/
matfuncs.py
593 lines (487 loc) · 17.6 KB
/
matfuncs.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
"""
Sparse matrix functions
"""
#
# Authors: Travis Oliphant, March 2002
# Anthony Scopatz, August 2012 (Sparse Updates)
# Jake Vanderplas, August 2012 (Sparse Updates)
#
from __future__ import division, print_function, absolute_import
__all__ = ['expm', 'inv']
import math
from numpy import asarray, dot, eye, ceil, log2
from numpy import matrix as mat
import numpy as np
import scipy.misc
from scipy.linalg.misc import norm
from scipy.linalg.basic import solve, solve_triangular, inv
from scipy.sparse.base import isspmatrix
from scipy.sparse.construct import eye as speye
from scipy.sparse.linalg import spsolve
import scipy.sparse
import scipy.sparse.linalg
from scipy.sparse.linalg.interface import LinearOperator
UPPER_TRIANGULAR = 'upper_triangular'
def inv(A):
"""
Compute the inverse of a sparse matrix
.. versionadded:: 0.12.0
Parameters
----------
A : (M,M) ndarray or sparse matrix
square matrix to be inverted
Returns
-------
Ainv : (M,M) ndarray or sparse matrix
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.
"""
I = speye(A.shape[0], A.shape[1], dtype=A.dtype, format=A.format)
Ainv = spsolve(A, I)
return Ainv
def _exact_1_norm(A):
# A compatibility function which should eventually disappear.
# This is copypasted from expm_action.
if scipy.sparse.isspmatrix(A):
return max(abs(A).sum(axis=0).flat)
else:
return np.linalg.norm(A, 1)
def _ident_like(A):
# A compatibility function which should eventually disappear.
# This is copypasted from expm_action.
if scipy.sparse.isspmatrix(A):
return scipy.sparse.construct.eye(A.shape[0], A.shape[1],
dtype=A.dtype, format=A.format)
else:
return np.eye(A.shape[0], A.shape[1], dtype=A.dtype)
def _count_nonzero(A):
# A compatibility function which should eventually disappear.
#XXX There should be a better way to do this when A is sparse
# in the traditional sense.
if isspmatrix(A):
return np.sum(A.toarray() != 0)
else:
return np.sum(A != 0)
def _is_upper_triangular(A):
# This function could possibly be of wider interest.
if isspmatrix(A):
lower_part = scipy.sparse.tril(A, -1)
if lower_part.nnz == 0:
# structural upper triangularity
return True
else:
# coincidental upper triangularity
return _count_nonzero(lower_part) == 0
else:
return _count_nonzero(np.tril(A, -1)) == 0
class MatrixPowerOperator(LinearOperator):
def __init__(self, A, p):
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.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):
for i in range(self._p):
x = x.dot(self._A)
return x
def matmat(self, X):
for i in range(self._p):
X = self._A.dot(X)
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):
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._operator_sequence = args
def matvec(self, x):
for A in reversed(self._operator_sequence):
x = A.dot(x)
return x
def rmatvec(self, x):
for A in self._operator_sequence:
x = x.dot(A)
return x
def matmat(self, X):
for A in reversed(self._operator_sequence):
X = A.dot(X)
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):
"""
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 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.
"""
#XXX Eventually turn this into an API function in the _onenormest module,
#XXX and remove its underscore,
#XXX but wait until expm_action and expm_2009 go into scipy.
return scipy.sparse.linalg.onenormest(MatrixPowerOperator(A, p))
def _onenormest_product(operator_seq,
t=2, itmax=5, compute_v=False, compute_w=False):
"""
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.
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.
"""
#XXX Eventually turn this into an API function in the _onenormest module,
#XXX and remove its underscore,
#XXX but wait until expm_2009 goes into scipy.
return scipy.sparse.linalg.onenormest(ProductOperator(*operator_seq))
def expm(A):
"""
Compute the matrix exponential using Pade approximation.
.. versionadded:: 0.12.0
Parameters
----------
A : (M,M) array or sparse matrix
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).
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
"""
# Detect upper triangularity.
structure = UPPER_TRIANGULAR if _is_upper_triangular(A) else None
# Define the identity matrix depending on sparsity.
ident = _ident_like(A)
# Try Pade order 3.
A2 = A.dot(A)
d6 = _onenormest_matrix_power(A2, 3)**(1/6.)
eta_1 = max(_onenormest_matrix_power(A2, 2)**(1/4.), d6)
if eta_1 < 1.495585217958292e-002 and _ell(A, 3) == 0:
U, V = _pade3(A, ident, A2)
return _solve_P_Q(U, V, structure=structure)
# Try Pade order 5.
A4 = A2.dot(A2)
d4 = _exact_1_norm(A4)**(1/4.)
eta_2 = max(d4, d6)
if eta_2 < 2.539398330063230e-001 and _ell(A, 5) == 0:
U, V = _pade5(A, ident, A2, A4)
return _solve_P_Q(U, V, structure=structure)
# Try Pade orders 7 and 9.
A6 = A2.dot(A4)
d6 = _exact_1_norm(A6)**(1/6.)
d8 = _onenormest_matrix_power(A4, 2)**(1/8.)
eta_3 = max(d6, d8)
if eta_3 < 9.504178996162932e-001 and _ell(A, 7) == 0:
U, V = _pade7(A, ident, A2, A4, A6)
return _solve_P_Q(U, V, structure=structure)
if eta_3 < 2.097847961257068e+000 and _ell(A, 9) == 0:
U, V = _pade9(A, ident, A2, A4, A6)
return _solve_P_Q(U, V, structure=structure)
# Use Pade order 13.
d10 = _onenormest_product((A4, A6))**(1/10.)
eta_4 = max(d8, d10)
eta_5 = min(eta_3, eta_4)
theta_13 = 4.25
s = max(int(np.ceil(np.log2(eta_5 / theta_13))), 0)
s = s + _ell(2**-s * A, 13)
B = A * 2**-s
B2 = A2 * 2**(-2*s)
B4 = A4 * 2**(-4*s)
B6 = A6 * 2**(-6*s)
U, V = _pade13(B, ident, B2, B4, B6)
X = _solve_P_Q(U, V, structure=structure)
if structure == UPPER_TRIANGULAR:
# Invoke Code Fragment 2.1.
X = _fragment_2_1(X, 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 isspmatrix(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 _sinch(x):
"""
Stably evaluate sinch.
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.
x2 = x*x
if abs(x) < 0.0135:
return 1 + (x2/6.)*(1 + (x2/20.)*(1 + (x2/42.)))
else:
return np.sinh(x) / 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 * np.exp(a) * _sinch(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 matrices, 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 = 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.
"""
p = 2*m + 1
# 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.
abs_c_recip = scipy.misc.comb(2*p, p, exact=True) * math.factorial(2*p + 1)
# 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
# Estimate the 1-norm of the matrix power.
est = _onenormest_matrix_power(abs(A), p)
# Treat zero norm as a special case.
if not est:
return 0
alpha = est / (_exact_1_norm(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)
# Implementation of Pade approximations of various degree
# using the algorithm presented in [Higham 2005].
# These should apply to both dense and sparse matricies.
# ident is the identity matrix, which matches A in being sparse or dense.
def _pade3(A, ident, A2=None):
b = (120., 60., 12., 1.)
if A2 is None:
A2 = A.dot(A)
U = A.dot(b[3]*A2 + b[1]*ident)
V = b[2]*A2 + b[0]*ident
return U,V
def _pade5(A, ident, A2=None, A4=None):
b = (30240., 15120., 3360., 420., 30., 1.)
if A2 is None:
A2 = A.dot(A)
if A4 is None:
A4 = A2.dot(A2)
U = A.dot(b[5]*A4 + b[3]*A2 + b[1]*ident)
V = b[4]*A4 + b[2]*A2 + b[0]*ident
return U,V
def _pade7(A, ident, A2=None, A4=None, A6=None):
b = (17297280., 8648640., 1995840., 277200., 25200., 1512., 56., 1.)
if A2 is None:
A2 = A.dot(A)
if A4 is None:
A4 = A2.dot(A2)
if A6 is None:
A6 = A4.dot(A2)
U = A.dot(b[7]*A6 + b[5]*A4 + b[3]*A2 + b[1]*ident)
V = b[6]*A6 + b[4]*A4 + b[2]*A2 + b[0]*ident
return U,V
def _pade9(A, ident, A2=None, A4=None, A6=None):
b = (17643225600., 8821612800., 2075673600., 302702400., 30270240.,
2162160., 110880., 3960., 90., 1.)
if A2 is None:
A2 = A.dot(A)
if A4 is None:
A4 = A2.dot(A2)
if A6 is None:
A6 = A4.dot(A2)
A8 = A6.dot(A2)
U = A.dot(b[9]*A8 + b[7]*A6 + b[5]*A4 + b[3]*A2 + b[1]*ident)
V = b[8]*A8 + b[6]*A6 + b[4]*A4 + b[2]*A2 + b[0]*ident
return U,V
def _pade13(A, ident, A2=None, A4=None, A6=None):
b = (64764752532480000., 32382376266240000., 7771770303897600.,
1187353796428800., 129060195264000., 10559470521600., 670442572800.,
33522128640., 1323241920., 40840800., 960960., 16380., 182., 1.)
if A2 is None:
A2 = A.dot(A)
if A4 is None:
A4 = A2.dot(A2)
if A6 is None:
A6 = A4.dot(A2)
U = A.dot(A6.dot(b[13]*A6 + b[11]*A4 + b[9]*A2) + b[7]*A6 + b[5]*A4 + b[3]*A2 + b[1]*ident)
V = A6.dot(b[12]*A6 + b[10]*A4 + b[8]*A2) + b[6]*A6 + b[4]*A4 + b[2]*A2 + b[0]*ident
return U,V