"""Sparse block 1-norm estimator.
"""
import numpy as np
from scipy.sparse.linalg import aslinearoperator
__all__ = ['onenormest']
[文档]
def onenormest(A, t=2, itmax=5, compute_v=False, compute_w=False):
"""
Compute a lower bound of the 1-norm of a sparse matrix.
Parameters
----------
A : ndarray or other linear operator
A linear operator that can be transposed and that can
produce matrix products.
t : int, optional
A positive parameter controlling the tradeoff between
accuracy versus time and memory usage.
Larger values take longer and use more memory
but give more accurate output.
itmax : int, optional
Use at most this many iterations.
compute_v : bool, optional
Request a norm-maximizing linear operator input vector if True.
compute_w : bool, optional
Request a norm-maximizing linear operator output vector if True.
Returns
-------
est : float
An underestimate of the 1-norm of the sparse matrix.
v : ndarray, optional
The vector such that ||Av||_1 == est*||v||_1.
It can be thought of as an input to the linear operator
that gives an output with particularly large norm.
w : ndarray, optional
The vector Av which has relatively large 1-norm.
It can be thought of as an output of the linear operator
that is relatively large in norm compared to the input.
Notes
-----
This is algorithm 2.4 of [1].
In [2] it is described as follows.
"This algorithm typically requires the evaluation of
about 4t matrix-vector products and almost invariably
produces a norm estimate (which is, in fact, a lower
bound on the norm) correct to within a factor 3."
.. versionadded:: 0.13.0
References
----------
.. [1] Nicholas J. Higham and Francoise Tisseur (2000),
"A Block Algorithm for Matrix 1-Norm Estimation,
with an Application to 1-Norm Pseudospectra."
SIAM J. Matrix Anal. Appl. Vol. 21, No. 4, pp. 1185-1201.
.. [2] Awad H. Al-Mohy and Nicholas J. Higham (2009),
"A new scaling and squaring algorithm for the matrix exponential."
SIAM J. Matrix Anal. Appl. Vol. 31, No. 3, pp. 970-989.
Examples
--------
>>> import numpy as np
>>> from scipy.sparse import csc_matrix
>>> from scipy.sparse.linalg import onenormest
>>> A = csc_matrix([[1., 0., 0.], [5., 8., 2.], [0., -1., 0.]], dtype=float)
>>> A.toarray()
array([[ 1., 0., 0.],
[ 5., 8., 2.],
[ 0., -1., 0.]])
>>> onenormest(A)
9.0
>>> np.linalg.norm(A.toarray(), ord=1)
9.0
"""
# Check the input.
A = aslinearoperator(A)
if A.shape[0] != A.shape[1]:
raise ValueError('expected the operator to act like a square matrix')
# If the operator size is small compared to t,
# then it is easier to compute the exact norm.
# Otherwise estimate the norm.
n = A.shape[1]
if t >= n:
A_explicit = np.asarray(aslinearoperator(A).matmat(np.identity(n)))
if A_explicit.shape != (n, n):
raise Exception('internal error: ',
'unexpected shape ' + str(A_explicit.shape))
col_abs_sums = abs(A_explicit).sum(axis=0)
if col_abs_sums.shape != (n, ):
raise Exception('internal error: ',
'unexpected shape ' + str(col_abs_sums.shape))
argmax_j = np.argmax(col_abs_sums)
v = elementary_vector(n, argmax_j)
w = A_explicit[:, argmax_j]
est = col_abs_sums[argmax_j]
else:
est, v, w, nmults, nresamples = _onenormest_core(A, A.H, t, itmax)
# Report the norm estimate along with some certificates of the estimate.
if compute_v or compute_w:
result = (est,)
if compute_v:
result += (v,)
if compute_w:
result += (w,)
return result
else:
return est
def _blocked_elementwise(func):
"""
Decorator for an elementwise function, to apply it blockwise along
first dimension, to avoid excessive memory usage in temporaries.
"""
block_size = 2**20
def wrapper(x):
if x.shape[0] < block_size:
return func(x)
else:
y0 = func(x[:block_size])
y = np.zeros((x.shape[0],) + y0.shape[1:], dtype=y0.dtype)
y[:block_size] = y0
del y0
for j in range(block_size, x.shape[0], block_size):
y[j:j+block_size] = func(x[j:j+block_size])
return y
return wrapper
@_blocked_elementwise
def sign_round_up(X):
"""
This should do the right thing for both real and complex matrices.
From Higham and Tisseur:
"Everything in this section remains valid for complex matrices
provided that sign(A) is redefined as the matrix (aij / |aij|)
(and sign(0) = 1) transposes are replaced by conjugate transposes."
"""
Y = X.copy()
Y[Y == 0] = 1
Y /= np.abs(Y)
return Y
@_blocked_elementwise
def _max_abs_axis1(X):
return np.max(np.abs(X), axis=1)
def _sum_abs_axis0(X):
block_size = 2**20
r = None
for j in range(0, X.shape[0], block_size):
y = np.sum(np.abs(X[j:j+block_size]), axis=0)
if r is None:
r = y
else:
r += y
return r
def elementary_vector(n, i):
v = np.zeros(n, dtype=float)
v[i] = 1
return v
def vectors_are_parallel(v, w):
# Columns are considered parallel when they are equal or negative.
# Entries are required to be in {-1, 1},
# which guarantees that the magnitudes of the vectors are identical.
if v.ndim != 1 or v.shape != w.shape:
raise ValueError('expected conformant vectors with entries in {-1,1}')
n = v.shape[0]
return np.dot(v, w) == n
def every_col_of_X_is_parallel_to_a_col_of_Y(X, Y):
for v in X.T:
if not any(vectors_are_parallel(v, w) for w in Y.T):
return False
return True
def column_needs_resampling(i, X, Y=None):
# column i of X needs resampling if either
# it is parallel to a previous column of X or
# it is parallel to a column of Y
n, t = X.shape
v = X[:, i]
if any(vectors_are_parallel(v, X[:, j]) for j in range(i)):
return True
if Y is not None:
if any(vectors_are_parallel(v, w) for w in Y.T):
return True
return False
def resample_column(i, X):
X[:, i] = np.random.randint(0, 2, size=X.shape[0])*2 - 1
def less_than_or_close(a, b):
return np.allclose(a, b) or (a < b)
def _algorithm_2_2(A, AT, t):
"""
This is Algorithm 2.2.
Parameters
----------
A : ndarray or other linear operator
A linear operator that can produce matrix products.
AT : ndarray or other linear operator
The transpose of A.
t : int, optional
A positive parameter controlling the tradeoff between
accuracy versus time and memory usage.
Returns
-------
g : sequence
A non-negative decreasing vector
such that g[j] is a lower bound for the 1-norm
of the column of A of jth largest 1-norm.
The first entry of this vector is therefore a lower bound
on the 1-norm of the linear operator A.
This sequence has length t.
ind : sequence
The ith entry of ind is the index of the column A whose 1-norm
is given by g[i].
This sequence of indices has length t, and its entries are
chosen from range(n), possibly with repetition,
where n is the order of the operator A.
Notes
-----
This algorithm is mainly for testing.
It uses the 'ind' array in a way that is similar to
its usage in algorithm 2.4. This algorithm 2.2 may be easier to test,
so it gives a chance of uncovering bugs related to indexing
which could have propagated less noticeably to algorithm 2.4.
"""
A_linear_operator = aslinearoperator(A)
AT_linear_operator = aslinearoperator(AT)
n = A_linear_operator.shape[0]
# Initialize the X block with columns of unit 1-norm.
X = np.ones((n, t))
if t > 1:
X[:, 1:] = np.random.randint(0, 2, size=(n, t-1))*2 - 1
X /= float(n)
# Iteratively improve the lower bounds.
# Track extra things, to assert invariants for debugging.
g_prev = None
h_prev = None
k = 1
ind = range(t)
while True:
Y = np.asarray(A_linear_operator.matmat(X))
g = _sum_abs_axis0(Y)
best_j = np.argmax(g)
g.sort()
g = g[::-1]
S = sign_round_up(Y)
Z = np.asarray(AT_linear_operator.matmat(S))
h = _max_abs_axis1(Z)
# If this algorithm runs for fewer than two iterations,
# then its return values do not have the properties indicated
# in the description of the algorithm.
# In particular, the entries of g are not 1-norms of any
# column of A until the second iteration.
# Therefore we will require the algorithm to run for at least
# two iterations, even though this requirement is not stated
# in the description of the algorithm.
if k >= 2:
if less_than_or_close(max(h), np.dot(Z[:, best_j], X[:, best_j])):
break
ind = np.argsort(h)[::-1][:t]
h = h[ind]
for j in range(t):
X[:, j] = elementary_vector(n, ind[j])
# Check invariant (2.2).
if k >= 2:
if not less_than_or_close(g_prev[0], h_prev[0]):
raise Exception('invariant (2.2) is violated')
if not less_than_or_close(h_prev[0], g[0]):
raise Exception('invariant (2.2) is violated')
# Check invariant (2.3).
if k >= 3:
for j in range(t):
if not less_than_or_close(g[j], g_prev[j]):
raise Exception('invariant (2.3) is violated')
# Update for the next iteration.
g_prev = g
h_prev = h
k += 1
# Return the lower bounds and the corresponding column indices.
return g, ind
def _onenormest_core(A, AT, t, itmax):
"""
Compute a lower bound of the 1-norm of a sparse matrix.
Parameters
----------
A : ndarray or other linear operator
A linear operator that can produce matrix products.
AT : ndarray or other linear operator
The transpose of A.
t : int, optional
A positive parameter controlling the tradeoff between
accuracy versus time and memory usage.
itmax : int, optional
Use at most this many iterations.
Returns
-------
est : float
An underestimate of the 1-norm of the sparse matrix.
v : ndarray, optional
The vector such that ||Av||_1 == est*||v||_1.
It can be thought of as an input to the linear operator
that gives an output with particularly large norm.
w : ndarray, optional
The vector Av which has relatively large 1-norm.
It can be thought of as an output of the linear operator
that is relatively large in norm compared to the input.
nmults : int, optional
The number of matrix products that were computed.
nresamples : int, optional
The number of times a parallel column was observed,
necessitating a re-randomization of the column.
Notes
-----
This is algorithm 2.4.
"""
# This function is a more or less direct translation
# of Algorithm 2.4 from the Higham and Tisseur (2000) paper.
A_linear_operator = aslinearoperator(A)
AT_linear_operator = aslinearoperator(AT)
if itmax < 2:
raise ValueError('at least two iterations are required')
if t < 1:
raise ValueError('at least one column is required')
n = A.shape[0]
if t >= n:
raise ValueError('t should be smaller than the order of A')
# Track the number of big*small matrix multiplications
# and the number of resamplings.
nmults = 0
nresamples = 0
# "We now explain our choice of starting matrix. We take the first
# column of X to be the vector of 1s [...] This has the advantage that
# for a matrix with nonnegative elements the algorithm converges
# with an exact estimate on the second iteration, and such matrices
# arise in applications [...]"
X = np.ones((n, t), dtype=float)
# "The remaining columns are chosen as rand{-1,1},
# with a check for and correction of parallel columns,
# exactly as for S in the body of the algorithm."
if t > 1:
for i in range(1, t):
# These are technically initial samples, not resamples,
# so the resampling count is not incremented.
resample_column(i, X)
for i in range(t):
while column_needs_resampling(i, X):
resample_column(i, X)
nresamples += 1
# "Choose starting matrix X with columns of unit 1-norm."
X /= float(n)
# "indices of used unit vectors e_j"
ind_hist = np.zeros(0, dtype=np.intp)
est_old = 0
S = np.zeros((n, t), dtype=float)
k = 1
ind = None
while True:
Y = np.asarray(A_linear_operator.matmat(X))
nmults += 1
mags = _sum_abs_axis0(Y)
est = np.max(mags)
best_j = np.argmax(mags)
if est > est_old or k == 2:
if k >= 2:
ind_best = ind[best_j]
w = Y[:, best_j]
# (1)
if k >= 2 and est <= est_old:
est = est_old
break
est_old = est
S_old = S
if k > itmax:
break
S = sign_round_up(Y)
del Y
# (2)
if every_col_of_X_is_parallel_to_a_col_of_Y(S, S_old):
break
if t > 1:
# "Ensure that no column of S is parallel to another column of S
# or to a column of S_old by replacing columns of S by rand{-1,1}."
for i in range(t):
while column_needs_resampling(i, S, S_old):
resample_column(i, S)
nresamples += 1
del S_old
# (3)
Z = np.asarray(AT_linear_operator.matmat(S))
nmults += 1
h = _max_abs_axis1(Z)
del Z
# (4)
if k >= 2 and max(h) == h[ind_best]:
break
# "Sort h so that h_first >= ... >= h_last
# and re-order ind correspondingly."
#
# Later on, we will need at most t+len(ind_hist) largest
# entries, so drop the rest
ind = np.argsort(h)[::-1][:t+len(ind_hist)].copy()
del h
if t > 1:
# (5)
# Break if the most promising t vectors have been visited already.
if np.isin(ind[:t], ind_hist).all():
break
# Put the most promising unvisited vectors at the front of the list
# and put the visited vectors at the end of the list.
# Preserve the order of the indices induced by the ordering of h.
seen = np.isin(ind, ind_hist)
ind = np.concatenate((ind[~seen], ind[seen]))
for j in range(t):
X[:, j] = elementary_vector(n, ind[j])
new_ind = ind[:t][~np.isin(ind[:t], ind_hist)]
ind_hist = np.concatenate((ind_hist, new_ind))
k += 1
v = elementary_vector(n, ind_best)
return est, v, w, nmults, nresamples
