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"""
Sparse matrix functions
"""
#
# Authors: Travis Oliphant, March 2002
# Anthony Scopatz, August 2012 (Sparse Updates)
# Jake Vanderplas, August 2012 (Sparse Updates)
#
__all__ = ['expm', 'inv', 'matrix_power']
import numpy as np
from scipy.linalg._basic import solve, solve_triangular
from scipy.sparse._base import issparse
from scipy.sparse.linalg import spsolve
from scipy.sparse._sputils import is_pydata_spmatrix, isintlike
import scipy.sparse
import scipy.sparse.linalg
from scipy.sparse.linalg._interface import LinearOperator
from scipy.sparse._construct import eye_array
from ._expm_multiply import _ident_like, _exact_1_norm as _onenorm
UPPER_TRIANGULAR = 'upper_triangular'
def inv(A):
"""
Compute the inverse of a sparse arrays
Parameters
----------
A : (M, M) sparse arrays
square matrix to be inverted
Returns
-------
Ainv : (M, M) sparse arrays
inverse of `A`
Notes
-----
This computes the sparse inverse of `A`. If the inverse of `A` is expected
to be non-sparse, it will likely be faster to convert `A` to dense and use
`scipy.linalg.inv`.
Examples
--------
>>> from scipy.sparse import csc_array
>>> from scipy.sparse.linalg import inv
>>> A = csc_array([[1., 0.], [1., 2.]])
>>> Ainv = inv(A)
>>> Ainv
<Compressed Sparse Column sparse array of dtype 'float64'
with 3 stored elements and shape (2, 2)>
>>> A.dot(Ainv)
<Compressed Sparse Column sparse array of dtype 'float64'
with 2 stored elements and shape (2, 2)>
>>> A.dot(Ainv).toarray()
array([[ 1., 0.],
[ 0., 1.]])
.. versionadded:: 0.12.0
"""
# Check input
if not (issparse(A) or is_pydata_spmatrix(A)):
raise TypeError('Input must be a sparse arrays')
# Use sparse direct solver to solve "AX = I" accurately
I = _ident_like(A)
Ainv = spsolve(A, I)
return Ainv
def _onenorm_matrix_power_nnm(A, p):
"""
Compute the 1-norm of a non-negative integer power of a non-negative matrix.
Parameters
----------
A : a square ndarray or matrix or sparse arrays
Input matrix with non-negative entries.
p : non-negative integer
The power to which the matrix is to be raised.
Returns
-------
out : float
The 1-norm of the matrix power p of A.
"""
# Check input
if int(p) != p or p < 0:
raise ValueError('expected non-negative integer p')
p = int(p)
if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
raise ValueError('expected A to be like a square matrix')
# Explicitly make a column vector so that this works when A is a
# numpy matrix (in addition to ndarray and sparse arrays).
v = np.ones((A.shape[0], 1), dtype=float)
M = A.T
for i in range(p):
v = M.dot(v)
return np.max(v)
def _is_upper_triangular(A):
# This function could possibly be of wider interest.
if issparse(A):
lower_part = scipy.sparse.tril(A, -1)
# Check structural upper triangularity,
# then coincidental upper triangularity if needed.
return lower_part.nnz == 0 or lower_part.count_nonzero() == 0
elif is_pydata_spmatrix(A):
import sparse
lower_part = sparse.tril(A, -1)
return lower_part.nnz == 0
else:
return not np.tril(A, -1).any()
def _smart_matrix_product(A, B, alpha=None, structure=None):
"""
A matrix product that knows about sparse and structured matrices.
Parameters
----------
A : 2d ndarray
First matrix.
B : 2d ndarray
Second matrix.
alpha : float
The matrix product will be scaled by this constant.
structure : str, optional
A string describing the structure of both matrices `A` and `B`.
Only `upper_triangular` is currently supported.
Returns
-------
M : 2d ndarray
Matrix product of A and B.
"""
if len(A.shape) != 2:
raise ValueError('expected A to be a rectangular matrix')
if len(B.shape) != 2:
raise ValueError('expected B to be a rectangular matrix')
f = None
if structure == UPPER_TRIANGULAR:
if (not issparse(A) and not issparse(B)
and not is_pydata_spmatrix(A) and not is_pydata_spmatrix(B)):
f, = scipy.linalg.get_blas_funcs(('trmm',), (A, B))
if f is not None:
if alpha is None:
alpha = 1.
out = f(alpha, A, B)
else:
if alpha is None:
out = A.dot(B)
else:
out = alpha * A.dot(B)
return out
class MatrixPowerOperator(LinearOperator):
def __init__(self, A, p, structure=None):
if A.ndim != 2 or A.shape[0] != A.shape[1]:
raise ValueError('expected A to be like a square matrix')
if p < 0:
raise ValueError('expected p to be a non-negative integer')
self._A = A
self._p = p
self._structure = structure
self.dtype = A.dtype
self.ndim = A.ndim
self.shape = A.shape
def _matvec(self, x):
for i in range(self._p):
x = self._A.dot(x)
return x
def _rmatvec(self, x):
A_T = self._A.T
x = x.ravel()
for i in range(self._p):
x = A_T.dot(x)
return x
def _matmat(self, X):
for i in range(self._p):
X = _smart_matrix_product(self._A, X, structure=self._structure)
return X
@property
def T(self):
return MatrixPowerOperator(self._A.T, self._p)
class ProductOperator(LinearOperator):
"""
For now, this is limited to products of multiple square matrices.
"""
def __init__(self, *args, **kwargs):
self._structure = kwargs.get('structure', None)
for A in args:
if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
raise ValueError(
'For now, the ProductOperator implementation is '
'limited to the product of multiple square matrices.')
if args:
n = args[0].shape[0]
for A in args:
for d in A.shape:
if d != n:
raise ValueError(
'The square matrices of the ProductOperator '
'must all have the same shape.')
self.shape = (n, n)
self.ndim = len(self.shape)
self.dtype = np.result_type(*[x.dtype for x in args])
self._operator_sequence = args
def _matvec(self, x):
for A in reversed(self._operator_sequence):
x = A.dot(x)
return x
def _rmatvec(self, x):
x = x.ravel()
for A in self._operator_sequence:
x = A.T.dot(x)
return x
def _matmat(self, X):
for A in reversed(self._operator_sequence):
X = _smart_matrix_product(A, X, structure=self._structure)
return X
@property
def T(self):
T_args = [A.T for A in reversed(self._operator_sequence)]
return ProductOperator(*T_args)
def _onenormest_matrix_power(A, p,
t=2, itmax=5, compute_v=False, compute_w=False, structure=None):
"""
Efficiently estimate the 1-norm of A^p.
Parameters
----------
A : ndarray
Matrix whose 1-norm of a power is to be computed.
p : int
Non-negative integer power.
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 arrays.
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.
"""
return scipy.sparse.linalg.onenormest(
MatrixPowerOperator(A, p, structure=structure))
def _onenormest_product(operator_seq,
t=2, itmax=5, compute_v=False, compute_w=False, structure=None):
"""
Efficiently estimate the 1-norm of the matrix product of the args.
Parameters
----------
operator_seq : linear operator sequence
Matrices whose 1-norm of product is to be computed.
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.
structure : str, optional
A string describing the structure of all operators.
Only `upper_triangular` is currently supported.
Returns
-------
est : float
An underestimate of the 1-norm of the sparse arrays.
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.
"""
return scipy.sparse.linalg.onenormest(
ProductOperator(*operator_seq, structure=structure))
class _ExpmPadeHelper:
"""
Help lazily evaluate a matrix exponential.
The idea is to not do more work than we need for high expm precision,
so we lazily compute matrix powers and store or precompute
other properties of the matrix.
"""
def __init__(self, A, structure=None, use_exact_onenorm=False):
"""
Initialize the object.
Parameters
----------
A : a dense or sparse square numpy matrix or ndarray
The matrix to be exponentiated.
structure : str, optional
A string describing the structure of matrix `A`.
Only `upper_triangular` is currently supported.
use_exact_onenorm : bool, optional
If True then only the exact one-norm of matrix powers and products
will be used. Otherwise, the one-norm of powers and products
may initially be estimated.
"""
self.A = A
self._A2 = None
self._A4 = None
self._A6 = None
self._A8 = None
self._A10 = None
self._d4_exact = None
self._d6_exact = None
self._d8_exact = None
self._d10_exact = None
self._d4_approx = None
self._d6_approx = None
self._d8_approx = None
self._d10_approx = None
self.ident = _ident_like(A)
self.structure = structure
self.use_exact_onenorm = use_exact_onenorm
@property
def A2(self):
if self._A2 is None:
self._A2 = _smart_matrix_product(
self.A, self.A, structure=self.structure)
return self._A2
@property
def A4(self):
if self._A4 is None:
self._A4 = _smart_matrix_product(
self.A2, self.A2, structure=self.structure)
return self._A4
@property
def A6(self):
if self._A6 is None:
self._A6 = _smart_matrix_product(
self.A4, self.A2, structure=self.structure)
return self._A6
@property
def A8(self):
if self._A8 is None:
self._A8 = _smart_matrix_product(
self.A6, self.A2, structure=self.structure)
return self._A8
@property
def A10(self):
if self._A10 is None:
self._A10 = _smart_matrix_product(
self.A4, self.A6, structure=self.structure)
return self._A10
@property
def d4_tight(self):
if self._d4_exact is None:
self._d4_exact = _onenorm(self.A4)**(1/4.)
return self._d4_exact
@property
def d6_tight(self):
if self._d6_exact is None:
self._d6_exact = _onenorm(self.A6)**(1/6.)
return self._d6_exact
@property
def d8_tight(self):
if self._d8_exact is None:
self._d8_exact = _onenorm(self.A8)**(1/8.)
return self._d8_exact
@property
def d10_tight(self):
if self._d10_exact is None:
self._d10_exact = _onenorm(self.A10)**(1/10.)
return self._d10_exact
@property
def d4_loose(self):
if self.use_exact_onenorm:
return self.d4_tight
if self._d4_exact is not None:
return self._d4_exact
else:
if self._d4_approx is None:
self._d4_approx = _onenormest_matrix_power(self.A2, 2,
structure=self.structure)**(1/4.)
return self._d4_approx
@property
def d6_loose(self):
if self.use_exact_onenorm:
return self.d6_tight
if self._d6_exact is not None:
return self._d6_exact
else:
if self._d6_approx is None:
self._d6_approx = _onenormest_matrix_power(self.A2, 3,
structure=self.structure)**(1/6.)
return self._d6_approx
@property
def d8_loose(self):
if self.use_exact_onenorm:
return self.d8_tight
if self._d8_exact is not None:
return self._d8_exact
else:
if self._d8_approx is None:
self._d8_approx = _onenormest_matrix_power(self.A4, 2,
structure=self.structure)**(1/8.)
return self._d8_approx
@property
def d10_loose(self):
if self.use_exact_onenorm:
return self.d10_tight
if self._d10_exact is not None:
return self._d10_exact
else:
if self._d10_approx is None:
self._d10_approx = _onenormest_product((self.A4, self.A6),
structure=self.structure)**(1/10.)
return self._d10_approx
def pade3(self):
b = (120., 60., 12., 1.)
U = _smart_matrix_product(self.A,
b[3]*self.A2 + b[1]*self.ident,
structure=self.structure)
V = b[2]*self.A2 + b[0]*self.ident
return U, V
def pade5(self):
b = (30240., 15120., 3360., 420., 30., 1.)
U = _smart_matrix_product(self.A,
b[5]*self.A4 + b[3]*self.A2 + b[1]*self.ident,
structure=self.structure)
V = b[4]*self.A4 + b[2]*self.A2 + b[0]*self.ident
return U, V
def pade7(self):
b = (17297280., 8648640., 1995840., 277200., 25200., 1512., 56., 1.)
U = _smart_matrix_product(self.A,
b[7]*self.A6 + b[5]*self.A4 + b[3]*self.A2 + b[1]*self.ident,
structure=self.structure)
V = b[6]*self.A6 + b[4]*self.A4 + b[2]*self.A2 + b[0]*self.ident
return U, V
def pade9(self):
b = (17643225600., 8821612800., 2075673600., 302702400., 30270240.,
2162160., 110880., 3960., 90., 1.)
U = _smart_matrix_product(self.A,
(b[9]*self.A8 + b[7]*self.A6 + b[5]*self.A4 +
b[3]*self.A2 + b[1]*self.ident),
structure=self.structure)
V = (b[8]*self.A8 + b[6]*self.A6 + b[4]*self.A4 +
b[2]*self.A2 + b[0]*self.ident)
return U, V
def pade13_scaled(self, s):
b = (64764752532480000., 32382376266240000., 7771770303897600.,
1187353796428800., 129060195264000., 10559470521600.,
670442572800., 33522128640., 1323241920., 40840800., 960960.,
16380., 182., 1.)
B = self.A * 2**-s
B2 = self.A2 * 2**(-2*s)
B4 = self.A4 * 2**(-4*s)
B6 = self.A6 * 2**(-6*s)
U2 = _smart_matrix_product(B6,
b[13]*B6 + b[11]*B4 + b[9]*B2,
structure=self.structure)
U = _smart_matrix_product(B,
(U2 + b[7]*B6 + b[5]*B4 +
b[3]*B2 + b[1]*self.ident),
structure=self.structure)
V2 = _smart_matrix_product(B6,
b[12]*B6 + b[10]*B4 + b[8]*B2,
structure=self.structure)
V = V2 + b[6]*B6 + b[4]*B4 + b[2]*B2 + b[0]*self.ident
return U, V
def expm(A):
"""
Compute the matrix exponential using Pade approximation.
Parameters
----------
A : (M,M) array_like or sparse array
2D Array or Matrix (sparse or dense) to be exponentiated
Returns
-------
expA : (M,M) ndarray
Matrix exponential of `A`
Notes
-----
This is algorithm (6.1) which is a simplification of algorithm (5.1).
.. versionadded:: 0.12.0
References
----------
.. [1] Awad H. Al-Mohy and Nicholas J. Higham (2009)
"A New Scaling and Squaring Algorithm for the Matrix Exponential."
SIAM Journal on Matrix Analysis and Applications.
31 (3). pp. 970-989. ISSN 1095-7162
Examples
--------
>>> from scipy.sparse import csc_array
>>> from scipy.sparse.linalg import expm
>>> A = csc_array([[1, 0, 0], [0, 2, 0], [0, 0, 3]])
>>> A.toarray()
array([[1, 0, 0],
[0, 2, 0],
[0, 0, 3]], dtype=int64)
>>> Aexp = expm(A)
>>> Aexp
<Compressed Sparse Column sparse array of dtype 'float64'
with 3 stored elements and shape (3, 3)>
>>> Aexp.toarray()
array([[ 2.71828183, 0. , 0. ],
[ 0. , 7.3890561 , 0. ],
[ 0. , 0. , 20.08553692]])
"""
return _expm(A, use_exact_onenorm='auto')
def _expm(A, use_exact_onenorm):
# Core of expm, separated to allow testing exact and approximate
# algorithms.
# Avoid indiscriminate asarray() to allow sparse or other strange arrays.
if isinstance(A, (list, tuple, np.matrix)):
A = np.asarray(A)
if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
raise ValueError('expected a square matrix')
# gracefully handle size-0 input,
# carefully handling sparse scenario
if A.shape == (0, 0):
out = np.zeros([0, 0], dtype=A.dtype)
if issparse(A) or is_pydata_spmatrix(A):
return A.__class__(out)
return out
# Trivial case
if A.shape == (1, 1):
out = [[np.exp(A[0, 0])]]
# Avoid indiscriminate casting to ndarray to
# allow for sparse or other strange arrays
if issparse(A) or is_pydata_spmatrix(A):
return A.__class__(out)
return np.array(out)
# Ensure input is of float type, to avoid integer overflows etc.
if ((isinstance(A, np.ndarray) or issparse(A) or is_pydata_spmatrix(A))
and not np.issubdtype(A.dtype, np.inexact)):
A = A.astype(float)
# Detect upper triangularity.
structure = UPPER_TRIANGULAR if _is_upper_triangular(A) else None
if use_exact_onenorm == "auto":
# Hardcode a matrix order threshold for exact vs. estimated one-norms.
use_exact_onenorm = A.shape[0] < 200
# Track functions of A to help compute the matrix exponential.
h = _ExpmPadeHelper(
A, structure=structure, use_exact_onenorm=use_exact_onenorm)
# Try Pade order 3.
eta_1 = max(h.d4_loose, h.d6_loose)
if eta_1 < 1.495585217958292e-002 and _ell(h.A, 3) == 0:
U, V = h.pade3()
return _solve_P_Q(U, V, structure=structure)
# Try Pade order 5.
eta_2 = max(h.d4_tight, h.d6_loose)
if eta_2 < 2.539398330063230e-001 and _ell(h.A, 5) == 0:
U, V = h.pade5()
return _solve_P_Q(U, V, structure=structure)
# Try Pade orders 7 and 9.
eta_3 = max(h.d6_tight, h.d8_loose)
if eta_3 < 9.504178996162932e-001 and _ell(h.A, 7) == 0:
U, V = h.pade7()
return _solve_P_Q(U, V, structure=structure)
if eta_3 < 2.097847961257068e+000 and _ell(h.A, 9) == 0:
U, V = h.pade9()
return _solve_P_Q(U, V, structure=structure)
# Use Pade order 13.
eta_4 = max(h.d8_loose, h.d10_loose)
eta_5 = min(eta_3, eta_4)
theta_13 = 4.25
# Choose smallest s>=0 such that 2**(-s) eta_5 <= theta_13
if eta_5 == 0:
# Nilpotent special case
s = 0
else:
s = max(int(np.ceil(np.log2(eta_5 / theta_13))), 0)
s = s + _ell(2**-s * h.A, 13)
U, V = h.pade13_scaled(s)
X = _solve_P_Q(U, V, structure=structure)
if structure == UPPER_TRIANGULAR:
# Invoke Code Fragment 2.1.
X = _fragment_2_1(X, h.A, s)
else:
# X = r_13(A)^(2^s) by repeated squaring.
for i in range(s):
X = X.dot(X)
return X
def _solve_P_Q(U, V, structure=None):
"""
A helper function for expm_2009.
Parameters
----------
U : ndarray
Pade numerator.
V : ndarray
Pade denominator.
structure : str, optional
A string describing the structure of both matrices `U` and `V`.
Only `upper_triangular` is currently supported.
Notes
-----
The `structure` argument is inspired by similar args
for theano and cvxopt functions.
"""
P = U + V
Q = -U + V
if issparse(U) or is_pydata_spmatrix(U):
return spsolve(Q, P)
elif structure is None:
return solve(Q, P)
elif structure == UPPER_TRIANGULAR:
return solve_triangular(Q, P)
else:
raise ValueError('unsupported matrix structure: ' + str(structure))
def _exp_sinch(a, x):
"""
Stably evaluate exp(a)*sinh(x)/x
Notes
-----
The strategy of falling back to a sixth order Taylor expansion
was suggested by the Spallation Neutron Source docs
which was found on the internet by google search.
http://www.ornl.gov/~t6p/resources/xal/javadoc/gov/sns/tools/math/ElementaryFunction.html
The details of the cutoff point and the Horner-like evaluation
was picked without reference to anything in particular.
Note that sinch is not currently implemented in scipy.special,
whereas the "engineer's" definition of sinc is implemented.
The implementation of sinc involves a scaling factor of pi
that distinguishes it from the "mathematician's" version of sinc.
"""
# If x is small then use sixth order Taylor expansion.
# How small is small? I am using the point where the relative error
# of the approximation is less than 1e-14.
# If x is large then directly evaluate sinh(x) / x.
if abs(x) < 0.0135:
x2 = x*x
return np.exp(a) * (1 + (x2/6.)*(1 + (x2/20.)*(1 + (x2/42.))))
else:
return (np.exp(a + x) - np.exp(a - x)) / (2*x)
def _eq_10_42(lam_1, lam_2, t_12):
"""
Equation (10.42) of Functions of Matrices: Theory and Computation.
Notes
-----
This is a helper function for _fragment_2_1 of expm_2009.
Equation (10.42) is on page 251 in the section on Schur algorithms.
In particular, section 10.4.3 explains the Schur-Parlett algorithm.
expm([[lam_1, t_12], [0, lam_1])
=
[[exp(lam_1), t_12*exp((lam_1 + lam_2)/2)*sinch((lam_1 - lam_2)/2)],
[0, exp(lam_2)]
"""
# The plain formula t_12 * (exp(lam_2) - exp(lam_2)) / (lam_2 - lam_1)
# apparently suffers from cancellation, according to Higham's textbook.
# A nice implementation of sinch, defined as sinh(x)/x,
# will apparently work around the cancellation.
a = 0.5 * (lam_1 + lam_2)
b = 0.5 * (lam_1 - lam_2)
return t_12 * _exp_sinch(a, b)
def _fragment_2_1(X, T, s):
"""
A helper function for expm_2009.
Notes
-----
The argument X is modified in-place, but this modification is not the same
as the returned value of the function.
This function also takes pains to do things in ways that are compatible
with sparse arrays, for example by avoiding fancy indexing
and by using methods of the matrices whenever possible instead of
using functions of the numpy or scipy libraries themselves.
"""
# Form X = r_m(2^-s T)
# Replace diag(X) by exp(2^-s diag(T)).
n = X.shape[0]
diag_T = np.ravel(T.diagonal().copy())
# Replace diag(X) by exp(2^-s diag(T)).
scale = 2 ** -s
exp_diag = np.exp(scale * diag_T)
for k in range(n):
X[k, k] = exp_diag[k]
for i in range(s-1, -1, -1):
X = X.dot(X)
# Replace diag(X) by exp(2^-i diag(T)).
scale = 2 ** -i
exp_diag = np.exp(scale * diag_T)
for k in range(n):
X[k, k] = exp_diag[k]
# Replace (first) superdiagonal of X by explicit formula
# for superdiagonal of exp(2^-i T) from Eq (10.42) of
# the author's 2008 textbook
# Functions of Matrices: Theory and Computation.
for k in range(n-1):
lam_1 = scale * diag_T[k]
lam_2 = scale * diag_T[k+1]
t_12 = scale * T[k, k+1]
value = _eq_10_42(lam_1, lam_2, t_12)
X[k, k+1] = value
# Return the updated X matrix.
return X
def _ell(A, m):
"""
A helper function for expm_2009.
Parameters
----------
A : linear operator
A linear operator whose norm of power we care about.
m : int
The power of the linear operator
Returns
-------
value : int
A value related to a bound.
"""
if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
raise ValueError('expected A to be like a square matrix')
# The c_i are explained in (2.2) and (2.6) of the 2005 expm paper.
# They are coefficients of terms of a generating function series expansion.
c_i = {3: 100800.,
5: 10059033600.,
7: 4487938430976000.,
9: 5914384781877411840000.,
13: 113250775606021113483283660800000000.
}
abs_c_recip = c_i[m]
# This is explained after Eq. (1.2) of the 2009 expm paper.
# It is the "unit roundoff" of IEEE double precision arithmetic.
u = 2**-53
# Compute the one-norm of matrix power p of abs(A).
A_abs_onenorm = _onenorm_matrix_power_nnm(abs(A), 2*m + 1)
# Treat zero norm as a special case.
if not A_abs_onenorm:
return 0
alpha = A_abs_onenorm / (_onenorm(A) * abs_c_recip)
log2_alpha_div_u = np.log2(alpha/u)
value = int(np.ceil(log2_alpha_div_u / (2 * m)))
return max(value, 0)
def matrix_power(A, power):
"""
Raise a square matrix to the integer power, `power`.
For non-negative integers, ``A**power`` is computed using repeated
matrix multiplications. Negative integers are not supported.
Parameters
----------
A : (M, M) square sparse array or matrix
sparse array that will be raised to power `power`
power : int
Exponent used to raise sparse array `A`
Returns
-------
A**power : (M, M) sparse array or matrix
The output matrix will be the same shape as A, and will preserve
the class of A, but the format of the output may be changed.
Notes
-----
This uses a recursive implementation of the matrix power. For computing
the matrix power using a reasonably large `power`, this may be less efficient
than computing the product directly, using A @ A @ ... @ A.
This is contingent upon the number of nonzero entries in the matrix.
.. versionadded:: 1.12.0
Examples
--------
>>> from scipy import sparse
>>> A = sparse.csc_array([[0,1,0],[1,0,1],[0,1,0]])
>>> A.todense()
array([[0, 1, 0],
[1, 0, 1],
[0, 1, 0]])
>>> (A @ A).todense()
array([[1, 0, 1],
[0, 2, 0],
[1, 0, 1]])
>>> A2 = sparse.linalg.matrix_power(A, 2)
>>> A2.todense()
array([[1, 0, 1],
[0, 2, 0],
[1, 0, 1]])
>>> A4 = sparse.linalg.matrix_power(A, 4)
>>> A4.todense()
array([[2, 0, 2],
[0, 4, 0],
[2, 0, 2]])
"""
M, N = A.shape
if M != N:
raise TypeError('sparse matrix is not square')
if isintlike(power):
power = int(power)
if power < 0:
raise ValueError('exponent must be >= 0')
if power == 0:
return eye_array(M, dtype=A.dtype)
if power == 1:
return A.copy()
tmp = matrix_power(A, power // 2)
if power % 2:
return A @ tmp @ tmp
else:
return tmp @ tmp
else:
raise ValueError("exponent must be an integer")
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