def distance_measure_common(A, func, alpha, R, k, epsilon):
# create test vectors
x = relaxation_vectors(A, R, k, alpha)
# apply distance measure function to vectors
d = func(x)
# drop distances to self
(rows, cols) = A.nonzero()
weak = np.where(rows == cols)[0]
d[weak] = 0
C = sparse.csr_matrix((d, (rows, cols)), shape=A.shape)
C.eliminate_zeros()
# remove weak connections
# removes entry e from a row if e > theta * min of all entries in the row
amg_core.apply_distance_filter(C.shape[0], epsilon, C.indptr,
C.indices, C.data)
C.eliminate_zeros()
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0/C.data
# Put an identity on the diagonal
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
return C
def distance_measure_common(A, func, alpha, R, k, epsilon):
"""
Helper function to create strength of connection matrix
from a function applied to relaxation vectors.
"""
# create test vectors
x = relaxation_vectors(A, R, k, alpha)
# apply distance measure function to vectors
d = func(x)
# drop distances to self
(rows, cols) = A.nonzero()
weak = np.where(rows == cols)[0]
d[weak] = 0
C = sparse.csr_matrix((d, (rows, cols)), shape=A.shape)
C.eliminate_zeros()
# remove weak connections
# removes entry e from a row if e > theta * min of all entries in the row
amg_core.apply_distance_filter(C.shape[0], epsilon, C.indptr, C.indices,
C.data)
C.eliminate_zeros()
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0 / C.data
# Put an identity on the diagonal
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
return C
def distance_measure_common(A, func, alpha, R, k, epsilon, cost):
# create test vectors
x = relaxation_vectors(A, R, k, alpha)
cost[0] += R * k
# apply distance measure function to vectors
(rows, cols) = A.nonzero()
[d, temp] = func(x, rows, cols, cost)
cost[0] += float(temp) / A.nnz
# drop distances to self
weak = np.where(rows == cols)[0]
d[weak] = 0
C = sparse.csr_matrix((d, (rows, cols)), shape=A.shape)
C.eliminate_zeros()
cost[0] += 1
# remove weak connections
# removes entry e from a row if e > theta * min of all entries in the row
amg_core.apply_distance_filter(C.shape[0], epsilon, C.indptr, C.indices,
C.data)
C.eliminate_zeros()
cost[0] += 2 * float(C.nnz) / A.nnz
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0 / C.data
cost[0] += float(C.nnz) / A.nnz # Note this is one WU of divides
# Put an identity on the diagonal
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
cost[0] += float(C.shape[0]) / A.nnz
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
cost[0] += float(
C.nnz) / A.nnz # Only 1 WU because largest element same as
# smallest found when removing weak connections.
return C
def distance_measure_common(A, func, alpha, R, k, epsilon, cost):
# create test vectors
x = relaxation_vectors(A, R, k, alpha)
cost[0] += R*k
# apply distance measure function to vectors
(rows, cols) = A.nonzero()
[d,temp] = func(x, rows, cols, cost)
cost[0] += float(temp) / A.nnz
# drop distances to self
weak = np.where(rows == cols)[0]
d[weak] = 0
C = sparse.csr_matrix((d, (rows, cols)), shape=A.shape)
C.eliminate_zeros()
cost[0] += 1
# remove weak connections
# removes entry e from a row if e > theta * min of all entries in the row
amg_core.apply_distance_filter(C.shape[0], epsilon, C.indptr,
C.indices, C.data)
C.eliminate_zeros()
cost[0] += 2 * float(C.nnz) / A.nnz
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0/C.data
cost[0] += float(C.nnz) / A.nnz # Note this is one WU of divides
# Put an identity on the diagonal
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
cost[0] += float(C.shape[0]) / A.nnz
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
cost[0] += float(C.nnz) / A.nnz # Only 1 WU because largest element same as
# smallest found when removing weak connections.
return C
def evolution_strength_of_connection(A,
B=None,
epsilon=4.0,
k=2,
proj_type="l2",
weighting='diagonal',
symmetrize_measure=True,
cost=[0]):
"""
Construct strength of connection matrix using an Evolution-based measure
Parameters
----------
A : {csr_matrix, bsr_matrix}
Sparse NxN matrix
B : {string, array}
If B=None, then the near nullspace vector used is all ones. If B is
an (NxK) array, then B is taken to be the near nullspace vectors.
epsilon : scalar
Drop tolerance
k : integer
ODE num time steps, step size is assumed to be 1/rho(DinvA)
proj_type : {'l2','D_A'}
Define norm for constrained min prob, i.e. define projection
weighting : {string}
'block', 'diagonal' or 'local' construction of the D-inverse
used to precondition A before "evolving" delta-functions. The
local option is the cheapest.
Returns
-------
Atilde : {csr_matrix}
Sparse matrix of strength values
References
----------
.. [1] Olson, L. N., Schroder, J., Tuminaro, R. S.,
"A New Perspective on Strength Measures in Algebraic Multigrid",
submitted, June, 2008.
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import evolution_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = evolution_strength_of_connection(A, np.ones((A.shape[0],1)))
"""
# local imports for evolution_strength_of_connection
from pyamg.util.utils import scale_rows, get_block_diag, scale_columns
from pyamg.util.linalg import approximate_spectral_radius
# ====================================================================
# Check inputs
if epsilon < 1.0:
raise ValueError("expected epsilon > 1.0")
if k <= 0:
raise ValueError("number of time steps must be > 0")
if proj_type not in ['l2', 'D_A']:
raise ValueError("proj_type must be 'l2' or 'D_A'")
if (not sparse.isspmatrix_csr(A)) and (not sparse.isspmatrix_bsr(A)):
raise TypeError("expected csr_matrix or bsr_matrix")
# ====================================================================
# Format A and B correctly.
# B must be in mat format, this isn't a deep copy
if B is None:
Bmat = np.mat(np.ones((A.shape[0], 1), dtype=A.dtype))
else:
Bmat = np.mat(B)
# Is matrix A CSR?
if (not sparse.isspmatrix_csr(A)):
numPDEs = A.blocksize[0]
csrflag = False
else:
numPDEs = 1
csrflag = True
# Pre-process A. We need A in CSR, to be devoid of explicit 0's, have
# sorted indices and be scaled by D-inverse
if weighting == 'block':
Dinv = get_block_diag(A, blocksize=numPDEs, inv_flag=True)
Dinv = sparse.bsr_matrix(
(Dinv, np.arange(Dinv.shape[0]), np.arange(Dinv.shape[0] + 1)),
shape=A.shape)
Dinv_A = (Dinv * A).tocsr()
cost[0] += 1
elif weighting == 'diagonal':
D = A.diagonal()
Dinv = get_diagonal(A, norm_eq=False, inv=True)
Dinv[D == 0] = 1.0
Dinv_A = scale_rows(A, Dinv, copy=True)
cost[0] += 1
elif weighting == 'local':
D = np.abs(A) * np.ones((A.shape[0], 1), dtype=A.dtype)
Dinv = np.zeros_like(D)
Dinv[D != 0] = 1.0 / np.abs(D[D != 0])
Dinv[D == 0] = 1.0
Dinv_A = scale_rows(A, Dinv, copy=True)
cost[0] += 1
else:
raise ValueError('Unrecognized weighting for Evolution measure')
A = A.tocsr()
A.eliminate_zeros()
A.sort_indices()
# Handle preliminaries for the algorithm
dimen = A.shape[1]
NullDim = Bmat.shape[1]
if weighting == 'diagonal' or weighting == 'block':
# Get spectral radius of Dinv*A, scales the time step size for the ODE
rho_DinvA = approximate_spectral_radius(Dinv_A)
cost[0] += 15 # 15 lanczos iterations to approximate spectral radius
else:
# Using local weighting, no need for spectral radius
rho_DinvA = 1.0
# Calculate D_A for later use in the minimization problem
if proj_type == "D_A":
D = A.diagonal()
D_A = sparse.spdiags([D], [0], dimen, dimen, format='csr')
else:
D_A = sparse.eye(dimen, dimen, format="csr", dtype=A.dtype)
# Calculate (I - delta_t Dinv A)^k
# We transpose the product, so that we can efficiently access
# the columns in CSR format. We want the columns (not rows) because
# strength is based on the columns of (I - delta_t Dinv A)^k, i.e.,
# relaxed delta functions
# Calculate the number of time steps that can be done by squaring, and
# the number of time steps that must be done incrementally
nsquare = int(np.log2(k))
ninc = k - 2**nsquare
# Calculate one time step
I = sparse.eye(dimen, dimen, format="csr", dtype=A.dtype)
Atilde = (I - (1.0 / rho_DinvA) * Dinv_A)
Atilde = Atilde.T.tocsr()
cost[0] += 1
# Construct a sparsity mask for Atilde that will restrict Atilde^T to the
# nonzero pattern of A, with the added constraint that row i of Atilde^T
# retains only the nonzeros that are also in the same PDE as i.
mask = A.copy()
# Restrict to same PDE
if numPDEs > 1:
row_length = np.diff(mask.indptr)
my_pde = np.mod(np.arange(dimen), numPDEs)
my_pde = np.repeat(my_pde, row_length)
mask.data[np.mod(mask.indices, numPDEs) != my_pde] = 0.0
del row_length, my_pde
mask.eliminate_zeros()
# If the total number of time steps is a power of two, then there is
# a very efficient computational short-cut. Otherwise, we support
# other numbers of time steps, through an inefficient algorithm.
if ninc > 0:
warn("The most efficient time stepping for the Evolution Strength\
Method is done in powers of two.\nYou have chosen " + str(k) +
" time steps.")
JacobiStep = csr_matrix(Atilde, copy=True)
# Calculate (Atilde^nsquare)^T = (Atilde^T)^nsquare
for i in range(nsquare):
cost[0] += mat_mat_complexity(Atilde, Atilde)
Atilde = Atilde * Atilde
for i in range(ninc):
cost[0] += mat_mat_complexity(Atilde, JacobiStep)
Atilde = Atilde * JacobiStep
del JacobiStep
# Apply mask to Atilde, zeros in mask have already been eliminated at
# start of routine.
mask.data[:] = 1.0
Atilde = Atilde.multiply(mask)
Atilde.eliminate_zeros()
Atilde.sort_indices()
cost[0] += Atilde.nnz / float(A.nnz)
elif nsquare == 0:
if numPDEs > 1:
# Apply mask to Atilde, zeros in mask have already been eliminated
# at start of routine.
mask.data[:] = 1.0
Atilde = Atilde.multiply(mask)
Atilde.eliminate_zeros()
Atilde.sort_indices()
else:
# Use computational short-cut for case (ninc == 0) and (nsquare > 0)
# Calculate Atilde^k only at the sparsity pattern of mask.
for i in range(nsquare - 1):
cost[0] += mat_mat_complexity(Atilde, Atilde)
Atilde = Atilde * Atilde
# Call incomplete mat-mat mult
AtildeCSC = Atilde.tocsc()
AtildeCSC.sort_indices()
mask.sort_indices()
Atilde.sort_indices()
amg_core.incomplete_mat_mult_csr(Atilde.indptr, Atilde.indices,
Atilde.data, AtildeCSC.indptr,
AtildeCSC.indices, AtildeCSC.data,
mask.indptr, mask.indices, mask.data,
dimen)
cost[0] += mat_mat_complexity(Atilde, mask, incomplete=True) / float(
A.nnz)
del AtildeCSC, Atilde
Atilde = mask
Atilde.eliminate_zeros()
Atilde.sort_indices()
del Dinv, Dinv_A, mask
# Calculate strength based on constrained min problem of
# min( z - B*x ), such that
# (B*x)|_i = z|_i, i.e. they are equal at point i
# z = (I - (t/k) Dinv A)^k delta_i
#
# Strength is defined as the relative point-wise approx. error between
# B*x and z. We don't use the full z in this problem, only that part of
# z that is in the sparsity pattern of A.
#
# Can use either the D-norm, and inner product, or l2-norm and inner-prod
# to solve the constrained min problem. Using D gives scale invariance.
#
# This is a quadratic minimization problem with a linear constraint, so
# we can build a linear system and solve it to find the critical point,
# i.e. minimum.
#
# We exploit a known shortcut for the case of NullDim = 1. The shortcut is
# mathematically equivalent to the longer constrained min. problem
if NullDim == 1:
# Use shortcut to solve constrained min problem if B is only a vector
# Strength(i,j) = | 1 - (z(i)/b(j))/(z(j)/b(i)) |
# These ratios can be calculated by diagonal row and column scalings
# Create necessary vectors for scaling Atilde
# Its not clear what to do where B == 0. This is an
# an easy programming solution, that may make sense.
Bmat_forscaling = np.ravel(Bmat)
Bmat_forscaling[Bmat_forscaling == 0] = 1.0
DAtilde = Atilde.diagonal()
DAtildeDivB = np.ravel(DAtilde) / Bmat_forscaling
cost[0] += Atilde.shape[0] / float(A.nnz)
# Calculate best approximation, z_tilde, in span(B)
# Importantly, scale_rows and scale_columns leave zero entries
# in the matrix. For previous implementations this was useful
# because we assume data and Atilde.data are the same length below
data = Atilde.data.copy()
Atilde.data[:] = 1.0
Atilde = scale_rows(Atilde, DAtildeDivB)
Atilde = scale_columns(Atilde, np.ravel(Bmat_forscaling))
cost[0] += 2.0 * Atilde.nnz / float(A.nnz)
# If angle in the complex plane between z and z_tilde is
# greater than 90 degrees, then weak. We can just look at the
# dot product to determine if angle is greater than 90 degrees.
angle = np.real(Atilde.data) * np.real(data) +\
np.imag(Atilde.data) * np.imag(data)
angle = angle < 0.0
angle = np.array(angle, dtype=bool)
cost[0] += Atilde.nnz / float(A.nnz)
if Atilde.dtype is 'complex':
cost[0] += Atilde.nnz / float(A.nnz)
# Calculate Approximation ratio
Atilde.data = Atilde.data / data
cost[0] += Atilde.nnz / float(A.nnz)
# If approximation ratio is less than tol, then weak connection
weak_ratio = (np.abs(Atilde.data) < 1e-4)
# Calculate Approximation error
Atilde.data = abs(1.0 - Atilde.data)
cost[0] += Atilde.nnz / float(A.nnz)
# Set small ratios and large angles to weak
Atilde.data[weak_ratio] = 0.0
Atilde.data[angle] = 0.0
# Set near perfect connections to 1e-4
Atilde.eliminate_zeros()
Atilde.data[Atilde.data < np.sqrt(np.finfo(float).eps)] = 1e-4
del data, weak_ratio, angle
else:
# For use in computing local B_i^H*B, precompute the element-wise
# multiply of each column of B with each other column. We also scale
# by 2.0 to account for BDB's eventual use in a constrained
# minimization problem
BDBCols = int(np.sum(np.arange(NullDim + 1)))
BDB = np.zeros((dimen, BDBCols), dtype=A.dtype)
counter = 0
for i in range(NullDim):
for j in range(i, NullDim):
BDB[:, counter] = 2.0 *\
(np.conjugate(np.ravel(np.asarray(B[:, i]))) *
np.ravel(np.asarray(D_A * B[:, j])))
counter = counter + 1
cost[0] += B.shape[0] / float(A.nnz)
# Choose tolerance for dropping "numerically zero" values later
t = Atilde.dtype.char
eps = np.finfo(np.float).eps
feps = np.finfo(np.single).eps
geps = np.finfo(np.longfloat).eps
_array_precision = {'f': 0, 'd': 1, 'g': 2, 'F': 0, 'D': 1, 'G': 2}
tol = {0: feps * 1e3, 1: eps * 1e6, 2: geps * 1e6}[_array_precision[t]]
# Use constrained min problem to define strength.
# This function is doing similar to NullDim=1 with more bad guys.
# Complexity accounts for computing the block inverse, and
# hat{z_i} = B_i*x, hat{z_i} .* hat{z_i},
# hat{z_i} = hat{z_i} / z_i, and abs(1.0 - hat{z_i}).
cost[0] += (Atilde.nnz * (3 + NullDim) +
(NullDim**3) * dimen) / float(A.nnz)
amg_core.evolution_strength_helper(
Atilde.data, Atilde.indptr, Atilde.indices, Atilde.shape[0],
np.ravel(np.asarray(B)),
np.ravel(np.asarray((D_A * np.conjugate(B)).T)),
np.ravel(np.asarray(BDB)), BDBCols, NullDim, tol)
Atilde.eliminate_zeros()
# All of the strength values are real by this point, so ditch the complex
# part
Atilde.data = np.array(np.real(Atilde.data), dtype=float)
# Apply drop tolerance
if epsilon != np.inf:
cost[0] += Atilde.nnz / float(A.nnz)
amg_core.apply_distance_filter(dimen, epsilon, Atilde.indptr,
Atilde.indices, Atilde.data)
Atilde.eliminate_zeros()
# Set diagonal to 1.0, as each point is strongly connected to itself.
I = sparse.eye(dimen, dimen, format="csr")
I.data -= Atilde.diagonal()
Atilde = Atilde + I
cost[0] += Atilde.shape[0] / float(A.nnz)
# If converted BSR to CSR, convert back and return amalgamated matrix,
# i.e. the sparsity structure of the blocks of Atilde
if not csrflag:
Atilde = Atilde.tobsr(blocksize=(numPDEs, numPDEs))
n_blocks = Atilde.indices.shape[0]
blocksize = Atilde.blocksize[0] * Atilde.blocksize[1]
CSRdata = np.zeros((n_blocks, ))
amg_core.min_blocks(n_blocks, blocksize,
np.ravel(np.asarray(Atilde.data)), CSRdata)
# Atilde = sparse.csr_matrix((data, row, col), shape=(*,*))
Atilde = sparse.csr_matrix((CSRdata, Atilde.indices, Atilde.indptr),
shape=(int(Atilde.shape[0] / numPDEs),
int(Atilde.shape[1] / numPDEs)))
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the algebraic distances computed here
Atilde.data = 1.0 / Atilde.data
cost[0] += Atilde.nnz / float(A.nnz)
# Scale C by the largest magnitude entry in each row
Atilde = scale_rows_by_largest_entry(Atilde)
cost[0] += Atilde.nnz / float(A.nnz)
# Symmetrize
if symmetrize_measure:
Atilde = 0.5 * (Atilde + Atilde.T)
cost[0] += Atilde.nnz / float(A.nnz)
return Atilde
def distance_strength_of_connection(A,
V,
theta=2.0,
relative_drop=True,
cost=[0]):
"""
Distance based strength-of-connection
Parameters
----------
A : csr_matrix or bsr_matrix
Square, sparse matrix in CSR or BSR format
V : array
Coordinates of the vertices of the graph of A
theta : float
Drop tolerance of distance between points, see relative_drop
relative_drop : bool
If false, then a connection must be within a distance of theta
from a point to be strongly connected.
If true, then the closest connection is always strong, and other points
must be within theta times the smallest distance to be strong
Returns
-------
C : csr_matrix
C(i,j) = distance(point_i, point_j)
Strength of connection matrix where strength values are
distances, i.e. the smaller the value, the stronger the connection.
Sparsity pattern of C is copied from A.
Notes
-----
- theta is a drop tolerance that is applied row-wise
- If a BSR matrix given, then the return matrix is still CSR. The strength
is given between super nodes based on the BSR block size.
Examples
--------
>>> from pyamg.gallery import load_example
>>> from pyamg.strength import distance_strength_of_connection
>>> data = load_example('airfoil')
>>> A = data['A'].tocsr()
>>> S = distance_strength_of_connection(data['A'], data['vertices'])
"""
# Amalgamate for the supernode case
if sparse.isspmatrix_bsr(A):
sn = int(A.shape[0] / A.blocksize[0])
u = np.ones((A.data.shape[0], ))
A = sparse.csr_matrix((u, A.indices, A.indptr), shape=(sn, sn))
if not sparse.isspmatrix_csr(A):
warn("Implicit conversion of A to csr", sparse.SparseEfficiencyWarning)
A = sparse.csr_matrix(A)
dim = V.shape[1]
# Create two arrays for differencing the different coordinates such
# that C(i,j) = distance(point_i, point_j)
cols = A.indices
rows = np.repeat(np.arange(A.shape[0]), A.indptr[1:] - A.indptr[0:-1])
# Insert difference for each coordinate into C
C = (V[rows, 0] - V[cols, 0])**2
for d in range(1, dim):
C += (V[rows, d] - V[cols, d])**2
C = np.sqrt(C)
C[C < 1e-6] = 1e-6
C = sparse.csr_matrix((C, A.indices.copy(), A.indptr.copy()),
shape=A.shape)
# 2 len(rows) operations initially, 3 each loop iteration,
# and one after --> 3*dim*len(rows) / A.nnz WUs = 3*dim WUs
cost[0] += 3 * dim
# Apply drop tolerance
if relative_drop is True:
if theta != np.inf:
amg_core.apply_distance_filter(C.shape[0], theta, C.indptr,
C.indices, C.data)
cost[0] += float(2.0 * C.nnz) / A.nnz
else:
amg_core.apply_absolute_distance_filter(C.shape[0], theta, C.indptr,
C.indices, C.data)
cost[0] += float(C.nnz) / A.nnz
C.eliminate_zeros()
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
cost[0] += float(C.shape[0]) / A.nnz
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0 / C.data
cost[0] += float(C.nnz) / A.nnz
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
# Assume largest entry can be tracked in applying distance filter.
# 1 WU to scale matrix.
cost[0] += float(C.nnz) / A.nnz
return C
def energy_based_strength_of_connection(A, theta=0.0, k=2, cost=[0]):
"""
Compute a strength of connection matrix using an energy-based measure.
Parameters
----------
A : {sparse-matrix}
matrix from which to generate strength of connection information
theta : {float}
Threshold parameter in [0,1]
k : {int}
Number of relaxation steps used to generate strength information
Returns
-------
S : {csr_matrix}
Matrix graph defining strong connections. The sparsity pattern
of S matches that of A. For BSR matrices, S is a reduced strength
of connection matrix that describes connections between supernodes.
Notes
-----
This method relaxes with weighted-Jacobi in order to approximate the
matrix inverse. A normalized change of energy is then used to define
point-wise strength of connection values. Specifically, let v be the
approximation to the i-th column of the inverse, then
(S_ij)^2 = <v_j, v_j>_A / <v, v>_A,
where v_j = v, such that entry j in v has been zeroed out. As is common,
larger values imply a stronger connection.
Current implementation is a very slow pure-python implementation for
experimental purposes, only.
References
----------
.. [1] Brannick, Brezina, MacLachlan, Manteuffel, McCormick.
"An Energy-Based AMG Coarsening Strategy",
Numerical Linear Algebra with Applications,
vol. 13, pp. 133-148, 2006.
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import energy_based_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = energy_based_strength_of_connection(A, 0.0)
"""
if (theta < 0):
raise ValueError('expected a positive theta')
if not sparse.isspmatrix(A):
raise ValueError('expected sparse matrix')
if (k < 0):
raise ValueError('expected positive number of steps')
if not isinstance(k, int):
raise ValueError('expected integer')
if sparse.isspmatrix_bsr(A):
bsr_flag = True
numPDEs = A.blocksize[0]
if A.blocksize[0] != A.blocksize[1]:
raise ValueError('expected square blocks in BSR matrix A')
else:
bsr_flag = False
# Convert A to csc and Atilde to csr
if sparse.isspmatrix_csr(A):
Atilde = A.copy()
A = A.tocsc()
else:
A = A.tocsc()
Atilde = A.copy()
Atilde = Atilde.tocsr()
# Calculate the weighted-Jacobi parameter
from pyamg.util.linalg import approximate_spectral_radius
D = A.diagonal()
Dinv = 1.0 / D
Dinv[D == 0] = 0.0
Dinv = sparse.csc_matrix(
(Dinv, (np.arange(A.shape[0]), np.arange(A.shape[1]))), shape=A.shape)
DinvA = Dinv * A
omega = 1.0 / approximate_spectral_radius(DinvA)
del DinvA
# Approximate A-inverse with k steps of w-Jacobi and a zero initial guess
S = sparse.csc_matrix(A.shape, dtype=A.dtype) # empty matrix
I = sparse.eye(A.shape[0], A.shape[1], format='csc')
for i in range(k + 1):
S = S + omega * (Dinv * (I - A * S))
# Calculate the strength entries in S column-wise, but only strength
# values at the sparsity pattern of A
for i in range(Atilde.shape[0]):
v = np.mat(S[:, i].todense())
Av = np.mat(A * v)
denom = np.sqrt(np.conjugate(v).T * Av)
# replace entries in row i with strength values
for j in range(Atilde.indptr[i], Atilde.indptr[i + 1]):
col = Atilde.indices[j]
vj = v[col].copy()
v[col] = 0.0
# = (||v_j||_A - ||v||_A) / ||v||_A
val = np.sqrt(np.conjugate(v).T * A * v) / denom - 1.0
# Negative values generally imply a weak connection
if val > -0.01:
Atilde.data[j] = abs(val)
else:
Atilde.data[j] = 0.0
v[col] = vj
# Apply drop tolerance
Atilde = classical_strength_of_connection_abs(Atilde, theta=theta)
Atilde.eliminate_zeros()
# Put ones on the diagonal
Atilde = Atilde + I.tocsr()
Atilde.sort_indices()
# Amalgamate Atilde for the BSR case, using ones for all strong connections
if bsr_flag:
Atilde = Atilde.tobsr(blocksize=(numPDEs, numPDEs))
nblocks = Atilde.indices.shape[0]
uone = np.ones((nblocks, ))
Atilde = sparse.csr_matrix((uone, Atilde.indices, Atilde.indptr),
shape=(int(Atilde.shape[0] / numPDEs),
int(Atilde.shape[1] / numPDEs)))
# Scale C by the largest magnitude entry in each row
Atilde = scale_rows_by_largest_entry(Atilde)
return Atilde
def classical_strength_of_connection(A,
theta=0.25,
block=None,
norm='abs',
cost=[0]):
"""
Return a strength of connection matrix using the classical AMG measure
An off-diagonal entry A[i,j] is a strong connection iff::
| A[i,j] | >= theta * max(| A[i,k] |), where k != i (norm='abs')
-A[i,j] >= theta * max(| A[i,k] |), where k != i (norm='min')
Parameters
----------
A : csr_matrix or bsr_matrix
Square, sparse matrix in CSR or BSR format
theta : float
Threshold parameter in [0,1].
block : string, default None for CSR matrix and 'block' for BSR matrix
How to treat block structure of A:
None : Compute SOC based on A as CSR matrix.
'block' : Compute SOC based on norm of blocks of A.
'amalgamate' : Compute SOC based on A as CSR matrix, then compute
norm of blocks in SOC matrix for a block SOC.
norm : 'string', default 'abs'
Option to compute SOC between elements or blocks:
'abs' : C_ij = k, where k is the maximum absolute value in block C_ij
'min' : C_ij = k, where k is the minimum (negative) value in block C_ij
'fro' : C_ij = k, where k is the Frobenius norm of block C_ij
- Only valid for block matrices, block='block'
Returns
-------
S : csr_matrix
Matrix graph defining strong connections. S[i,j]=1 if vertex i
is strongly influenced by vertex j.
See Also
--------
symmetric_strength_of_connection : symmetric measure used in SA
evolution_strength_of_connection : relaxation based strength measure
Notes
-----
- A symmetric A does not necessarily yield a symmetric strength matrix S
- Calls C++ function classical_strength_of_connection
References
----------
.. [1] Briggs, W. L., Henson, V. E., McCormick, S. F., "A multigrid
tutorial", Second edition. Society for Industrial and Applied
Mathematics (SIAM), Philadelphia, PA, 2000. xii+193 pp.
ISBN: 0-89871-462-1
.. [2] Trottenberg, U., Oosterlee, C. W., Schuller, A., "Multigrid",
Academic Press, Inc., San Diego, CA, 2001. xvi+631 pp.
ISBN: 0-12-701070-X
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import classical_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = classical_strength_of_connection(A, 0.0)
"""
if (theta < 0 or theta > 1):
raise ValueError('expected theta in [0,1]')
if (theta < 0 or theta > 1):
raise ValueError('expected theta in [0,1]')
if sparse.isspmatrix_bsr(A):
blocksize = A.blocksize[0]
else:
blocksize = 1
# Block structure considered before computing SOC
if (block == 'block') or sparse.isspmatrix_bsr(A):
R, C = A.blocksize
if (R != C) or (R < 1):
raise ValueError('Matrix must have square blocks')
N = int(A.shape[0] / R)
# SOC based on maximum absolute value element in each block
if norm == 'abs':
data = np.max(np.max(np.abs(A.data), axis=1), axis=1)
cost[0] += 1
# SOC based on hard minimum of entry in each off-diagonal block
elif norm == 'min':
data = np.min(np.min(A.data, axis=1), axis=1)
cost[0] += 1
# SOC based on Frobenius norms of blocks
elif norm == 'fro':
data = (np.conjugate(A.data) * A.data).reshape(-1,
R * C).sum(axis=1)
cost[0] += 1
else:
raise ValueError("Invalid choice of norm.")
data[np.abs(data) < 1e-16] = 0.0
S_rowptr = np.empty_like(A.indptr)
S_colinds = np.empty_like(A.indices)
S_data = np.empty_like(data)
if norm == 'abs' or norm == 'fro':
amg_core.classical_strength_of_connection_abs(
N, theta, A.indptr, A.indices, data, S_rowptr, S_colinds,
S_data)
elif norm == 'min':
amg_core.classical_strength_of_connection_min(
N, theta, A.indptr, A.indices, data, S_rowptr, S_colinds,
S_data)
else:
raise ValueError("Unrecognized option for norm.")
# One pass through nnz to find largest entry, one to filter
S = sparse.csr_matrix((S_data, S_colinds, S_rowptr), shape=[N, N])
cost[0] += 2
# Take magnitude and scale by largest entry
S.data = np.abs(S.data)
S = scale_rows_by_largest_entry(S)
S.eliminate_zeros()
# Assume largest entry can be tracked from filtering.
# 1 WU to scale matrix.
cost[0] += float(S.nnz) / A.nnz
return S
# SOC computed based on A as CSR
else:
S_rowptr = np.empty_like(A.indptr)
S_colinds = np.empty_like(A.indices)
S_data = np.empty_like(A.data)
if norm == 'abs' or norm == 'fro':
amg_core.classical_strength_of_connection_abs(
A.shape[0], theta, A.indptr, A.indices, A.data, S_rowptr,
S_colinds, S_data)
elif norm == 'min':
amg_core.classical_strength_of_connection_min(
A.shape[0], theta, A.indptr, A.indices, A.data, S_rowptr,
S_colinds, S_data)
else:
raise ValueError("Unrecognized option for norm.")
# One pass through nnz to find largest entry, one to filter
S = sparse.csr_matrix((S_data, S_colinds, S_rowptr), shape=A.shape)
cost[0] += 2
if blocksize > 1 and block == 'amalgamate':
S = amalgamate(S, blocksize, norm=norm)
# Take magnitude and scale by largest entry
S.data = np.abs(S.data)
S = scale_rows_by_largest_entry(S)
S.eliminate_zeros()
# Assume largest entry can be tracked from filtering.
# 1 WU to scale matrix.
cost[0] += float(S.nnz) / A.nnz
return S
def symmetric_strength_of_connection(A, theta=0, cost=[0]):
"""
Compute strength of connection matrix using the standard symmetric measure
An off-diagonal connection A[i,j] is strong iff::
abs(A[i,j]) >= theta * sqrt( abs(A[i,i]) * abs(A[j,j]) )
Parameters
----------
A : csr_matrix
Matrix graph defined in sparse format. Entry A[i,j] describes the
strength of edge [i,j]
theta : float
Threshold parameter (positive).
Returns
-------
S : csr_matrix
Matrix graph defining strong connections. S[i,j]=1 if vertex i
is strongly influenced by vertex j.
See Also
--------
symmetric_strength_of_connection : symmetric measure used in SA
evolution_strength_of_connection : relaxation based strength measure
Notes
-----
- For vector problems, standard strength measures may produce
undesirable aggregates. A "block approach" from Vanek et al. is used
to replace vertex comparisons with block-type comparisons. A
connection between nodes i and j in the block case is strong if::
||AB[i,j]|| >= theta * sqrt( ||AB[i,i]||*||AB[j,j]|| ) where AB[k,l]
is the matrix block (degrees of freedom) associated with nodes k and
l and ||.|| is a matrix norm, such a Frobenius.
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
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import symmetric_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = symmetric_strength_of_connection(A, 0.0)
"""
if theta < 0:
raise ValueError('expected a positive theta')
if sparse.isspmatrix_csr(A):
S_rowptr = np.empty_like(A.indptr)
S_colinds = np.empty_like(A.indices)
S_data = np.empty_like(A.data)
fn = amg_core.symmetric_strength_of_connection
fn(A.shape[0], theta, A.indptr, A.indices, A.data, S_rowptr, S_colinds,
S_data)
# Assume takes ~0.5 pass to find diagonals, 1 pass to filter
cost[0] += 1.5
S = sparse.csr_matrix((S_data, S_colinds, S_rowptr), shape=A.shape)
elif sparse.isspmatrix_bsr(A):
M, N = A.shape
R, C = A.blocksize
if R != C:
raise ValueError('matrix must have square blocks')
if theta == 0:
data = np.ones(len(A.indices), dtype=A.dtype)
S = sparse.csr_matrix((data, A.indices.copy(), A.indptr.copy()),
shape=(int(M / R), int(N / C)))
else:
# the strength of connection matrix is based on the
# Frobenius norms of the blocks
data = (np.conjugate(A.data) * A.data).reshape(-1,
R * C).sum(axis=1)
cost[0] += 1
A = sparse.csr_matrix((data, A.indices, A.indptr),
shape=(int(M / R), int(N / C)))
return symmetric_strength_of_connection(A, theta, cost)
else:
raise TypeError('expected csr_matrix or bsr_matrix')
# Strength represents "distance", so take the magnitude
S.data = np.abs(S.data)
# Scale S by the largest magnitude entry in each row
S = scale_rows_by_largest_entry(S)
# One pass to find largest entry, 1 pass to scale all elements
# by it and adjust signs
cost[0] += 2 * float(S.nnz) / A.nnz
return S
def algebraic_distance(A, alpha=0.5, R=5, k=20, theta=0.1, p=2):
"""Construct an AMG strength of connection matrix using an algebraic
distance measure.
Parameters
----------
A : {csr_matrix}
Sparse NxN matrix
alpha : scalar
Weight for Jacobi
R : integer
Number of random vectors
k : integer
Number of relaxation passes
theta : scalar
Drop values larger than theta
p : scalar or inf
p-norm of the measure
Returns
-------
C : {csr_matrix}
Sparse matrix of strength values
References
----------
.. [1] "Advanced Coarsening Schemes for Graph Partitioning"
by Ilya Safro, Peter Sanders, and Christian Schulz
Notes
-----
No unit testing yet.
Does not handle BSR matrices yet.
"""
print A.format
if not sparse.isspmatrix_csr(A):
warn("Implicit conversion of A to csr", sparse.SparseEfficiencyWarning)
A = sparse.csr_matrix(A)
if alpha < 0:
raise ValueError('expected alpha>0')
if R <= 0 or not isinstance(R, int):
raise ValueError('expected integer R>0')
if k <= 0 or not isinstance(k, int):
raise ValueError('expected integer k>0')
if theta < 0:
raise ValueError('expected theta>0.0')
if p < 1:
raise ValueError('expected p>1 or equal to numpy.inf')
# random n x R block in column ordering
n = A.shape[0]
x = np.random.rand(n * R) - 0.5
x = np.reshape(x, (n, R), order='F')
b = np.zeros((n, 1))
# relax k times
for r in range(0, R):
pyamg.relaxation.jacobi(A, x[:, r], b, iterations=k, omega=alpha)
# get distance measure d
C = A.tocoo()
I = C.row
J = C.col
# TODO : this is not right vvvvvvv
if p != np.inf:
d = np.sum((x[I] - x[J])**p, axis=1)**(1.0 / p)
else:
d = np.abs(x[I] - x[J]).max(axis=1)
del d
# drop weak connections larger than theta
weak = np.where(C.data > theta)[0]
C.data[weak] = 0
C = C.tocsr()
C.eliminate_zeros()
# Strength represents "distance", so take the magnitude
C.data = np.abs(C.data)
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0 / C.data
# Put an identity on the diagonal
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
return C
def classical_strength_of_connection(A, theta=0.0):
"""
Return a strength of connection matrix using the classical AMG measure
An off-diagonal entry A[i,j] is a strong connection iff::
| A[i,j] | >= theta * max(| A[i,k] |), where k != i
Parameters
----------
A : csr_matrix or bsr_matrix
Square, sparse matrix in CSR or BSR format
theta : float
Threshold parameter in [0,1].
Returns
-------
S : csr_matrix
Matrix graph defining strong connections. S[i,j]=1 if vertex i
is strongly influenced by vertex j.
See Also
--------
symmetric_strength_of_connection : symmetric measure used in SA
evolution_strength_of_connection : relaxation based strength measure
Notes
-----
- A symmetric A does not necessarily yield a symmetric strength matrix S
- Calls C++ function classical_strength_of_connection
- The version as implemented is designed form M-matrices. Trottenberg et
al. use max A[i,k] over all negative entries, which is the same. A
positive edge weight never indicates a strong connection.
References
----------
.. [1] Briggs, W. L., Henson, V. E., McCormick, S. F., "A multigrid
tutorial", Second edition. Society for Industrial and Applied
Mathematics (SIAM), Philadelphia, PA, 2000. xii+193 pp.
ISBN: 0-89871-462-1
.. [2] Trottenberg, U., Oosterlee, C. W., Schuller, A., "Multigrid",
Academic Press, Inc., San Diego, CA, 2001. xvi+631 pp.
ISBN: 0-12-701070-X
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import classical_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = classical_strength_of_connection(A, 0.0)
"""
if sparse.isspmatrix_bsr(A):
blocksize = A.blocksize[0]
else:
blocksize = 1
if not sparse.isspmatrix_csr(A):
warn("Implicit conversion of A to csr", sparse.SparseEfficiencyWarning)
A = sparse.csr_matrix(A)
if (theta < 0 or theta > 1):
raise ValueError('expected theta in [0,1]')
Sp = np.empty_like(A.indptr)
Sj = np.empty_like(A.indices)
Sx = np.empty_like(A.data)
fn = amg_core.classical_strength_of_connection
fn(A.shape[0], theta, A.indptr, A.indices, A.data, Sp, Sj, Sx)
S = sparse.csr_matrix((Sx, Sj, Sp), shape=A.shape)
if blocksize > 1:
S = amalgamate(S, blocksize)
# Strength represents "distance", so take the magnitude
S.data = np.abs(S.data)
# Scale S by the largest magnitude entry in each row
S = scale_rows_by_largest_entry(S)
return S
def classical_strength_of_connection(A, theta=0.25, block=None, norm='abs', cost=[0]):
"""
Return a strength of connection matrix using the classical AMG measure
An off-diagonal entry A[i,j] is a strong connection iff::
| A[i,j] | >= theta * max(| A[i,k] |), where k != i (norm='abs')
-A[i,j] >= theta * max(| A[i,k] |), where k != i (norm='min')
Parameters
----------
A : csr_matrix or bsr_matrix
Square, sparse matrix in CSR or BSR format
theta : float
Threshold parameter in [0,1].
block : string, default None for CSR matrix and 'block' for BSR matrix
How to treat block structure of A:
None : Compute SOC based on A as CSR matrix.
'block' : Compute SOC based on norm of blocks of A.
'amalgamate' : Compute SOC based on A as CSR matrix, then compute
norm of blocks in SOC matrix for a block SOC.
norm : 'string', default 'abs'
Option to compute SOC between elements or blocks:
'abs' : C_ij = k, where k is the maximum absolute value in block C_ij
'min' : C_ij = k, where k is the minimum (negative) value in block C_ij
'fro' : C_ij = k, where k is the Frobenius norm of block C_ij
- Only valid for block matrices, block='block'
Returns
-------
S : csr_matrix
Matrix graph defining strong connections. S[i,j]=1 if vertex i
is strongly influenced by vertex j.
See Also
--------
symmetric_strength_of_connection : symmetric measure used in SA
evolution_strength_of_connection : relaxation based strength measure
Notes
-----
- A symmetric A does not necessarily yield a symmetric strength matrix S
- Calls C++ function classical_strength_of_connection
References
----------
.. [1] Briggs, W. L., Henson, V. E., McCormick, S. F., "A multigrid
tutorial", Second edition. Society for Industrial and Applied
Mathematics (SIAM), Philadelphia, PA, 2000. xii+193 pp.
ISBN: 0-89871-462-1
.. [2] Trottenberg, U., Oosterlee, C. W., Schuller, A., "Multigrid",
Academic Press, Inc., San Diego, CA, 2001. xvi+631 pp.
ISBN: 0-12-701070-X
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import classical_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = classical_strength_of_connection(A, 0.0)
"""
if (theta < 0 or theta > 1):
raise ValueError('expected theta in [0,1]')
if (theta < 0 or theta > 1):
raise ValueError('expected theta in [0,1]')
if sparse.isspmatrix_bsr(A):
blocksize = A.blocksize[0]
else:
blocksize = 1
# Block structure considered before computing SOC
if (block == 'block') or sparse.isspmatrix_bsr(A):
R, C = A.blocksize
if (R != C) or (R < 1):
raise ValueError('Matrix must have square blocks')
N = int(A.shape[0] / R)
# SOC based on maximum absolute value element in each block
if norm == 'abs':
data = np.max(np.max(np.abs(A.data),axis=1),axis=1)
cost[0] += 1
# SOC based on hard minimum of entry in each off-diagonal block
elif norm == 'min':
data = np.min(np.min(A.data,axis=1),axis=1)
cost[0] += 1
# SOC based on Frobenius norms of blocks
elif norm == 'fro':
data = (np.conjugate(A.data) * A.data).reshape(-1, R*C).sum(axis=1)
cost[0] += 1
else:
raise ValueError("Invalid choice of norm.")
data[np.abs(data)<1e-16] = 0.0
S_rowptr = np.empty_like(A.indptr)
S_colinds = np.empty_like(A.indices)
S_data = np.empty_like(data)
if norm == 'abs' or norm == 'fro':
amg_core.classical_strength_of_connection_abs(N, theta, A.indptr, A.indices, data,
S_rowptr, S_colinds, S_data)
elif norm == 'min':
amg_core.classical_strength_of_connection_min(N, theta, A.indptr, A.indices, data,
S_rowptr, S_colinds, S_data)
else:
raise ValueError("Unrecognized option for norm.")
# One pass through nnz to find largest entry, one to filter
S = sparse.csr_matrix((S_data, S_colinds, S_rowptr), shape=[N, N])
cost[0] += 2
# Take magnitude and scale by largest entry
S.data = np.abs(S.data)
S = scale_rows_by_largest_entry(S)
S.eliminate_zeros()
# Assume largest entry can be tracked from filtering.
# 1 WU to scale matrix.
cost[0] += float(S.nnz) / A.nnz
return S
# SOC computed based on A as CSR
else:
S_rowptr = np.empty_like(A.indptr)
S_colinds = np.empty_like(A.indices)
S_data = np.empty_like(A.data)
if norm == 'abs' or norm == 'fro':
amg_core.classical_strength_of_connection_abs(A.shape[0], theta, A.indptr,
A.indices, A.data, S_rowptr, S_colinds, S_data)
elif norm == 'min':
amg_core.classical_strength_of_connection_min(A.shape[0], theta, A.indptr,
A.indices, A.data, S_rowptr, S_colinds, S_data)
else:
raise ValueError("Unrecognized option for norm.")
# One pass through nnz to find largest entry, one to filter
S = sparse.csr_matrix((S_data, S_colinds, S_rowptr), shape=A.shape)
cost[0] += 2
if blocksize > 1 and block == 'amalgamate':
S = amalgamate(S, blocksize, norm=norm)
# Take magnitude and scale by largest entry
S.data = np.abs(S.data)
S = scale_rows_by_largest_entry(S)
S.eliminate_zeros()
# Assume largest entry can be tracked from filtering.
# 1 WU to scale matrix.
cost[0] += float(S.nnz) / A.nnz
return S
def classical_strength_of_connection(A, theta=0.0):
"""
Return a strength of connection matrix using the classical AMG measure
An off-diagonal entry A[i,j] is a strong connection iff::
| A[i,j] | >= theta * max(| A[i,k] |), where k != i
Parameters
----------
A : csr_matrix or bsr_matrix
Square, sparse matrix in CSR or BSR format
theta : float
Threshold parameter in [0,1].
Returns
-------
S : csr_matrix
Matrix graph defining strong connections. S[i,j]=1 if vertex i
is strongly influenced by vertex j.
See Also
--------
symmetric_strength_of_connection : symmetric measure used in SA
evolution_strength_of_connection : relaxation based strength measure
Notes
-----
- A symmetric A does not necessarily yield a symmetric strength matrix S
- Calls C++ function classical_strength_of_connection
- The version as implemented is designed form M-matrices. Trottenberg et
al. use max A[i,k] over all negative entries, which is the same. A
positive edge weight never indicates a strong connection.
References
----------
.. [1] Briggs, W. L., Henson, V. E., McCormick, S. F., "A multigrid
tutorial", Second edition. Society for Industrial and Applied
Mathematics (SIAM), Philadelphia, PA, 2000. xii+193 pp.
ISBN: 0-89871-462-1
.. [2] Trottenberg, U., Oosterlee, C. W., Schuller, A., "Multigrid",
Academic Press, Inc., San Diego, CA, 2001. xvi+631 pp.
ISBN: 0-12-701070-X
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import classical_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = classical_strength_of_connection(A, 0.0)
"""
if sparse.isspmatrix_bsr(A):
blocksize = A.blocksize[0]
else:
blocksize = 1
if not sparse.isspmatrix_csr(A):
warn("Implicit conversion of A to csr", sparse.SparseEfficiencyWarning)
A = sparse.csr_matrix(A)
if (theta < 0 or theta > 1):
raise ValueError('expected theta in [0,1]')
Sp = np.empty_like(A.indptr)
Sj = np.empty_like(A.indices)
Sx = np.empty_like(A.data)
fn = amg_core.classical_strength_of_connection
fn(A.shape[0], theta, A.indptr, A.indices, A.data, Sp, Sj, Sx)
S = sparse.csr_matrix((Sx, Sj, Sp), shape=A.shape)
if blocksize > 1:
S = amalgamate(S, blocksize)
# Strength represents "distance", so take the magnitude
S.data = np.abs(S.data)
# Scale S by the largest magnitude entry in each row
S = scale_rows_by_largest_entry(S)
return S
def algebraic_distance(A, alpha=0.5, R=5, k=20, theta=0.1, p=2):
"""Construct an AMG strength of connection matrix using an algebraic
distance measure.
Parameters
----------
A : {csr_matrix}
Sparse NxN matrix
alpha : scalar
Weight for Jacobi
R : integer
Number of random vectors
k : integer
Number of relaxation passes
theta : scalar
Drop values larger than theta
p : scalar or inf
p-norm of the measure
Returns
-------
C : {csr_matrix}
Sparse matrix of strength values
References
----------
.. [1] "Advanced Coarsening Schemes for Graph Partitioning"
by Ilya Safro, Peter Sanders, and Christian Schulz
Notes
-----
No unit testing yet.
Does not handle BSR matrices yet.
"""
print A.format
if not sparse.isspmatrix_csr(A):
warn("Implicit conversion of A to csr", sparse.SparseEfficiencyWarning)
A = sparse.csr_matrix(A)
if alpha < 0:
raise ValueError('expected alpha>0')
if R <= 0 or not isinstance(R, int):
raise ValueError('expected integer R>0')
if k <= 0 or not isinstance(k, int):
raise ValueError('expected integer k>0')
if theta < 0:
raise ValueError('expected theta>0.0')
if p < 1:
raise ValueError('expected p>1 or equal to numpy.inf')
# random n x R block in column ordering
n = A.shape[0]
x = np.random.rand(n * R) - 0.5
x = np.reshape(x, (n, R), order='F')
b = np.zeros((n, 1))
# relax k times
for r in range(0, R):
pyamg.relaxation.jacobi(A, x[:, r], b, iterations=k, omega=alpha)
# get distance measure d
C = A.tocoo()
I = C.row
J = C.col
# TODO : this is not right vvvvvvv
if p != np.inf:
d = np.sum((x[I] - x[J])**p, axis=1)**(1.0/p)
else:
d = np.abs(x[I] - x[J]).max(axis=1)
del d
# drop weak connections larger than theta
weak = np.where(C.data > theta)[0]
C.data[weak] = 0
C = C.tocsr()
C.eliminate_zeros()
# Strength represents "distance", so take the magnitude
C.data = np.abs(C.data)
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0/C.data
# Put an identity on the diagonal
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
return C
def evolution_strength_of_connection(A, B='ones', epsilon=4.0, k=2,
proj_type="l2", block_flag=False,
symmetrize_measure=True):
"""
Construct strength of connection matrix using an Evolution-based measure
Parameters
----------
A : {csr_matrix, bsr_matrix}
Sparse NxN matrix
B : {string, array}
If B='ones', then the near nullspace vector used is all ones. If B is
an (NxK) array, then B is taken to be the near nullspace vectors.
epsilon : scalar
Drop tolerance
k : integer
ODE num time steps, step size is assumed to be 1/rho(DinvA)
proj_type : {'l2','D_A'}
Define norm for constrained min prob, i.e. define projection
block_flag : {boolean}
If True, use a block D inverse as preconditioner for A during
weighted-Jacobi
Returns
-------
Atilde : {csr_matrix}
Sparse matrix of strength values
References
----------
.. [1] Olson, L. N., Schroder, J., Tuminaro, R. S.,
"A New Perspective on Strength Measures in Algebraic Multigrid",
submitted, June, 2008.
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import evolution_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = evolution_strength_of_connection(A, np.ones((A.shape[0],1)))
"""
# local imports for evolution_strength_of_connection
from pyamg.util.utils import scale_rows, get_block_diag, scale_columns
from pyamg.util.linalg import approximate_spectral_radius
# ====================================================================
# Check inputs
if epsilon < 1.0:
raise ValueError("expected epsilon > 1.0")
if k <= 0:
raise ValueError("number of time steps must be > 0")
if proj_type not in ['l2', 'D_A']:
raise ValueError("proj_type must be 'l2' or 'D_A'")
if (not sparse.isspmatrix_csr(A)) and (not sparse.isspmatrix_bsr(A)):
raise TypeError("expected csr_matrix or bsr_matrix")
# ====================================================================
# Format A and B correctly.
# B must be in mat format, this isn't a deep copy
if B == 'ones':
Bmat = np.mat(np.ones((A.shape[0], 1), dtype=A.dtype))
else:
Bmat = np.mat(B)
# Pre-process A. We need A in CSR, to be devoid of explicit 0's and have
# sorted indices
if (not sparse.isspmatrix_csr(A)):
csrflag = False
numPDEs = A.blocksize[0]
D = A.diagonal()
# Calculate Dinv*A
if block_flag:
Dinv = get_block_diag(A, blocksize=numPDEs, inv_flag=True)
Dinv = sparse.bsr_matrix((Dinv, np.arange(Dinv.shape[0]),
np.arange(Dinv.shape[0] + 1)),
shape=A.shape)
Dinv_A = (Dinv * A).tocsr()
else:
Dinv = np.zeros_like(D)
mask = (D != 0.0)
Dinv[mask] = 1.0 / D[mask]
Dinv[D == 0] = 1.0
Dinv_A = scale_rows(A, Dinv, copy=True)
A = A.tocsr()
else:
csrflag = True
numPDEs = 1
D = A.diagonal()
Dinv = np.zeros_like(D)
mask = (D != 0.0)
Dinv[mask] = 1.0 / D[mask]
Dinv[D == 0] = 1.0
Dinv_A = scale_rows(A, Dinv, copy=True)
A.eliminate_zeros()
A.sort_indices()
# Handle preliminaries for the algorithm
dimen = A.shape[1]
NullDim = Bmat.shape[1]
# Get spectral radius of Dinv*A, this will be used to scale the time step
# size for the ODE
rho_DinvA = approximate_spectral_radius(Dinv_A)
# Calculate D_A for later use in the minimization problem
if proj_type == "D_A":
D_A = sparse.spdiags([D], [0], dimen, dimen, format='csr')
else:
D_A = sparse.eye(dimen, dimen, format="csr", dtype=A.dtype)
# Calculate (I - delta_t Dinv A)^k
# In order to later access columns, we calculate the transpose in
# CSR format so that columns will be accessed efficiently
# Calculate the number of time steps that can be done by squaring, and
# the number of time steps that must be done incrementally
nsquare = int(np.log2(k))
ninc = k - 2**nsquare
# Calculate one time step
I = sparse.eye(dimen, dimen, format="csr", dtype=A.dtype)
Atilde = (I - (1.0/rho_DinvA)*Dinv_A)
Atilde = Atilde.T.tocsr()
# Construct a sparsity mask for Atilde that will restrict Atilde^T to the
# nonzero pattern of A, with the added constraint that row i of Atilde^T
# retains only the nonzeros that are also in the same PDE as i.
mask = A.copy()
# Restrict to same PDE
if numPDEs > 1:
row_length = np.diff(mask.indptr)
my_pde = np.mod(range(dimen), numPDEs)
my_pde = np.repeat(my_pde, row_length)
mask.data[np.mod(mask.indices, numPDEs) != my_pde] = 0.0
del row_length, my_pde
mask.eliminate_zeros()
# If the total number of time steps is a power of two, then there is
# a very efficient computational short-cut. Otherwise, we support
# other numbers of time steps, through an inefficient algorithm.
if ninc > 0:
warn("The most efficient time stepping for the Evolution Strength\
Method is done in powers of two.\nYou have chosen " + str(k) +
" time steps.")
# Calculate (Atilde^nsquare)^T = (Atilde^T)^nsquare
for i in range(nsquare):
Atilde = Atilde*Atilde
JacobiStep = (I - (1.0/rho_DinvA)*Dinv_A).T.tocsr()
for i in range(ninc):
Atilde = Atilde*JacobiStep
del JacobiStep
# Apply mask to Atilde, zeros in mask have already been eliminated at
# start of routine.
mask.data[:] = 1.0
Atilde = Atilde.multiply(mask)
Atilde.eliminate_zeros()
Atilde.sort_indices()
elif nsquare == 0:
if numPDEs > 1:
# Apply mask to Atilde, zeros in mask have already been eliminated
# at start of routine.
mask.data[:] = 1.0
Atilde = Atilde.multiply(mask)
Atilde.eliminate_zeros()
Atilde.sort_indices()
else:
# Use computational short-cut for case (ninc == 0) and (nsquare > 0)
# Calculate Atilde^k only at the sparsity pattern of mask.
for i in range(nsquare-1):
Atilde = Atilde*Atilde
# Call incomplete mat-mat mult
AtildeCSC = Atilde.tocsc()
AtildeCSC.sort_indices()
mask.sort_indices()
Atilde.sort_indices()
amg_core.incomplete_mat_mult_csr(Atilde.indptr, Atilde.indices,
Atilde.data, AtildeCSC.indptr,
AtildeCSC.indices, AtildeCSC.data,
mask.indptr, mask.indices, mask.data,
dimen)
del AtildeCSC, Atilde
Atilde = mask
Atilde.eliminate_zeros()
Atilde.sort_indices()
del Dinv, Dinv_A, mask
# Calculate strength based on constrained min problem of
# min( z - B*x ), such that
# (B*x)|_i = z|_i, i.e. they are equal at point i
# z = (I - (t/k) Dinv A)^k delta_i
#
# Strength is defined as the relative point-wise approx. error between
# B*x and z. We don't use the full z in this problem, only that part of
# z that is in the sparsity pattern of A.
#
# Can use either the D-norm, and inner product, or l2-norm and inner-prod
# to solve the constrained min problem. Using D gives scale invariance.
#
# This is a quadratic minimization problem with a linear constraint, so
# we can build a linear system and solve it to find the critical point,
# i.e. minimum.
#
# We exploit a known shortcut for the case of NullDim = 1. The shortcut is
# mathematically equivalent to the longer constrained min. problem
if NullDim == 1:
# Use shortcut to solve constrained min problem if B is only a vector
# Strength(i,j) = | 1 - (z(i)/b(j))/(z(j)/b(i)) |
# These ratios can be calculated by diagonal row and column scalings
# Create necessary vectors for scaling Atilde
# Its not clear what to do where B == 0. This is an
# an easy programming solution, that may make sense.
Bmat_forscaling = np.ravel(Bmat)
Bmat_forscaling[Bmat_forscaling == 0] = 1.0
DAtilde = Atilde.diagonal()
DAtildeDivB = np.ravel(DAtilde) / Bmat_forscaling
# Calculate best approximation, z_tilde, in span(B)
# Importantly, scale_rows and scale_columns leave zero entries
# in the matrix. For previous implementations this was useful
# because we assume data and Atilde.data are the same length below
data = Atilde.data.copy()
Atilde.data[:] = 1.0
Atilde = scale_rows(Atilde, DAtildeDivB)
Atilde = scale_columns(Atilde, np.ravel(Bmat_forscaling))
# If angle in the complex plane between z and z_tilde is
# greater than 90 degrees, then weak. We can just look at the
# dot product to determine if angle is greater than 90 degrees.
angle = np.real(Atilde.data) * np.real(data) +\
np.imag(Atilde.data) * np.imag(data)
angle = angle < 0.0
angle = np.array(angle, dtype=bool)
# Calculate Approximation ratio
Atilde.data = Atilde.data/data
# If approximation ratio is less than tol, then weak connection
weak_ratio = (np.abs(Atilde.data) < 1e-4)
# Calculate Approximation error
Atilde.data = abs(1.0 - Atilde.data)
# Set small ratios and large angles to weak
Atilde.data[weak_ratio] = 0.0
Atilde.data[angle] = 0.0
# Set near perfect connections to 1e-4
Atilde.eliminate_zeros()
Atilde.data[Atilde.data < np.sqrt(np.finfo(float).eps)] = 1e-4
del data, weak_ratio, angle
else:
# For use in computing local B_i^H*B, precompute the element-wise
# multiply of each column of B with each other column. We also scale
# by 2.0 to account for BDB's eventual use in a constrained
# minimization problem
BDBCols = int(np.sum(range(NullDim + 1)))
BDB = np.zeros((dimen, BDBCols), dtype=A.dtype)
counter = 0
for i in range(NullDim):
for j in range(i, NullDim):
BDB[:, counter] = 2.0 *\
(np.conjugate(np.ravel(np.asarray(B[:, i]))) *
np.ravel(np.asarray(D_A * B[:, j])))
counter = counter + 1
# Choose tolerance for dropping "numerically zero" values later
t = Atilde.dtype.char
eps = np.finfo(np.float).eps
feps = np.finfo(np.single).eps
geps = np.finfo(np.longfloat).eps
_array_precision = {'f': 0, 'd': 1, 'g': 2, 'F': 0, 'D': 1, 'G': 2}
tol = {0: feps*1e3, 1: eps*1e6, 2: geps*1e6}[_array_precision[t]]
# Use constrained min problem to define strength
amg_core.evolution_strength_helper(Atilde.data,
Atilde.indptr,
Atilde.indices,
Atilde.shape[0],
np.ravel(np.asarray(B)),
np.ravel(np.asarray(
(D_A * np.conjugate(B)).T)),
np.ravel(np.asarray(BDB)),
BDBCols, NullDim, tol)
Atilde.eliminate_zeros()
# All of the strength values are real by this point, so ditch the complex
# part
Atilde.data = np.array(np.real(Atilde.data), dtype=float)
# Apply drop tolerance
if symmetrize_measure:
Atilde = 0.5*(Atilde + Atilde.T)
if epsilon != np.inf:
amg_core.apply_distance_filter(dimen, epsilon, Atilde.indptr,
Atilde.indices, Atilde.data)
Atilde.eliminate_zeros()
# Set diagonal to 1.0, as each point is strongly connected to itself.
I = sparse.eye(dimen, dimen, format="csr")
I.data -= Atilde.diagonal()
Atilde = Atilde + I
# If converted BSR to CSR, convert back and return amalgamated matrix,
# i.e. the sparsity structure of the blocks of Atilde
if not csrflag:
Atilde = Atilde.tobsr(blocksize=(numPDEs, numPDEs))
n_blocks = Atilde.indices.shape[0]
blocksize = Atilde.blocksize[0]*Atilde.blocksize[1]
CSRdata = np.zeros((n_blocks,))
amg_core.min_blocks(n_blocks, blocksize,
np.ravel(np.asarray(Atilde.data)), CSRdata)
# Atilde = sparse.csr_matrix((data, row, col), shape=(*,*))
Atilde = sparse.csr_matrix((CSRdata, Atilde.indices, Atilde.indptr),
shape=(Atilde.shape[0] / numPDEs,
Atilde.shape[1] / numPDEs))
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the algebraic distances computed here
Atilde.data = 1.0/Atilde.data
# Scale C by the largest magnitude entry in each row
Atilde = scale_rows_by_largest_entry(Atilde)
return Atilde
def energy_based_strength_of_connection(A, theta=0.0, k=2):
"""
Compute a strength of connection matrix using an energy-based measure.
Parameters
----------
A : {sparse-matrix}
matrix from which to generate strength of connection information
theta : {float}
Threshold parameter in [0,1]
k : {int}
Number of relaxation steps used to generate strength information
Returns
-------
S : {csr_matrix}
Matrix graph defining strong connections. The sparsity pattern
of S matches that of A. For BSR matrices, S is a reduced strength
of connection matrix that describes connections between supernodes.
Notes
-----
This method relaxes with weighted-Jacobi in order to approximate the
matrix inverse. A normalized change of energy is then used to define
point-wise strength of connection values. Specifically, let v be the
approximation to the i-th column of the inverse, then
(S_ij)^2 = <v_j, v_j>_A / <v, v>_A,
where v_j = v, such that entry j in v has been zeroed out. As is common,
larger values imply a stronger connection.
Current implementation is a very slow pure-python implementation for
experimental purposes, only.
References
----------
.. [1] Brannick, Brezina, MacLachlan, Manteuffel, McCormick.
"An Energy-Based AMG Coarsening Strategy",
Numerical Linear Algebra with Applications,
vol. 13, pp. 133-148, 2006.
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import energy_based_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = energy_based_strength_of_connection(A, 0.0)
"""
if (theta < 0):
raise ValueError('expected a positive theta')
if not sparse.isspmatrix(A):
raise ValueError('expected sparse matrix')
if (k < 0):
raise ValueError('expected positive number of steps')
if not isinstance(k, int):
raise ValueError('expected integer')
if sparse.isspmatrix_bsr(A):
bsr_flag = True
numPDEs = A.blocksize[0]
if A.blocksize[0] != A.blocksize[1]:
raise ValueError('expected square blocks in BSR matrix A')
else:
bsr_flag = False
# Convert A to csc and Atilde to csr
if sparse.isspmatrix_csr(A):
Atilde = A.copy()
A = A.tocsc()
else:
A = A.tocsc()
Atilde = A.copy()
Atilde = Atilde.tocsr()
# Calculate the weighted-Jacobi parameter
from pyamg.util.linalg import approximate_spectral_radius
D = A.diagonal()
Dinv = 1.0 / D
Dinv[D == 0] = 0.0
Dinv = sparse.csc_matrix((Dinv, (np.arange(A.shape[0]),
np.arange(A.shape[1]))), shape=A.shape)
DinvA = Dinv*A
omega = 1.0/approximate_spectral_radius(DinvA)
del DinvA
# Approximate A-inverse with k steps of w-Jacobi and a zero initial guess
S = sparse.csc_matrix(A.shape, dtype=A.dtype) # empty matrix
I = sparse.eye(A.shape[0], A.shape[1], format='csc')
for i in range(k+1):
S = S + omega*(Dinv*(I - A * S))
# Calculate the strength entries in S column-wise, but only strength
# values at the sparsity pattern of A
for i in range(Atilde.shape[0]):
v = np.mat(S[:, i].todense())
Av = np.mat(A * v)
denom = np.sqrt(np.conjugate(v).T * Av)
# replace entries in row i with strength values
for j in range(Atilde.indptr[i], Atilde.indptr[i+1]):
col = Atilde.indices[j]
vj = v[col].copy()
v[col] = 0.0
# = (||v_j||_A - ||v||_A) / ||v||_A
val = np.sqrt(np.conjugate(v).T * A * v)/denom - 1.0
# Negative values generally imply a weak connection
if val > -0.01:
Atilde.data[j] = abs(val)
else:
Atilde.data[j] = 0.0
v[col] = vj
# Apply drop tolerance
Atilde = classical_strength_of_connection(Atilde, theta=theta)
Atilde.eliminate_zeros()
# Put ones on the diagonal
Atilde = Atilde + I.tocsr()
Atilde.sort_indices()
# Amalgamate Atilde for the BSR case, using ones for all strong connections
if bsr_flag:
Atilde = Atilde.tobsr(blocksize=(numPDEs, numPDEs))
nblocks = Atilde.indices.shape[0]
uone = np.ones((nblocks,))
Atilde = sparse.csr_matrix((uone, Atilde.indices, Atilde.indptr),
shape=(
Atilde.shape[0] / numPDEs,
Atilde.shape[1] / numPDEs))
# Scale C by the largest magnitude entry in each row
Atilde = scale_rows_by_largest_entry(Atilde)
return Atilde
def distance_strength_of_connection(A, V, theta=2.0, relative_drop=True):
"""
Distance based strength-of-connection
Parameters
----------
A : csr_matrix or bsr_matrix
Square, sparse matrix in CSR or BSR format
V : array
Coordinates of the vertices of the graph of A
relative_drop : bool
If false, then a connection must be within a distance of theta
from a point to be strongly connected.
If true, then the closest connection is always strong, and other points
must be within theta times the smallest distance to be strong
Returns
-------
C : csr_matrix
C(i,j) = distance(point_i, point_j)
Strength of connection matrix where strength values are
distances, i.e. the smaller the value, the stronger the connection.
Sparsity pattern of C is copied from A.
Notes
-----
- theta is a drop tolerance that is applied row-wise
- If a BSR matrix given, then the return matrix is still CSR. The strength
is given between super nodes based on the BSR block size.
Examples
--------
>>> from pyamg.gallery import load_example
>>> from pyamg.strength import distance_strength_of_connection
>>> data = load_example('airfoil')
>>> A = data['A'].tocsr()
>>> S = distance_strength_of_connection(data['A'], data['vertices'])
"""
# Amalgamate for the supernode case
if sparse.isspmatrix_bsr(A):
sn = A.shape[0]/A.blocksize[0]
u = np.ones((A.data.shape[0],))
A = sparse.csr_matrix((u, A.indices, A.indptr), shape=(sn, sn))
if not sparse.isspmatrix_csr(A):
warn("Implicit conversion of A to csr", sparse.SparseEfficiencyWarning)
A = sparse.csr_matrix(A)
dim = V.shape[1]
# Create two arrays for differencing the different coordinates such
# that C(i,j) = distance(point_i, point_j)
cols = A.indices
rows = np.repeat(np.arange(A.shape[0]), A.indptr[1:] - A.indptr[0:-1])
# Insert difference for each coordinate into C
C = (V[rows, 0] - V[cols, 0])**2
for d in range(1, dim):
C += (V[rows, d] - V[cols, d])**2
C = np.sqrt(C)
C[C < 1e-6] = 1e-6
C = sparse.csr_matrix((C, A.indices.copy(), A.indptr.copy()),
shape=A.shape)
# Apply drop tolerance
if relative_drop is True:
if theta != np.inf:
amg_core.apply_distance_filter(C.shape[0], theta, C.indptr,
C.indices, C.data)
else:
amg_core.apply_absolute_distance_filter(C.shape[0], theta, C.indptr,
C.indices, C.data)
C.eliminate_zeros()
C = C + sparse.eye(C.shape[0], C.shape[1], format='csr')
# Standardized strength values require small values be weak and large
# values be strong. So, we invert the distances.
C.data = 1.0/C.data
# Scale C by the largest magnitude entry in each row
C = scale_rows_by_largest_entry(C)
return C
def symmetric_strength_of_connection(A, theta=0):
"""
Compute strength of connection matrix using the standard symmetric measure
An off-diagonal connection A[i,j] is strong iff::
abs(A[i,j]) >= theta * sqrt( abs(A[i,i]) * abs(A[j,j]) )
Parameters
----------
A : csr_matrix
Matrix graph defined in sparse format. Entry A[i,j] describes the
strength of edge [i,j]
theta : float
Threshold parameter (positive).
Returns
-------
S : csr_matrix
Matrix graph defining strong connections. S[i,j]=1 if vertex i
is strongly influenced by vertex j.
See Also
--------
symmetric_strength_of_connection : symmetric measure used in SA
evolution_strength_of_connection : relaxation based strength measure
Notes
-----
- For vector problems, standard strength measures may produce
undesirable aggregates. A "block approach" from Vanek et al. is used
to replace vertex comparisons with block-type comparisons. A
connection between nodes i and j in the block case is strong if::
||AB[i,j]|| >= theta * sqrt( ||AB[i,i]||*||AB[j,j]|| ) where AB[k,l]
is the matrix block (degrees of freedom) associated with nodes k and
l and ||.|| is a matrix norm, such a Frobenius.
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
Examples
--------
>>> import numpy as np
>>> from pyamg.gallery import stencil_grid
>>> from pyamg.strength import symmetric_strength_of_connection
>>> n=3
>>> stencil = np.array([[-1.0,-1.0,-1.0],
... [-1.0, 8.0,-1.0],
... [-1.0,-1.0,-1.0]])
>>> A = stencil_grid(stencil, (n,n), format='csr')
>>> S = symmetric_strength_of_connection(A, 0.0)
"""
if theta < 0:
raise ValueError('expected a positive theta')
if sparse.isspmatrix_csr(A):
# if theta == 0:
# return A
Sp = np.empty_like(A.indptr)
Sj = np.empty_like(A.indices)
Sx = np.empty_like(A.data)
fn = amg_core.symmetric_strength_of_connection
fn(A.shape[0], theta, A.indptr, A.indices, A.data, Sp, Sj, Sx)
S = sparse.csr_matrix((Sx, Sj, Sp), shape=A.shape)
elif sparse.isspmatrix_bsr(A):
M, N = A.shape
R, C = A.blocksize
if R != C:
raise ValueError('matrix must have square blocks')
if theta == 0:
data = np.ones(len(A.indices), dtype=A.dtype)
S = sparse.csr_matrix((data, A.indices.copy(), A.indptr.copy()),
shape=(M / R, N / C))
else:
# the strength of connection matrix is based on the
# Frobenius norms of the blocks
data = (np.conjugate(A.data) * A.data).reshape(-1, R*C).sum(axis=1)
A = sparse.csr_matrix((data, A.indices, A.indptr),
shape=(M / R, N / C))
return symmetric_strength_of_connection(A, theta)
else:
raise TypeError('expected csr_matrix or bsr_matrix')
# Strength represents "distance", so take the magnitude
S.data = np.abs(S.data)
# Scale S by the largest magnitude entry in each row
S = scale_rows_by_largest_entry(S)
return S
