levels[-1].complexity['CF'] += kwargs['cost'][0] * C.nnz / float(A.nnz)
temp = np.sum(splitting)
if (temp == len(splitting)) or (temp == 0):
return 1
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
r_flag = False
fn, kwargs = unpack_arg(interp)
if fn == 'standard':
P = standard_interpolation(A, C, splitting, **kwargs)
elif fn == 'distance_two':
P = distance_two_interpolation(A, C, splitting, **kwargs)
elif fn == 'direct':
P = direct_interpolation(A, C, splitting, **kwargs)
elif fn == 'one_point':
P = one_point_interpolation(A, C, splitting, **kwargs)
elif fn == 'inject':
P = injection_interpolation(A, splitting, **kwargs)
elif fn == 'neumann':
P = neumann_ideal_interpolation(A, splitting, **kwargs)
elif fn == 'scaledAfc':
P = scaled_Afc_interpolation(A, splitting, **kwargs)
elif fn == 'air':
if isspmatrix_bsr(A):
temp_A = bsr_matrix(A.T)
P = local_AIR(temp_A, splitting, **kwargs)
P = bsr_matrix(P.T)
else:
temp_A = csr_matrix(A.T)
P = local_AIR(temp_A, splitting, **kwargs)
P = csr_matrix(P.T)
elif fn == 'restrict':
r_flag = True
else:
raise ValueError('unknown interpolation method (%s)' % interp)
levels[-1].complexity['interpolate'] += kwargs['cost'][0] * A.nnz / float(
A.nnz)
# Build restriction operator
fn, kwargs = unpack_arg(restrict)
if fn is None:
R = P.T
elif fn == 'air':
R = local_AIR(A, splitting, **kwargs)
elif fn == 'neumann':
R = neumann_AIR(A, splitting, **kwargs)
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R = one_point_interpolation(A, temp_C, splitting, **kwargs)
if isspmatrix_bsr(A):
R = R.T.tobsr()
else:
R = R.T.tocsr()
elif fn == 'inject': # Don't need A^T or C^T here
R = injection_interpolation(A, splitting, **kwargs)
if isspmatrix_bsr(A):
R = R.T.tobsr()
else:
R = R.T.tocsr()
elif fn == 'standard':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'distance_two':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'direct':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
else:
raise ValueError('unknown restriction method (%s)' % restrict)
# If set P = R^T
if r_flag:
P = R.T
# Optional different interpolation for RAP
fn, kwargs = unpack_arg(coarse_grid_P)
if fn == 'standard':
P_temp = standard_interpolation(A, C, splitting, **kwargs)
elif fn == 'distance_two':
P_temp = distance_two_interpolation(A, C, splitting, **kwargs)
elif fn == 'direct':
P_temp = direct_interpolation(A, C, splitting, **kwargs)
elif fn == 'one_point':
P_temp = one_point_interpolation(A, C, splitting, **kwargs)
elif fn == 'inject':
P_temp = injection_interpolation(A, splitting, **kwargs)
elif fn == 'neumann':
P_temp = neumann_ideal_interpolation(A, splitting, **kwargs)
elif fn == 'air':
if isspmatrix_bsr(A):
temp_A = bsr_matrix(A.T)
P_temp = local_AIR(temp_A, splitting, **kwargs)
P_temp = bsr_matrix(P_temp.T)
else:
temp_A = csr_matrix(A.T)
P_temp = local_AIR(temp_A, splitting, **kwargs)
P_temp = csr_matrix(P_temp.T)
else:
P_temp = P
# Optional different restriction for RAP
fn, kwargs = unpack_arg(coarse_grid_R)
if fn == 'air':
R_temp = local_AIR(A, splitting, **kwargs)
elif fn == 'neumann':
R_temp = neumann_AIR(A, splitting, **kwargs)
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R_temp = one_point_interpolation(A, temp_C, splitting, **kwargs)
if isspmatrix_bsr(A):
R_temp = R_temp.T.tobsr()
else:
R_temp = R_temp.T.tocsr()
elif fn == 'inject': # Don't need A^T or C^T here
R_temp = injection_interpolation(A, splitting, **kwargs)
if isspmatrix_bsr(A):
R_temp = R_temp.T.tobsr()
else:
R_temp = R_temp.T.tocsr()
elif fn == 'standard':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R_temp = standard_interpolation(temp_A, temp_C, splitting,
**kwargs)
R_temp = R_temp.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R_temp = standard_interpolation(temp_A, temp_C, splitting,
**kwargs)
R_temp = R_temp.T.tocsr()
elif fn == 'distance_two':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R_temp = distance_two_interpolation(temp_A, temp_C, splitting,
**kwargs)
R_temp = R_temp.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R_temp = distance_two_interpolation(temp_A, temp_C, splitting,
**kwargs)
R_temp = R_temp.T.tocsr()
elif fn == 'direct':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R_temp = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R_temp = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tocsr()
else:
R_temp = R
# Store relevant information for this level
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels[-1].splitting = splitting # C/F splitting
# Form coarse grid operator, get complexity
#levels[-1].complexity['RAP'] = mat_mat_complexity(R_temp,A) / float(A.nnz)
#RA = R_temp * A
#levels[-1].complexity['RAP'] += mat_mat_complexity(RA,P_temp) / float(A.nnz)
#A = RA * P_temp
# RL: RAP = R*(A*P)
levels[-1].complexity['RAP'] = mat_mat_complexity(A, P_temp) / float(A.nnz)
AP = A * P_temp
levels[-1].complexity['RAP'] += mat_mat_complexity(R_temp, AP) / float(
A.nnz)
A = R_temp * AP
# Make sure coarse-grid operator is in correct sparse format
if (isspmatrix_csr(P) and (not isspmatrix_csr(A))):
A = A.tocsr()
elif (isspmatrix_bsr(P) and (not isspmatrix_bsr(A))):
A = A.tobsr()
A.eliminate_zeros()
levels.append(multilevel_solver.level())
levels[-1].A = A
return 0
def smoothed_aggregation_solver(A, B=None, BH=None,
symmetry='hermitian', strength='symmetric',
aggregate='standard',
smooth=('jacobi', {'omega': 4.0/3.0}),
presmoother=('block_gauss_seidel',
{'sweep': 'symmetric'}),
postsmoother=('block_gauss_seidel',
{'sweep': 'symmetric'}),
improve_candidates=[('block_gauss_seidel',
{'sweep': 'symmetric',
'iterations': 4}),
None],
max_levels = 10, max_coarse = 10,
diagonal_dominance=False,
keep=False, **kwargs):
"""
Create a multilevel solver using classical-style Smoothed Aggregation (SA)
Parameters
----------
A : {csr_matrix, bsr_matrix}
Sparse NxN matrix in CSR or BSR format
B : {None, array_like}
Right near-nullspace candidates stored in the columns of an NxK array.
The default value B=None is equivalent to B=ones((N,1))
BH : {None, array_like}
Left near-nullspace candidates stored in the columns of an NxK array.
BH is only used if symmetry is 'nonsymmetric'.
The default value B=None is equivalent to BH=B.copy()
symmetry : {string}
'symmetric' refers to both real and complex symmetric
'hermitian' refers to both complex Hermitian and real Hermitian
'nonsymmetric' i.e. nonsymmetric in a hermitian sense
Note, in the strictly real case, symmetric and hermitian are the same
Note, this flag does not denote definiteness of the operator.
strength : {list} : default ['symmetric', 'classical', 'evolution',
'algebraic_distance', 'affinity',
('predefined', {'C' : csr_matrix}), None]
Method used to determine the strength of connection between unknowns of
the linear system. Method-specific parameters may be passed in using a
tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If strength=None,
all nonzero entries of the matrix are considered strong.
See notes below for varying this parameter on a per level basis. Also,
see notes below for using a predefined strength matrix on each level.
aggregate : {list} : default ['standard', 'lloyd', 'naive',
('predefined', {'AggOp' : csr_matrix})]
Method used to aggregate nodes. See notes below for varying this
parameter on a per level basis. Also, see notes below for using a
predefined aggregation on each level.
smooth : {list} : default ['jacobi', 'richardson', 'energy', None]
Method used to smooth the tentative prolongator. Method-specific
parameters may be passed in using a tuple, e.g. smooth=
('jacobi',{'filter' : True }). See notes below for varying this
parameter on a per level basis.
presmoother : {tuple, string, list} : default ('block_gauss_seidel',
{'sweep':'symmetric'})
Defines the presmoother for the multilevel cycling. The default block
Gauss-Seidel option defaults to point-wise Gauss-Seidel, if the matrix
is CSR or is a BSR matrix with blocksize of 1. See notes below for
varying this parameter on a per level basis.
postsmoother : {tuple, string, list}
Same as presmoother, except defines the postsmoother.
improve_candidates : {tuple, string, list} : default
[('block_gauss_seidel',
{'sweep': 'symmetric', 'iterations': 4}), None]
The ith entry defines the method used to improve the candidates B on
level i. If the list is shorter than max_levels, then the last entry
will define the method for all levels lower. If tuple or string, then
this single relaxation descriptor defines improve_candidates on all
levels.
The list elements are relaxation descriptors of the form used for
presmoother and postsmoother. A value of None implies no action on B.
max_levels : {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse : {integer} : default 500
Maximum number of variables permitted on the coarse grid.
diagonal_dominance : {bool, tuple} : default False
If True (or the first tuple entry is True), then avoid coarsening
diagonally dominant rows. The second tuple entry requires a
dictionary, where the key value 'theta' is used to tune the diagonal
dominance threshold.
keep : {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C),
tentative prolongation (T), and aggregation (AggOp) are kept.
Other Parameters
----------------
cycle_type : ['V','W','F']
Structrure of multigrid cycle
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
setup_complexity : bool
For a detailed, more accurate setup complexity, pass in
'setup_complexity' = True. This will slow down performance, but
increase accuracy of complexity count.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
See Also
--------
multilevel_solver, classical.ruge_stuben_solver,
aggregation.smoothed_aggregation_solver
Notes
-----
- This method implements classical-style SA, not root-node style SA
(see aggregation.rootnode_solver).
- The additional parameters are passed through as arguments to
multilevel_solver. Refer to pyamg.multilevel_solver for additional
documentation.
- At each level, four steps are executed in order to define the coarser
level operator.
1. Matrix A is given and used to derive a strength matrix, C.
2. Based on the strength matrix, indices are grouped or aggregated.
3. The aggregates define coarse nodes and a tentative prolongation
operator T is defined by injection
4. The tentative prolongation operator is smoothed by a relaxation
scheme to improve the quality and extent of interpolation from the
aggregates to fine nodes.
- The parameters smooth, strength, aggregate, presmoother, postsmoother
can be varied on a per level basis. For different methods on
different levels, use a list as input so that the i-th entry defines
the method at the i-th level. If there are more levels in the
hierarchy than list entries, the last entry will define the method
for all levels lower.
Examples are:
smooth=[('jacobi', {'omega':1.0}), None, 'jacobi']
presmoother=[('block_gauss_seidel', {'sweep':symmetric}), 'sor']
aggregate=['standard', 'naive']
strength=[('symmetric', {'theta':0.25}), ('symmetric',
{'theta':0.08})]
- Predefined strength of connection and aggregation schemes can be
specified. These options are best used together, but aggregation can
be predefined while strength of connection is not.
For predefined strength of connection, use a list consisting of
tuples of the form ('predefined', {'C' : C0}), where C0 is a
csr_matrix and each degree-of-freedom in C0 represents a supernode.
For instance to predefine a three-level hierarchy, use
[('predefined', {'C' : C0}), ('predefined', {'C' : C1}) ].
Similarly for predefined aggregation, use a list of tuples. For
instance to predefine a three-level hierarchy, use [('predefined',
{'AggOp' : Agg0}), ('predefined', {'AggOp' : Agg1}) ], where the
dimensions of A, Agg0 and Agg1 are compatible, i.e. Agg0.shape[1] ==
A.shape[0] and Agg1.shape[1] == Agg0.shape[0]. Each AggOp is a
csr_matrix.
Examples
--------
>>> from pyamg import smoothed_aggregation_solver
>>> from pyamg.gallery import poisson
>>> from scipy.sparse.linalg import cg
>>> import numpy as np
>>> A = poisson((100,100), format='csr') # matrix
>>> b = np.ones((A.shape[0])) # RHS
>>> ml = smoothed_aggregation_solver(A) # AMG solver
>>> M = ml.aspreconditioner(cycle='V') # preconditioner
>>> x,info = cg(A, b, tol=1e-8, maxiter=30, M=M) # solve with CG
References
----------
.. [1] Vanek, P. and Mandel, J. and Brezina, M.,
"Algebraic Multigrid by Smoothed Aggregation for
Second and Fourth Order Elliptic Problems",
Computing, vol. 56, no. 3, pp. 179--196, 1996.
http://citeseer.ist.psu.edu/vanek96algebraic.html
"""
if ('setup_complexity' in kwargs):
if kwargs['setup_complexity'] == True:
mat_mat_complexity.__detailed__ = True
del kwargs['setup_complexity']
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')
A = A.asfptype()
if (symmetry != 'symmetric') and (symmetry != 'hermitian') and\
(symmetry != 'nonsymmetric'):
raise ValueError('expected \'symmetric\', \'nonsymmetric\' or '
'hermitian\' for the symmetry parameter ')
A.symmetry = symmetry
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
# Right near nullspace candidates use constant for each variable as default
if B is None:
B = np.kron(np.ones((int(A.shape[0]/blocksize(A)), 1), dtype=A.dtype),
np.eye(blocksize(A)))
else:
B = np.asarray(B, dtype=A.dtype)
if len(B.shape) == 1:
B = B.reshape(-1, 1)
if B.shape[0] != A.shape[0]:
raise ValueError('The near null-space modes B have incorrect \
dimensions for matrix A')
if B.shape[1] < blocksize(A):
warn('Having less target vectors, B.shape[1], than \
blocksize of A can degrade convergence factors.')
# Left near nullspace candidates
if A.symmetry == 'nonsymmetric':
if BH is None:
BH = B.copy()
else:
BH = np.asarray(BH, dtype=A.dtype)
if len(BH.shape) == 1:
BH = BH.reshape(-1, 1)
if BH.shape[1] != B.shape[1]:
raise ValueError('The number of left and right near \
null-space modes B and BH, must be equal')
if BH.shape[0] != A.shape[0]:
raise ValueError('The near null-space modes BH have \
incorrect dimensions for matrix A')
# Levelize the user parameters, so that they become lists describing the
# desired user option on each level.
max_levels, max_coarse, strength =\
levelize_strength_or_aggregation(strength, max_levels, max_coarse)
max_levels, max_coarse, aggregate =\
levelize_strength_or_aggregation(aggregate, max_levels, max_coarse)
improve_candidates =\
levelize_smooth_or_improve_candidates(improve_candidates, max_levels)
smooth = levelize_smooth_or_improve_candidates(smooth, max_levels)
# Construct multilevel structure
levels = []
levels.append(multilevel_solver.level())
levels[-1].A = A # matrix
# Append near nullspace candidates
levels[-1].B = B # right candidates
if A.symmetry == 'nonsymmetric':
levels[-1].BH = BH # left candidates
while len(levels) < max_levels and\
int(levels[-1].A.shape[0]/blocksize(levels[-1].A)) > max_coarse:
extend_hierarchy(levels, strength, aggregate, smooth,
improve_candidates, diagonal_dominance, keep)
# Construct and return multilevel hierarchy
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def AIR_solver(A,
strength=('classical', {
'theta': 0.3,
'norm': 'min'
}),
CF='RS',
interp='one_point',
restrict='neumann',
presmoother=None,
postsmoother=('FC_jacobi', {
'omega': 1.0,
'iterations': 1,
'withrho': False,
'F_iterations': 2,
'C_iterations': 0
}),
filter_operator=None,
coarse_grid_P=None,
coarse_grid_R=None,
max_levels=20,
max_coarse=20,
keep=False,
**kwargs):
"""Create a multilevel solver using Classical AMG (Ruge-Stuben AMG)
Parameters
----------
A : csr_matrix
Square nonsymmetric matrix in CSR format
strength : ['symmetric', 'classical', 'evolution', 'distance',
'algebraic_distance','affinity', 'energy_based', None]
Method used to determine the strength of connection between unknowns
of the linear system. Method-specific parameters may be passed in
using a tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If
strength=None, all nonzero entries of the matrix are considered strong.
CF : {string} : default 'RS'
Method used for coarse grid selection (C/F splitting)
Supported methods are RS, PMIS, PMISc, CLJP, CLJPc, and CR.
interp : {string} : default 'one-point'
Options include 'direct', 'standard', 'inject' and 'one-point'.
restrict : {string} : default 'neumann'
Options include 'air' for approximate ideal
restriction.
presmoother : {string or dict} : default None
Method used for presmoothing at each level. Method-specific parameters
may be passed in using a tuple, e.g.
presmoother=('gauss_seidel',{'sweep':'symmetric}), the default.
postsmoother : {string or dict} : default F-Jacobi
Postsmoothing method with the same usage as presmoother
filter_operator : (bool, tol) : default None
Remove small entries in operators on each level if True. Entries are
considered "small" if |a_ij| < tol |a_ii|.
coarse_grid_P : {string} : default None
Option to specify a different construction of P used in computing RAP
vs. for interpolation in an actual solve.
max_levels: {integer} : default 20
Maximum number of levels to be used in the multilevel solver.
max_coarse: {integer} : default 20
Maximum number of variables permitted on the coarse grid.
keep: {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C) and
tentative prolongation (T) are kept.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
Other Parameters
----------------
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
setup_complexity : bool
For a detailed, more accurate setup complexity, pass in
'setup_complexity' = True. This will slow down performance, but
increase accuracy of complexity count.
Notes
-----
References
----------
.. [1]
See Also
--------
aggregation.smoothed_aggregation_solver, multilevel_solver,
aggregation.rootnode_solver
"""
if ('setup_complexity' in kwargs):
if kwargs['setup_complexity'] == True:
mat_mat_complexity.__detailed__ = True
del kwargs['setup_complexity']
# preprocess A
A = A.asfptype()
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
levels = [multilevel_solver.level()]
levels[-1].A = A
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
bottom = extend_hierarchy(levels, strength, CF, interp, restrict,
filter_operator, coarse_grid_P,
coarse_grid_R, keep)
if bottom:
break
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def smoothed_aggregation_helmholtz_solver(A, planewaves, use_constant=(True, {'last_level':0}),
symmetry='symmetric', strength='symmetric', aggregate='standard',
smooth=('energy', {'krylov': 'gmres'}),
presmoother=('gauss_seidel_nr',{'sweep':'symmetric'}),
postsmoother=('gauss_seidel_nr',{'sweep':'symmetric'}),
improve_candidates='default', max_levels = 10, max_coarse = 100, **kwargs):
"""
Create a multilevel solver using Smoothed Aggregation (SA) for a 2D Helmholtz operator
Parameters
----------
A : {csr_matrix, bsr_matrix}
Sparse NxN matrix in CSR or BSR format
planewaves : { list }
[pw_0, pw_1, ..., pw_n], where the k-th tuple pw_k is of the form (fn,
args). fn is a callable and args is a dictionary of arguments for fn.
This k-th tuple is used to define any new planewaves (i.e., new coarse
grid basis functions) to be appended to the existing B_k at that level.
The function fn must return functions defined on the finest level,
i.e., a collection of vector(s) of length A.shape[0]. These vectors
are then restricted to the appropriate level, where they enrich the
coarse space.
Instead of a tuple, None can be used to stipulate no introduction
of planewaves at that level. If len(planewaves) < max_levels, the
last entry is used to define coarser level planewaves.
use_constant : {tuple}
Tuple of the form (bool, {'last_level':int}). The boolean denotes
whether to introduce the constant in B at level 0. 'last_level' denotes
the final level to use the constant in B. That is, if 'last_level' is 1,
then the vector in B corresponding to the constant on level 0 is dropped
from B at level 2.
This is important, because using constant based interpolation beyond
the Nyquist rate will result in poor solver performance.
symmetry : {string}
'symmetric' refers to both real and complex symmetric
'hermitian' refers to both complex Hermitian and real Hermitian
'nonsymmetric' i.e. nonsymmetric in a hermitian sense
Note that for the strictly real case, symmetric and hermitian are the same
Note that this flag does not denote definiteness of the operator.
strength : ['symmetric', 'classical', 'evolution', ('predefined', {'C' : csr_matrix}), None]
Method used to determine the strength of connection between unknowns of
the linear system. Method-specific parameters may be passed in using a
tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If strength=None,
all nonzero entries of the matrix are considered strong.
See notes below for varying this parameter on a per level basis. Also,
see notes below for using a predefined strength matrix on each level.
aggregate : ['standard', 'lloyd', 'naive', ('predefined', {'AggOp' : csr_matrix})]
Method used to aggregate nodes. See notes below for varying this
parameter on a per level basis. Also, see notes below for using a
predefined aggregation on each level.
smooth : ['jacobi', 'richardson', 'energy', None]
Method used to smooth the tentative prolongator. Method-specific
parameters may be passed in using a tuple, e.g. smooth=
('jacobi',{'filter' : True }). See notes below for varying this
parameter on a per level basis.
presmoother : {tuple, string, list} : default ('block_gauss_seidel', {'sweep':'symmetric'})
Defines the presmoother for the multilevel cycling. The default block
Gauss-Seidel option defaults to point-wise Gauss-Seidel, if the matrix
is CSR or is a BSR matrix with blocksize of 1. See notes below for
varying this parameter on a per level basis.
postsmoother : {tuple, string, list}
Same as presmoother, except defines the postsmoother.
improve_candidates : {list} : default [('block_gauss_seidel', {'sweep':'symmetric'}), None]
The ith entry defines the method used to improve the candidates B on
level i. If the list is shorter than max_levels, then the last entry
will define the method for all levels lower.
The list elements are relaxation descriptors of the form used for
presmoother and postsmoother. A value of None implies no action on B.
max_levels : {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse : {integer} : default 500
Maximum number of variables permitted on the coarse grid.
Other Parameters
----------------
coarse_solver : ['splu','lu', ... ]
Solver used at the coarsest level of the MG hierarchy
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
See Also
--------
multilevel_solver, smoothed_aggregation_solver
Notes
-----
- The additional parameters are passed through as arguments to
multilevel_solver. Refer to pyamg.multilevel_solver for additional
documentation.
- The parameters smooth, strength, aggregate, presmoother, postsmoother can
be varied on a per level basis. For different methods on different
levels, use a list as input so that the ith entry defines the method at
the ith level. If there are more levels in the hierarchy than list
entries, the last entry will define the method for all levels lower.
Examples are:
smooth=[('jacobi', {'omega':1.0}), None, 'jacobi']
presmoother=[('block_gauss_seidel', {'sweep':symmetric}), 'sor']
aggregate=['standard', 'naive']
strength=[('symmetric', {'theta':0.25}), ('symmetric',{'theta':0.08})]
- Predefined strength of connection and aggregation schemes can be
specified. These options are best used together, but aggregation can be
predefined while strength of connection is not.
For predefined strength of connection, use a list consisting of tuples of
the form ('predefined', {'C' : C0}), where C0 is a csr_matrix and each
degree-of-freedom in C0 represents a supernode. For instance to
predefine a three-level hierarchy, use [('predefined', {'C' : C0}),
('predefined', {'C' : C1}) ].
Similarly for predefined aggregation, use a list of tuples. For instance
to predefine a three-level hierarchy, use [('predefined', {'AggOp' :
Agg0}), ('predefined', {'AggOp' : Agg1}) ], where the dimensions of A,
Agg0 and Agg1 are compatible, i.e. Agg0.shape[1] == A.shape[0] and
Agg1.shape[1] == Agg0.shape[0]. Each AggOp is a csr_matrix.
Examples
--------
>>> from pyamg import smoothed_aggregation_helmholtz_solver, poisson
>>> from scipy.sparse.linalg import cg
>>> from scipy import rand
>>> A = poisson((100,100), format='csr') # matrix
>>> b = rand(A.shape[0]) # random RHS
>>> ml = smoothed_aggregation_solver(A) # AMG solver
>>> M = ml.aspreconditioner(cycle='V') # preconditioner
>>> x,info = cg(A, b, tol=1e-8, maxiter=30, M=M) # solve with CG
References
----------
.. [1] L. N. Olson and J. B. Schroder. Smoothed Aggregation for Helmholtz
Problems. Numerical Linear Algebra with Applications. pp. 361--386. 17
(2010).
"""
if not (isspmatrix_csr(A) or isspmatrix_bsr(A)):
raise TypeError('argument A must have type csr_matrix or bsr_matrix')
A = A.asfptype()
if (symmetry != 'symmetric') and (symmetry != 'hermitian') and (symmetry != 'nonsymmetric'):
raise ValueError('expected \'symmetric\', \'nonsymmetric\' or \'hermitian\' for the symmetry parameter ')
A.symmetry = symmetry
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
##
# Preprocess and extend planewaves to length max_levels
planewaves = preprocess_planewaves(planewaves, max_levels)
# Check that the user has defined functions for B at each level
use_const, args = unpack_arg(use_constant)
first_planewave_level = -1
for pw in planewaves:
first_planewave_level += 1
if pw is not None:
break
##
if (use_const == False) and (planewaves[0] == None):
raise ValueError('No functions defined for B on the finest level, ' + \
'either use_constant must be true, or planewaves must be defined for level 0')
elif (use_const == True) and (args['last_level'] < first_planewave_level-1):
raise ValueError('Some levels have no function(s) defined for B. ' + \
'Change use_constant and/or planewave arguments.')
##
# Levelize the user parameters, so that they become lists describing the
# desired user option on each level.
max_levels, max_coarse, strength =\
levelize_strength_or_aggregation(strength, max_levels, max_coarse)
max_levels, max_coarse, aggregate =\
levelize_strength_or_aggregation(aggregate, max_levels, max_coarse)
improve_candidates = levelize_smooth_or_improve_candidates(improve_candidates, max_levels)
smooth = levelize_smooth_or_improve_candidates(smooth, max_levels)
##
# Start first level
levels = []
levels.append( multilevel_solver.level() )
levels[-1].A = A # matrix
levels[-1].B = numpy.zeros((A.shape[0],0)) # place-holder for near-nullspace candidates
zeros_0 = numpy.zeros((levels[0].A.shape[0],), dtype=A.dtype)
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
A = levels[0].A
A_l = levels[-1].A
zeros_l = numpy.zeros((levels[-1].A.shape[0],), dtype=A.dtype)
##
# Generate additions to n-th level candidates
if planewaves[len(levels)-1] != None:
fn, args = unpack_arg(planewaves[len(levels)-1])
Bcoarse2 = numpy.array(fn(**args))
##
# As in alpha-SA, relax the candidates before restriction
if improve_candidates[0] is not None:
Bcoarse2 = relaxation_as_linear_operator(improve_candidates[0], A, zeros_0)*Bcoarse2
##
# Restrict Bcoarse2 to current level
for i in range(len(levels)-1):
Bcoarse2 = levels[i].R*Bcoarse2
# relax after restriction
if improve_candidates[len(levels)-1] is not None:
Bcoarse2 =relaxation_as_linear_operator(improve_candidates[len(levels)-1],A_l,zeros_l)*Bcoarse2
else:
Bcoarse2 = numpy.zeros((A_l.shape[0],0),dtype=A.dtype)
##
# Deal with the use of constant in interpolation
use_const, args = unpack_arg(use_constant)
if use_const and len(levels) == 1:
# If level 0, and the constant is to be used in interpolation
levels[0].B = numpy.hstack( (numpy.ones((A.shape[0],1), dtype=A.dtype), Bcoarse2) )
elif use_const and args['last_level'] == len(levels)-2:
# If the previous level was the last level to use the constant, then remove the
# coarse grid function based on the constant from B
levels[-1].B = numpy.hstack( (levels[-1].B[:,1:], Bcoarse2) )
else:
levels[-1].B = numpy.hstack((levels[-1].B, Bcoarse2))
##
# Create and Append new level
extend_hierarchy(levels, strength, aggregate, smooth, [None for i in range(max_levels)] ,keep=True)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def extend_hierarchy(levels, strength, CF, interp, restrict, filter_operator,
coarse_grid_P, coarse_grid_R, keep):
""" helper function for local methods """
# Filter operator. Need to keep original matrix on fineest level for
# computing residuals
if (filter_operator is not None) and (filter_operator[1] != 0):
if len(levels) == 1:
A = deepcopy(levels[-1].A)
else:
A = levels[-1].A
filter_matrix_rows(A, filter_operator[1], diagonal=True, lump=filter_operator[0])
else:
A = levels[-1].A
# Check if matrix was filtered to be diagonal --> coarsest grid
if A.nnz == A.shape[0]:
return 1
# Zero initial complexities for strength, splitting and interpolation
levels[-1].complexity['CF'] = 0.0
levels[-1].complexity['strength'] = 0.0
levels[-1].complexity['interpolate'] = 0.0
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
C = evolution_strength_of_connection(A, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
levels[-1].complexity['strength'] += kwargs['cost'][0] * A.nnz / float(A.nnz)
# Generate the C/F splitting
fn, kwargs = unpack_arg(CF)
if fn == 'RS':
splitting = RS(C, **kwargs)
elif fn == 'PMIS':
splitting = PMIS(C, **kwargs)
elif fn == 'PMISc':
splitting = PMISc(C, **kwargs)
elif fn == 'CLJP':
splitting = CLJP(C, **kwargs)
elif fn == 'CLJPc':
splitting = CLJPc(C, **kwargs)
elif fn == 'CR':
splitting = CR(C, **kwargs)
elif fn == 'weighted_matching':
splitting, soc = weighted_matching(C, **kwargs)
if soc is not None:
C = soc
else:
raise ValueError('unknown C/F splitting method (%s)' % CF)
levels[-1].complexity['CF'] += kwargs['cost'][0] * C.nnz / float(A.nnz)
temp = np.sum(splitting)
if (temp == len(splitting)) or (temp == 0):
return 1
# Generate the interpolation matrix that maps from the coarse-grid to the
# fine-grid
r_flag = False
fn, kwargs = unpack_arg(interp)
if fn == 'standard':
P = standard_interpolation(A, C, splitting, **kwargs)
elif fn == 'distance_two':
P = distance_two_interpolation(A, C, splitting, **kwargs)
elif fn == 'direct':
P = direct_interpolation(A, C, splitting, **kwargs)
elif fn == 'one_point':
P = one_point_interpolation(A, C, splitting, **kwargs)
elif fn == 'inject':
P = injection_interpolation(A, splitting, **kwargs)
elif fn == 'neumann':
P = neumann_ideal_interpolation(A, splitting, **kwargs)
elif fn == 'scaledAfc':
P = scaled_Afc_interpolation(A, splitting, **kwargs)
elif fn == 'air':
if isspmatrix_bsr(A):
temp_A = bsr_matrix(A.T)
P = local_AIR(temp_A, splitting, **kwargs)
P = bsr_matrix(P.T)
else:
temp_A = csr_matrix(A.T)
P = local_AIR(temp_A, splitting, **kwargs)
P = csr_matrix(P.T)
elif fn == 'restrict':
r_flag = True
else:
raise ValueError('unknown interpolation method (%s)' % interp)
levels[-1].complexity['interpolate'] += kwargs['cost'][0] * A.nnz / float(A.nnz)
# Build restriction operator
fn, kwargs = unpack_arg(restrict)
if fn is None:
R = P.T
elif fn == 'air':
R = local_AIR(A, splitting, **kwargs)
elif fn == 'neumann':
R = neumann_AIR(A, splitting, **kwargs)
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R = one_point_interpolation(A, temp_C, splitting, **kwargs)
if isspmatrix_bsr(A):
R = R.T.tobsr()
else:
R = R.T.tocsr()
elif fn == 'inject': # Don't need A^T or C^T here
R = injection_interpolation(A, splitting, **kwargs)
if isspmatrix_bsr(A):
R = R.T.tobsr()
else:
R = R.T.tocsr()
elif fn == 'standard':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'distance_two':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
elif fn == 'direct':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R = R.T.tocsr()
else:
raise ValueError('unknown restriction method (%s)' % restrict)
# If set P = R^T
if r_flag:
P = R.T
# Optional different interpolation for RAP
fn, kwargs = unpack_arg(coarse_grid_P)
if fn == 'standard':
P_temp = standard_interpolation(A, C, splitting, **kwargs)
elif fn == 'distance_two':
P_temp = distance_two_interpolation(A, C, splitting, **kwargs)
elif fn == 'direct':
P_temp = direct_interpolation(A, C, splitting, **kwargs)
elif fn == 'one_point':
P_temp = one_point_interpolation(A, C, splitting, **kwargs)
elif fn == 'inject':
P_temp = injection_interpolation(A, splitting, **kwargs)
elif fn == 'neumann':
P_temp = neumann_ideal_interpolation(A, splitting, **kwargs)
elif fn == 'air':
if isspmatrix_bsr(A):
temp_A = bsr_matrix(A.T)
P_temp = local_AIR(temp_A, splitting, **kwargs)
P_temp = bsr_matrix(P_temp.T)
else:
temp_A = csr_matrix(A.T)
P_temp = local_AIR(temp_A, splitting, **kwargs)
P_temp = csr_matrix(P_temp.T)
else:
P_temp = P
# Optional different restriction for RAP
fn, kwargs = unpack_arg(coarse_grid_R)
if fn == 'air':
R_temp = local_AIR(A, splitting, **kwargs)
elif fn == 'neumann':
R_temp = neumann_AIR(A, splitting, **kwargs)
elif fn == 'one_point': # Don't need A^T here
temp_C = C.T.tocsr()
R_temp = one_point_interpolation(A, temp_C, splitting, **kwargs)
if isspmatrix_bsr(A):
R_temp = R_temp.T.tobsr()
else:
R_temp = R_temp.T.tocsr()
elif fn == 'inject': # Don't need A^T or C^T here
R_temp = injection_interpolation(A, splitting, **kwargs)
if isspmatrix_bsr(A):
R_temp = R_temp.T.tobsr()
else:
R_temp = R_temp.T.tocsr()
elif fn == 'standard':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R_temp = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R_temp = standard_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tocsr()
elif fn == 'distance_two':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R_temp = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R_temp = distance_two_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tocsr()
elif fn == 'direct':
if isspmatrix_bsr(A):
temp_A = A.T.tobsr()
temp_C = C.T.tobsr()
R_temp = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tobsr()
else:
temp_A = A.T.tocsr()
temp_C = C.T.tocsr()
R_temp = direct_interpolation(temp_A, temp_C, splitting, **kwargs)
R_temp = R_temp.T.tocsr()
else:
R_temp = R
# Store relevant information for this level
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].P = P # prolongation operator
levels[-1].R = R # restriction operator
levels[-1].splitting = splitting # C/F splitting
# Form coarse grid operator, get complexity
#levels[-1].complexity['RAP'] = mat_mat_complexity(R_temp,A) / float(A.nnz)
#RA = R_temp * A
#levels[-1].complexity['RAP'] += mat_mat_complexity(RA,P_temp) / float(A.nnz)
#A = RA * P_temp
# RL: RAP = R*(A*P)
levels[-1].complexity['RAP'] = mat_mat_complexity(A, P_temp) / float(A.nnz)
AP = A * P_temp
levels[-1].complexity['RAP'] += mat_mat_complexity(R_temp, AP) / float(A.nnz)
A = R_temp * AP
# Make sure coarse-grid operator is in correct sparse format
if (isspmatrix_csr(P) and (not isspmatrix_csr(A))):
A = A.tocsr()
elif (isspmatrix_bsr(P) and (not isspmatrix_bsr(A))):
A = A.tobsr()
A.eliminate_zeros()
levels.append(multilevel_solver.level())
levels[-1].A = A
return 0
def extend_hierarchy(levels,
strength,
aggregate,
smooth,
improve_candidates,
diagonal_dominance=False,
keep=True):
"""Extend the multigrid hierarchy.
Service routine to implement the strength of connection, aggregation,
tentative prolongation construction, and prolongation smoothing. Called by
smoothed_aggregation_solver.
"""
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
A = levels[-1].A
B = levels[-1].B
if A.symmetry == "nonsymmetric":
AH = A.H.asformat(A.format)
BH = levels[-1].BH
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength[len(levels) - 1])
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
if 'B' in kwargs:
C = evolution_strength_of_connection(A, **kwargs)
else:
C = evolution_strength_of_connection(A, B, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'predefined':
C = kwargs['C'].tocsr()
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A.tocsr()
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
# Avoid coarsening diagonally dominant rows
flag, kwargs = unpack_arg(diagonal_dominance)
if flag:
C = eliminate_diag_dom_nodes(A, C, **kwargs)
# Compute the aggregation matrix AggOp (i.e., the nodal coarsening of A).
# AggOp is a boolean matrix, where the sparsity pattern for the k-th column
# denotes the fine-grid nodes agglomerated into k-th coarse-grid node.
fn, kwargs = unpack_arg(aggregate[len(levels) - 1])
if fn == 'standard':
AggOp = standard_aggregation(C, **kwargs)[0]
elif fn == 'naive':
AggOp = naive_aggregation(C, **kwargs)[0]
elif fn == 'lloyd':
AggOp = lloyd_aggregation(C, **kwargs)[0]
elif fn == 'predefined':
AggOp = kwargs['AggOp'].tocsr()
else:
raise ValueError('unrecognized aggregation method %s' % str(fn))
# Improve near nullspace candidates by relaxing on A B = 0
fn, kwargs = unpack_arg(improve_candidates[len(levels) - 1])
if fn is not None:
b = np.zeros((A.shape[0], 1), dtype=A.dtype)
B = relaxation_as_linear_operator((fn, kwargs), A, b) * B
levels[-1].B = B
if A.symmetry == "nonsymmetric":
BH = relaxation_as_linear_operator((fn, kwargs), AH, b) * BH
levels[-1].BH = BH
# Compute the tentative prolongator, T, which is a tentative interpolation
# matrix from the coarse-grid to the fine-grid. T exactly interpolates
# B_fine = T B_coarse.
T, B = fit_candidates(AggOp, B)
if A.symmetry == "nonsymmetric":
TH, BH = fit_candidates(AggOp, BH)
# Smooth the tentative prolongator, so that it's accuracy is greatly
# improved for algebraically smooth error.
fn, kwargs = unpack_arg(smooth[len(levels) - 1])
if fn == 'jacobi':
P = jacobi_prolongation_smoother(A, T, C, B, **kwargs)
elif fn == 'richardson':
P = richardson_prolongation_smoother(A, T, **kwargs)
elif fn == 'energy':
P = energy_prolongation_smoother(A, T, C, B, None, (False, {}),
**kwargs)
elif fn is None:
P = T
else:
raise ValueError('unrecognized prolongation smoother method %s' %
str(fn))
# Compute the restriction matrix, R, which interpolates from the fine-grid
# to the coarse-grid. If A is nonsymmetric, then R must be constructed
# based on A.H. Otherwise R = P.H or P.T.
symmetry = A.symmetry
if symmetry == 'hermitian':
R = P.H
elif symmetry == 'symmetric':
R = P.T
elif symmetry == 'nonsymmetric':
fn, kwargs = unpack_arg(smooth[len(levels) - 1])
if fn == 'jacobi':
R = jacobi_prolongation_smoother(AH, TH, C, BH, **kwargs).H
elif fn == 'richardson':
R = richardson_prolongation_smoother(AH, TH, **kwargs).H
elif fn == 'energy':
R = energy_prolongation_smoother(AH, TH, C, BH, None, (False, {}),
**kwargs)
R = R.H
elif fn is None:
R = T.H
else:
raise ValueError('unrecognized prolongation smoother method %s' %
str(fn))
if keep:
levels[-1].C = C # strength of connection matrix
levels[-1].AggOp = AggOp # aggregation operator
levels[-1].T = T # tentative prolongator
levels[-1].P = P # smoothed prolongator
levels[-1].R = R # restriction operator
levels.append(multilevel_solver.level())
A = R * A * P # Galerkin operator
A.symmetry = symmetry
levels[-1].A = A
levels[-1].B = B # right near nullspace candidates
if A.symmetry == "nonsymmetric":
levels[-1].BH = BH # left near nullspace candidates
def AIR_solver(A,
strength=('classical', {'theta': 0.3 ,'norm': 'min'}),
CF='RS',
interp='one_point',
restrict='neumann',
presmoother=None,
postsmoother=('FC_jacobi', {'omega': 1.0, 'iterations': 1,
'withrho': False, 'F_iterations': 2,
'C_iterations': 0} ),
filter_operator=None,
coarse_grid_P=None,
coarse_grid_R=None,
max_levels=20, max_coarse=20,
keep=False, **kwargs):
"""Create a multilevel solver using Classical AMG (Ruge-Stuben AMG)
Parameters
----------
A : csr_matrix
Square nonsymmetric matrix in CSR format
strength : ['symmetric', 'classical', 'evolution', 'distance',
'algebraic_distance','affinity', 'energy_based', None]
Method used to determine the strength of connection between unknowns
of the linear system. Method-specific parameters may be passed in
using a tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If
strength=None, all nonzero entries of the matrix are considered strong.
CF : {string} : default 'RS'
Method used for coarse grid selection (C/F splitting)
Supported methods are RS, PMIS, PMISc, CLJP, CLJPc, and CR.
interp : {string} : default 'one-point'
Options include 'direct', 'standard', 'inject' and 'one-point'.
restrict : {string} : default 'neumann'
Options include 'air' for approximate ideal
restriction.
presmoother : {string or dict} : default None
Method used for presmoothing at each level. Method-specific parameters
may be passed in using a tuple, e.g.
presmoother=('gauss_seidel',{'sweep':'symmetric}), the default.
postsmoother : {string or dict} : default F-Jacobi
Postsmoothing method with the same usage as presmoother
filter_operator : (bool, tol) : default None
Remove small entries in operators on each level if True. Entries are
considered "small" if |a_ij| < tol |a_ii|.
coarse_grid_P : {string} : default None
Option to specify a different construction of P used in computing RAP
vs. for interpolation in an actual solve.
max_levels: {integer} : default 20
Maximum number of levels to be used in the multilevel solver.
max_coarse: {integer} : default 20
Maximum number of variables permitted on the coarse grid.
keep: {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C) and
tentative prolongation (T) are kept.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
Other Parameters
----------------
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
setup_complexity : bool
For a detailed, more accurate setup complexity, pass in
'setup_complexity' = True. This will slow down performance, but
increase accuracy of complexity count.
Notes
-----
References
----------
.. [1]
See Also
--------
aggregation.smoothed_aggregation_solver, multilevel_solver,
aggregation.rootnode_solver
"""
if ('setup_complexity' in kwargs):
if kwargs['setup_complexity'] == True:
mat_mat_complexity.__detailed__ = True
del kwargs['setup_complexity']
# preprocess A
A = A.asfptype()
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
levels = [multilevel_solver.level()]
levels[-1].A = A
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
bottom = extend_hierarchy(levels, strength, CF, interp, restrict, filter_operator,
coarse_grid_P, coarse_grid_R, keep)
if bottom:
break
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def ruge_stuben_solver(A,
strength=('classical', {'theta': 0.25}),
CF='RS',
presmoother=('gauss_seidel', {'sweep': 'symmetric'}),
postsmoother=('gauss_seidel', {'sweep': 'symmetric'}),
max_levels=10, max_coarse=500, keep=False, **kwargs):
"""Create a multilevel solver using Classical AMG (Ruge-Stuben AMG)
Parameters
----------
A : csr_matrix
Square matrix in CSR format
strength : ['symmetric', 'classical', 'evolution', None]
Method used to determine the strength of connection between unknowns
of the linear system. Method-specific parameters may be passed in
using a tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If
strength=None, all nonzero entries of the matrix are considered strong.
CF : {string} : default 'RS'
Method used for coarse grid selection (C/F splitting)
Supported methods are RS, PMIS, PMISc, CLJP, and CLJPc
presmoother : {string or dict}
Method used for presmoothing at each level. Method-specific parameters
may be passed in using a tuple, e.g.
presmoother=('gauss_seidel',{'sweep':'symmetric}), the default.
postsmoother : {string or dict}
Postsmoothing method with the same usage as presmoother
max_levels: {integer} : default 10
Maximum number of levels to be used in the multilevel solver.
max_coarse: {integer} : default 500
Maximum number of variables permitted on the coarse grid.
keep: {bool} : default False
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C) and
tentative prolongation (T) are kept.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
Examples
--------
>>> from pyamg.gallery import poisson
>>> from pyamg import ruge_stuben_solver
>>> A = poisson((10,),format='csr')
>>> ml = ruge_stuben_solver(A,max_coarse=3)
Notes
-----
"coarse_solver" is an optional argument and is the solver used at the
coarsest grid. The default is a pseudo-inverse. Most simply,
coarse_solver can be one of ['splu', 'lu', 'cholesky, 'pinv',
'gauss_seidel', ... ]. Additionally, coarse_solver may be a tuple
(fn, args), where fn is a string such as ['splu', 'lu', ...] or a callable
function, and args is a dictionary of arguments to be passed to fn.
References
----------
.. [1] Trottenberg, U., Oosterlee, C. W., and Schuller, A.,
"Multigrid" San Diego: Academic Press, 2001. Appendix A
See Also
--------
aggregation.smoothed_aggregation_solver, multilevel_solver,
aggregation.rootnode_solver
"""
levels = [multilevel_solver.level()]
# convert A to csr
if not isspmatrix_csr(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 be convertible to csr_matrix')
# preprocess A
A = A.asfptype()
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
levels[-1].A = A
while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse:
extend_hierarchy(levels, strength, CF, keep)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml
def rootnode_solver(A, B=None, BH=None,
symmetry='hermitian', strength='symmetric',
aggregate='standard', smooth='energy',
presmoother=('block_gauss_seidel',
{'sweep': 'symmetric'}),
postsmoother=('block_gauss_seidel',
{'sweep': 'symmetric'}),
improve_candidates=('block_gauss_seidel',
{'sweep': 'symmetric',
'iterations': 4}),
max_levels=10, max_coarse=10,
diagonal_dominance=False, keep=False, **kwargs):
"""Create a multilevel solver using root-node based Smoothed Aggregation (SA).
See the notes below, for the major differences with the classical-style
smoothed aggregation solver in aggregation.smoothed_aggregation_solver.
Parameters
----------
A : csr_matrix, bsr_matrix
Sparse NxN matrix in CSR or BSR format
B : None, array_like
Right near-nullspace candidates stored in the columns of an NxK array.
K must be >= the blocksize of A (see reference [2011OlScTu]_). The default value
B=None is equivalent to choosing the constant over each block-variable,
B=np.kron(np.ones((A.shape[0]/blocksize(A), 1)), np.eye(blocksize(A)))
BH : None, array_like
Left near-nullspace candidates stored in the columns of an NxK array.
BH is only used if symmetry is 'nonsymmetric'. K must be >= the
blocksize of A (see reference [2011OlScTu]_). The default value B=None is
equivalent to choosing the constant over each block-variable,
B=np.kron(np.ones((A.shape[0]/blocksize(A), 1)), np.eye(blocksize(A)))
symmetry : string
'symmetric' refers to both real and complex symmetric
'hermitian' refers to both complex Hermitian and real Hermitian
'nonsymmetric' i.e. nonsymmetric in a hermitian sense
Note that for the strictly real case, symmetric and hermitian are
the same
Note that this flag does not denote definiteness of the operator.
strength : list
Method used to determine the strength of connection between unknowns of
the linear system. Method-specific parameters may be passed in using a
tuple, e.g. strength=('symmetric',{'theta' : 0.25 }). If strength=None,
all nonzero entries of the matrix are considered strong.
aggregate : list
Method used to aggregate nodes.
smooth : list
Method used to smooth the tentative prolongator. Method-specific
parameters may be passed in using a tuple, e.g. smooth=
('energy',{'krylov' : 'gmres'}). Only 'energy' and None are valid
prolongation smoothing options.
presmoother : tuple, string, list
Defines the presmoother for the multilevel cycling. The default block
Gauss-Seidel option defaults to point-wise Gauss-Seidel, if the matrix
is CSR or is a BSR matrix with blocksize of 1. See notes below for
varying this parameter on a per level basis.
postsmoother : tuple, string, list
Same as presmoother, except defines the postsmoother.
improve_candidates : tuple, string, list
The ith entry defines the method used to improve the candidates B on
level i. If the list is shorter than max_levels, then the last entry
will define the method for all levels lower. If tuple or string, then
this single relaxation descriptor defines improve_candidates on all
levels.
The list elements are relaxation descriptors of the form used for
presmoother and postsmoother. A value of None implies no action on B.
max_levels : integer
Maximum number of levels to be used in the multilevel solver.
max_coarse : integer
Maximum number of variables permitted on the coarse grid.
diagonal_dominance : bool, tuple
If True (or the first tuple entry is True), then avoid coarsening
diagonally dominant rows. The second tuple entry requires a
dictionary, where the key value 'theta' is used to tune the diagonal
dominance threshold.
keep : bool
Flag to indicate keeping extra operators in the hierarchy for
diagnostics. For example, if True, then strength of connection (C),
tentative prolongation (T), aggregation (AggOp), and arrays
storing the C-points (Cpts) and F-points (Fpts) are kept at
each level.
Other Parameters
----------------
cycle_type : ['V','W','F']
Structrure of multigrid cycle
coarse_solver : ['splu', 'lu', 'cholesky, 'pinv', 'gauss_seidel', ... ]
Solver used at the coarsest level of the MG hierarchy.
Optionally, may be a tuple (fn, args), where fn is a string such as
['splu', 'lu', ...] or a callable function, and args is a dictionary of
arguments to be passed to fn.
Returns
-------
ml : multilevel_solver
Multigrid hierarchy of matrices and prolongation operators
See Also
--------
multilevel_solver, aggregation.smoothed_aggregation_solver,
classical.ruge_stuben_solver
Notes
-----
- Root-node style SA differs from classical SA primarily by preserving
and identity block in the interpolation operator, P. Each aggregate
has a "root-node" or "center-node" associated with it, and this
root-node is injected from the coarse grid to the fine grid. The
injection corresponds to the identity block.
- Only smooth={'energy', None} is supported for prolongation
smoothing. See reference [2011OlScTu]_ below for more details on why the
'energy' prolongation smoother is the natural counterpart to
root-node style SA.
- The additional parameters are passed through as arguments to
multilevel_solver. Refer to pyamg.multilevel_solver for additional
documentation.
- At each level, four steps are executed in order to define the coarser
level operator.
1. Matrix A is given and used to derive a strength matrix, C.
2. Based on the strength matrix, indices are grouped or aggregated.
3. The aggregates define coarse nodes and a tentative prolongation
operator T is defined by injection
4. The tentative prolongation operator is smoothed by a relaxation
scheme to improve the quality and extent of interpolation from the
aggregates to fine nodes.
- The parameters smooth, strength, aggregate, presmoother, postsmoother
can be varied on a per level basis. For different methods on
different levels, use a list as input so that the i-th entry defines
the method at the i-th level. If there are more levels in the
hierarchy than list entries, the last entry will define the method
for all levels lower.
Examples are:
smooth=[('jacobi', {'omega':1.0}), None, 'jacobi']
presmoother=[('block_gauss_seidel', {'sweep':symmetric}), 'sor']
aggregate=['standard', 'naive']
strength=[('symmetric', {'theta':0.25}), ('symmetric', {'theta':0.08})]
- Predefined strength of connection and aggregation schemes can be
specified. These options are best used together, but aggregation can
be predefined while strength of connection is not.
For predefined strength of connection, use a list consisting of
tuples of the form ('predefined', {'C' : C0}), where C0 is a
csr_matrix and each degree-of-freedom in C0 represents a supernode.
For instance to predefine a three-level hierarchy, use
[('predefined', {'C' : C0}), ('predefined', {'C' : C1}) ].
Similarly for predefined aggregation, use a list of tuples. For
instance to predefine a three-level hierarchy, use [('predefined',
{'AggOp' : Agg0}), ('predefined', {'AggOp' : Agg1}) ], where the
dimensions of A, Agg0 and Agg1 are compatible, i.e. Agg0.shape[1] ==
A.shape[0] and Agg1.shape[1] == Agg0.shape[0]. Each AggOp is a
csr_matrix.
Because this is a root-nodes solver, if a member of the predefined
aggregation list is predefined, it must be of the form
('predefined', {'AggOp' : Agg, 'Cnodes' : Cnodes}).
Examples
--------
>>> from pyamg import rootnode_solver
>>> from pyamg.gallery import poisson
>>> from scipy.sparse.linalg import cg
>>> import numpy as np
>>> A = poisson((100, 100), format='csr') # matrix
>>> b = np.ones((A.shape[0])) # RHS
>>> ml = rootnode_solver(A) # AMG solver
>>> M = ml.aspreconditioner(cycle='V') # preconditioner
>>> x, info = cg(A, b, tol=1e-8, maxiter=30, M=M) # solve with CG
References
----------
.. [1996VaMa] Vanek, P. and Mandel, J. and Brezina, M.,
"Algebraic Multigrid by Smoothed Aggregation for
Second and Fourth Order Elliptic Problems",
Computing, vol. 56, no. 3, pp. 179--196, 1996.
http://citeseer.ist.psu.edu/vanek96algebraic.html
.. [2011OlScTu] Olson, L. and Schroder, J. and Tuminaro, R.,
"A general interpolation strategy for algebraic
multigrid using energy minimization", SIAM Journal
on Scientific Computing (SISC), vol. 33, pp.
966--991, 2011.
"""
if not (isspmatrix_csr(A) or isspmatrix_bsr(A)):
try:
A = csr_matrix(A)
warn("Implicit conversion of A to CSR",
SparseEfficiencyWarning)
except BaseException:
raise TypeError('Argument A must have type csr_matrix, \
bsr_matrix, or be convertible to csr_matrix')
A = A.asfptype()
if (symmetry != 'symmetric') and (symmetry != 'hermitian') and \
(symmetry != 'nonsymmetric'):
raise ValueError('expected \'symmetric\', \'nonsymmetric\' \
or \'hermitian\' for the symmetry parameter ')
A.symmetry = symmetry
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix')
# Right near nullspace candidates use constant for each variable as default
if B is None:
B = np.kron(np.ones((int(A.shape[0]/blocksize(A)), 1), dtype=A.dtype),
np.eye(blocksize(A)))
else:
B = np.asarray(B, dtype=A.dtype)
if len(B.shape) == 1:
B = B.reshape(-1, 1)
if B.shape[0] != A.shape[0]:
raise ValueError('The near null-space modes B have incorrect \
dimensions for matrix A')
if B.shape[1] < blocksize(A):
raise ValueError('B.shape[1] must be >= the blocksize of A')
# Left near nullspace candidates
if A.symmetry == 'nonsymmetric':
if BH is None:
BH = B.copy()
else:
BH = np.asarray(BH, dtype=A.dtype)
if len(BH.shape) == 1:
BH = BH.reshape(-1, 1)
if BH.shape[1] != B.shape[1]:
raise ValueError('The number of left and right near \
null-space modes B and BH, must be equal')
if BH.shape[0] != A.shape[0]:
raise ValueError('The near null-space modes BH have \
incorrect dimensions for matrix A')
# Levelize the user parameters, so that they become lists describing the
# desired user option on each level.
max_levels, max_coarse, strength =\
levelize_strength_or_aggregation(strength, max_levels, max_coarse)
max_levels, max_coarse, aggregate =\
levelize_strength_or_aggregation(aggregate, max_levels, max_coarse)
improve_candidates =\
levelize_smooth_or_improve_candidates(improve_candidates, max_levels)
smooth = levelize_smooth_or_improve_candidates(smooth, max_levels)
# Construct multilevel structure
levels = []
levels.append(multilevel_solver.level())
levels[-1].A = A # matrix
# Append near nullspace candidates
levels[-1].B = B # right candidates
if A.symmetry == 'nonsymmetric':
levels[-1].BH = BH # left candidates
while len(levels) < max_levels and \
int(levels[-1].A.shape[0]/blocksize(levels[-1].A)) > max_coarse:
extend_hierarchy(levels, strength, aggregate, smooth,
improve_candidates, diagonal_dominance, keep)
ml = multilevel_solver(levels, **kwargs)
change_smoothers(ml, presmoother, postsmoother)
return ml