-
-
Notifications
You must be signed in to change notification settings - Fork 5.1k
/
_optimize.py
4109 lines (3559 loc) · 143 KB
/
_optimize.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
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#__docformat__ = "restructuredtext en"
# ******NOTICE***************
# optimize.py module by Travis E. Oliphant
#
# You may copy and use this module as you see fit with no
# guarantee implied provided you keep this notice in all copies.
# *****END NOTICE************
# A collection of optimization algorithms. Version 0.5
# CHANGES
# Added fminbound (July 2001)
# Added brute (Aug. 2002)
# Finished line search satisfying strong Wolfe conditions (Mar. 2004)
# Updated strong Wolfe conditions line search to use
# cubic-interpolation (Mar. 2004)
# Minimization routines
__all__ = ['fmin', 'fmin_powell', 'fmin_bfgs', 'fmin_ncg', 'fmin_cg',
'fminbound', 'brent', 'golden', 'bracket', 'rosen', 'rosen_der',
'rosen_hess', 'rosen_hess_prod', 'brute', 'approx_fprime',
'line_search', 'check_grad', 'OptimizeResult', 'show_options',
'OptimizeWarning']
__docformat__ = "restructuredtext en"
import warnings
import sys
import inspect
from numpy import (atleast_1d, eye, argmin, zeros, shape, squeeze,
asarray, sqrt, Inf)
import numpy as np
from scipy.sparse.linalg import LinearOperator
from ._linesearch import (line_search_wolfe1, line_search_wolfe2,
line_search_wolfe2 as line_search,
LineSearchWarning)
from ._numdiff import approx_derivative
from ._hessian_update_strategy import HessianUpdateStrategy
from scipy._lib._util import getfullargspec_no_self as _getfullargspec
from scipy._lib._util import MapWrapper, check_random_state
from scipy.optimize._differentiable_functions import ScalarFunction, FD_METHODS
# standard status messages of optimizers
_status_message = {'success': 'Optimization terminated successfully.',
'maxfev': 'Maximum number of function evaluations has '
'been exceeded.',
'maxiter': 'Maximum number of iterations has been '
'exceeded.',
'pr_loss': 'Desired error not necessarily achieved due '
'to precision loss.',
'nan': 'NaN result encountered.',
'out_of_bounds': 'The result is outside of the provided '
'bounds.'}
class MemoizeJac:
""" Decorator that caches the return values of a function returning `(fun, grad)`
each time it is called. """
def __init__(self, fun):
self.fun = fun
self.jac = None
self._value = None
self.x = None
def _compute_if_needed(self, x, *args):
if not np.all(x == self.x) or self._value is None or self.jac is None:
self.x = np.asarray(x).copy()
fg = self.fun(x, *args)
self.jac = fg[1]
self._value = fg[0]
def __call__(self, x, *args):
""" returns the function value """
self._compute_if_needed(x, *args)
return self._value
def derivative(self, x, *args):
self._compute_if_needed(x, *args)
return self.jac
def _indenter(s, n=0):
"""
Ensures that lines after the first are indented by the specified amount
"""
split = s.split("\n")
indent = " "*n
return ("\n" + indent).join(split)
def _float_formatter_10(x):
"""
Returns a string representation of a float with exactly ten characters
"""
if np.isposinf(x):
return " inf"
elif np.isneginf(x):
return " -inf"
elif np.isnan(x):
return " nan"
return np.format_float_scientific(x, precision=3, pad_left=2, unique=False)
def _dict_formatter(d, n=0, mplus=1, sorter=None):
"""
Pretty printer for dictionaries
`n` keeps track of the starting indentation;
lines are indented by this much after a line break.
`mplus` is additional left padding applied to keys
"""
if isinstance(d, dict):
m = max(map(len, list(d.keys()))) + mplus # width to print keys
s = '\n'.join([k.rjust(m) + ': ' + # right justified, width m
_indenter(_dict_formatter(v, m+n+2, 0, sorter), m+2)
for k, v in sorter(d)]) # +2 for ': '
else:
# By default, NumPy arrays print with linewidth=76. `n` is
# the indent at which a line begins printing, so it is subtracted
# from the default to avoid exceeding 76 characters total.
# `edgeitems` is the number of elements to include before and after
# ellipses when arrays are not shown in full.
# `threshold` is the maximum number of elements for which an
# array is shown in full.
# These values tend to work well for use with OptimizeResult.
with np.printoptions(linewidth=76-n, edgeitems=2, threshold=12,
formatter={'float_kind': _float_formatter_10}):
s = str(d)
return s
def _wrap_callback(callback, method=None):
"""Wrap a user-provided callback so that attributes can be attached."""
if callback is None or method in {'tnc', 'slsqp', 'cobyla'}:
return callback # don't wrap
sig = inspect.signature(callback)
if set(sig.parameters) == {'intermediate_result'}:
def wrapped_callback(res):
return callback(intermediate_result=res)
elif method == 'trust-constr':
def wrapped_callback(res):
return callback(np.copy(res.x), res)
else:
def wrapped_callback(res):
return callback(np.copy(res.x))
wrapped_callback.stop_iteration = False
return wrapped_callback
def _call_callback_maybe_halt(callback, res):
"""Call wrapped callback; return True if minimization should stop.
Parameters
----------
callback : callable or None
A user-provided callback wrapped with `_wrap_callback`
res : OptimizeResult
Information about the current iterate
Returns
-------
halt : bool
True if minimization should stop
"""
if callback is None:
return False
try:
callback(res)
return False
except StopIteration:
callback.stop_iteration = True # make `minimize` override status/msg
return True
class OptimizeResult(dict):
""" Represents the optimization result.
Attributes
----------
x : ndarray
The solution of the optimization.
success : bool
Whether or not the optimizer exited successfully.
status : int
Termination status of the optimizer. Its value depends on the
underlying solver. Refer to `message` for details.
message : str
Description of the cause of the termination.
fun, jac, hess: ndarray
Values of objective function, its Jacobian and its Hessian (if
available). The Hessians may be approximations, see the documentation
of the function in question.
hess_inv : object
Inverse of the objective function's Hessian; may be an approximation.
Not available for all solvers. The type of this attribute may be
either np.ndarray or scipy.sparse.linalg.LinearOperator.
nfev, njev, nhev : int
Number of evaluations of the objective functions and of its
Jacobian and Hessian.
nit : int
Number of iterations performed by the optimizer.
maxcv : float
The maximum constraint violation.
Notes
-----
Depending on the specific solver being used, `OptimizeResult` may
not have all attributes listed here, and they may have additional
attributes not listed here. Since this class is essentially a
subclass of dict with attribute accessors, one can see which
attributes are available using the `OptimizeResult.keys` method.
"""
def __getattr__(self, name):
try:
return self[name]
except KeyError as e:
raise AttributeError(name) from e
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
def __repr__(self):
order_keys = ['message', 'success', 'status', 'fun', 'funl', 'x', 'xl',
'col_ind', 'nit', 'lower', 'upper', 'eqlin', 'ineqlin',
'converged', 'flag', 'function_calls', 'iterations',
'root']
# 'slack', 'con' are redundant with residuals
# 'crossover_nit' is probably not interesting to most users
omit_keys = {'slack', 'con', 'crossover_nit'}
def key(item):
try:
return order_keys.index(item[0].lower())
except ValueError: # item not in list
return np.inf
def omit_redundant(items):
for item in items:
if item[0] in omit_keys:
continue
yield item
def item_sorter(d):
return sorted(omit_redundant(d.items()), key=key)
if self.keys():
return _dict_formatter(self, sorter=item_sorter)
else:
return self.__class__.__name__ + "()"
def __dir__(self):
return list(self.keys())
class OptimizeWarning(UserWarning):
pass
def _check_unknown_options(unknown_options):
if unknown_options:
msg = ", ".join(map(str, unknown_options.keys()))
# Stack level 4: this is called from _minimize_*, which is
# called from another function in SciPy. Level 4 is the first
# level in user code.
warnings.warn("Unknown solver options: %s" % msg, OptimizeWarning, 4)
def is_finite_scalar(x):
"""Test whether `x` is either a finite scalar or a finite array scalar.
"""
return np.size(x) == 1 and np.isfinite(x)
_epsilon = sqrt(np.finfo(float).eps)
def vecnorm(x, ord=2):
if ord == Inf:
return np.amax(np.abs(x))
elif ord == -Inf:
return np.amin(np.abs(x))
else:
return np.sum(np.abs(x)**ord, axis=0)**(1.0 / ord)
def _prepare_scalar_function(fun, x0, jac=None, args=(), bounds=None,
epsilon=None, finite_diff_rel_step=None,
hess=None):
"""
Creates a ScalarFunction object for use with scalar minimizers
(BFGS/LBFGSB/SLSQP/TNC/CG/etc).
Parameters
----------
fun : callable
The objective function to be minimized.
``fun(x, *args) -> float``
where ``x`` is an 1-D array with shape (n,) and ``args``
is a tuple of the fixed parameters needed to completely
specify the function.
x0 : ndarray, shape (n,)
Initial guess. Array of real elements of size (n,),
where 'n' is the number of independent variables.
jac : {callable, '2-point', '3-point', 'cs', None}, optional
Method for computing the gradient vector. If it is a callable, it
should be a function that returns the gradient vector:
``jac(x, *args) -> array_like, shape (n,)``
If one of `{'2-point', '3-point', 'cs'}` is selected then the gradient
is calculated with a relative step for finite differences. If `None`,
then two-point finite differences with an absolute step is used.
args : tuple, optional
Extra arguments passed to the objective function and its
derivatives (`fun`, `jac` functions).
bounds : sequence, optional
Bounds on variables. 'new-style' bounds are required.
eps : float or ndarray
If `jac is None` the absolute step size used for numerical
approximation of the jacobian via forward differences.
finite_diff_rel_step : None or array_like, optional
If `jac in ['2-point', '3-point', 'cs']` the relative step size to
use for numerical approximation of the jacobian. The absolute step
size is computed as ``h = rel_step * sign(x0) * max(1, abs(x0))``,
possibly adjusted to fit into the bounds. For ``method='3-point'``
the sign of `h` is ignored. If None (default) then step is selected
automatically.
hess : {callable, '2-point', '3-point', 'cs', None}
Computes the Hessian matrix. If it is callable, it should return the
Hessian matrix:
``hess(x, *args) -> {LinearOperator, spmatrix, array}, (n, n)``
Alternatively, the keywords {'2-point', '3-point', 'cs'} select a
finite difference scheme for numerical estimation.
Whenever the gradient is estimated via finite-differences, the Hessian
cannot be estimated with options {'2-point', '3-point', 'cs'} and needs
to be estimated using one of the quasi-Newton strategies.
Returns
-------
sf : ScalarFunction
"""
if callable(jac):
grad = jac
elif jac in FD_METHODS:
# epsilon is set to None so that ScalarFunction is made to use
# rel_step
epsilon = None
grad = jac
else:
# default (jac is None) is to do 2-point finite differences with
# absolute step size. ScalarFunction has to be provided an
# epsilon value that is not None to use absolute steps. This is
# normally the case from most _minimize* methods.
grad = '2-point'
epsilon = epsilon
if hess is None:
# ScalarFunction requires something for hess, so we give a dummy
# implementation here if nothing is provided, return a value of None
# so that downstream minimisers halt. The results of `fun.hess`
# should not be used.
def hess(x, *args):
return None
if bounds is None:
bounds = (-np.inf, np.inf)
# ScalarFunction caches. Reuse of fun(x) during grad
# calculation reduces overall function evaluations.
sf = ScalarFunction(fun, x0, args, grad, hess,
finite_diff_rel_step, bounds, epsilon=epsilon)
return sf
def _clip_x_for_func(func, bounds):
# ensures that x values sent to func are clipped to bounds
# this is used as a mitigation for gh11403, slsqp/tnc sometimes
# suggest a move that is outside the limits by 1 or 2 ULP. This
# unclean fix makes sure x is strictly within bounds.
def eval(x):
x = _check_clip_x(x, bounds)
return func(x)
return eval
def _check_clip_x(x, bounds):
if (x < bounds[0]).any() or (x > bounds[1]).any():
warnings.warn("Values in x were outside bounds during a "
"minimize step, clipping to bounds", RuntimeWarning)
x = np.clip(x, bounds[0], bounds[1])
return x
return x
def rosen(x):
"""
The Rosenbrock function.
The function computed is::
sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)
Parameters
----------
x : array_like
1-D array of points at which the Rosenbrock function is to be computed.
Returns
-------
f : float
The value of the Rosenbrock function.
See Also
--------
rosen_der, rosen_hess, rosen_hess_prod
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen
>>> X = 0.1 * np.arange(10)
>>> rosen(X)
76.56
For higher-dimensional input ``rosen`` broadcasts.
In the following example, we use this to plot a 2D landscape.
Note that ``rosen_hess`` does not broadcast in this manner.
>>> import matplotlib.pyplot as plt
>>> from mpl_toolkits.mplot3d import Axes3D
>>> x = np.linspace(-1, 1, 50)
>>> X, Y = np.meshgrid(x, x)
>>> ax = plt.subplot(111, projection='3d')
>>> ax.plot_surface(X, Y, rosen([X, Y]))
>>> plt.show()
"""
x = asarray(x)
r = np.sum(100.0 * (x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0,
axis=0)
return r
def rosen_der(x):
"""
The derivative (i.e. gradient) of the Rosenbrock function.
Parameters
----------
x : array_like
1-D array of points at which the derivative is to be computed.
Returns
-------
rosen_der : (N,) ndarray
The gradient of the Rosenbrock function at `x`.
See Also
--------
rosen, rosen_hess, rosen_hess_prod
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen_der
>>> X = 0.1 * np.arange(9)
>>> rosen_der(X)
array([ -2. , 10.6, 15.6, 13.4, 6.4, -3. , -12.4, -19.4, 62. ])
"""
x = asarray(x)
xm = x[1:-1]
xm_m1 = x[:-2]
xm_p1 = x[2:]
der = np.zeros_like(x)
der[1:-1] = (200 * (xm - xm_m1**2) -
400 * (xm_p1 - xm**2) * xm - 2 * (1 - xm))
der[0] = -400 * x[0] * (x[1] - x[0]**2) - 2 * (1 - x[0])
der[-1] = 200 * (x[-1] - x[-2]**2)
return der
def rosen_hess(x):
"""
The Hessian matrix of the Rosenbrock function.
Parameters
----------
x : array_like
1-D array of points at which the Hessian matrix is to be computed.
Returns
-------
rosen_hess : ndarray
The Hessian matrix of the Rosenbrock function at `x`.
See Also
--------
rosen, rosen_der, rosen_hess_prod
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen_hess
>>> X = 0.1 * np.arange(4)
>>> rosen_hess(X)
array([[-38., 0., 0., 0.],
[ 0., 134., -40., 0.],
[ 0., -40., 130., -80.],
[ 0., 0., -80., 200.]])
"""
x = atleast_1d(x)
H = np.diag(-400 * x[:-1], 1) - np.diag(400 * x[:-1], -1)
diagonal = np.zeros(len(x), dtype=x.dtype)
diagonal[0] = 1200 * x[0]**2 - 400 * x[1] + 2
diagonal[-1] = 200
diagonal[1:-1] = 202 + 1200 * x[1:-1]**2 - 400 * x[2:]
H = H + np.diag(diagonal)
return H
def rosen_hess_prod(x, p):
"""
Product of the Hessian matrix of the Rosenbrock function with a vector.
Parameters
----------
x : array_like
1-D array of points at which the Hessian matrix is to be computed.
p : array_like
1-D array, the vector to be multiplied by the Hessian matrix.
Returns
-------
rosen_hess_prod : ndarray
The Hessian matrix of the Rosenbrock function at `x` multiplied
by the vector `p`.
See Also
--------
rosen, rosen_der, rosen_hess
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import rosen_hess_prod
>>> X = 0.1 * np.arange(9)
>>> p = 0.5 * np.arange(9)
>>> rosen_hess_prod(X, p)
array([ -0., 27., -10., -95., -192., -265., -278., -195., -180.])
"""
x = atleast_1d(x)
Hp = np.zeros(len(x), dtype=x.dtype)
Hp[0] = (1200 * x[0]**2 - 400 * x[1] + 2) * p[0] - 400 * x[0] * p[1]
Hp[1:-1] = (-400 * x[:-2] * p[:-2] +
(202 + 1200 * x[1:-1]**2 - 400 * x[2:]) * p[1:-1] -
400 * x[1:-1] * p[2:])
Hp[-1] = -400 * x[-2] * p[-2] + 200*p[-1]
return Hp
def _wrap_scalar_function(function, args):
# wraps a minimizer function to count number of evaluations
# and to easily provide an args kwd.
ncalls = [0]
if function is None:
return ncalls, None
def function_wrapper(x, *wrapper_args):
ncalls[0] += 1
# A copy of x is sent to the user function (gh13740)
fx = function(np.copy(x), *(wrapper_args + args))
# Ideally, we'd like to a have a true scalar returned from f(x). For
# backwards-compatibility, also allow np.array([1.3]), np.array([[1.3]]) etc.
if not np.isscalar(fx):
try:
fx = np.asarray(fx).item()
except (TypeError, ValueError) as e:
raise ValueError("The user-provided objective function "
"must return a scalar value.") from e
return fx
return ncalls, function_wrapper
class _MaxFuncCallError(RuntimeError):
pass
def _wrap_scalar_function_maxfun_validation(function, args, maxfun):
# wraps a minimizer function to count number of evaluations
# and to easily provide an args kwd.
ncalls = [0]
if function is None:
return ncalls, None
def function_wrapper(x, *wrapper_args):
if ncalls[0] >= maxfun:
raise _MaxFuncCallError("Too many function calls")
ncalls[0] += 1
# A copy of x is sent to the user function (gh13740)
fx = function(np.copy(x), *(wrapper_args + args))
# Ideally, we'd like to a have a true scalar returned from f(x). For
# backwards-compatibility, also allow np.array([1.3]),
# np.array([[1.3]]) etc.
if not np.isscalar(fx):
try:
fx = np.asarray(fx).item()
except (TypeError, ValueError) as e:
raise ValueError("The user-provided objective function "
"must return a scalar value.") from e
return fx
return ncalls, function_wrapper
def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None,
full_output=0, disp=1, retall=0, callback=None, initial_simplex=None):
"""
Minimize a function using the downhill simplex algorithm.
This algorithm only uses function values, not derivatives or second
derivatives.
Parameters
----------
func : callable func(x,*args)
The objective function to be minimized.
x0 : ndarray
Initial guess.
args : tuple, optional
Extra arguments passed to func, i.e., ``f(x,*args)``.
xtol : float, optional
Absolute error in xopt between iterations that is acceptable for
convergence.
ftol : number, optional
Absolute error in func(xopt) between iterations that is acceptable for
convergence.
maxiter : int, optional
Maximum number of iterations to perform.
maxfun : number, optional
Maximum number of function evaluations to make.
full_output : bool, optional
Set to True if fopt and warnflag outputs are desired.
disp : bool, optional
Set to True to print convergence messages.
retall : bool, optional
Set to True to return list of solutions at each iteration.
callback : callable, optional
Called after each iteration, as callback(xk), where xk is the
current parameter vector.
initial_simplex : array_like of shape (N + 1, N), optional
Initial simplex. If given, overrides `x0`.
``initial_simplex[j,:]`` should contain the coordinates of
the jth vertex of the ``N+1`` vertices in the simplex, where
``N`` is the dimension.
Returns
-------
xopt : ndarray
Parameter that minimizes function.
fopt : float
Value of function at minimum: ``fopt = func(xopt)``.
iter : int
Number of iterations performed.
funcalls : int
Number of function calls made.
warnflag : int
1 : Maximum number of function evaluations made.
2 : Maximum number of iterations reached.
allvecs : list
Solution at each iteration.
See also
--------
minimize: Interface to minimization algorithms for multivariate
functions. See the 'Nelder-Mead' `method` in particular.
Notes
-----
Uses a Nelder-Mead simplex algorithm to find the minimum of function of
one or more variables.
This algorithm has a long history of successful use in applications.
But it will usually be slower than an algorithm that uses first or
second derivative information. In practice, it can have poor
performance in high-dimensional problems and is not robust to
minimizing complicated functions. Additionally, there currently is no
complete theory describing when the algorithm will successfully
converge to the minimum, or how fast it will if it does. Both the ftol and
xtol criteria must be met for convergence.
Examples
--------
>>> def f(x):
... return x**2
>>> from scipy import optimize
>>> minimum = optimize.fmin(f, 1)
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 17
Function evaluations: 34
>>> minimum[0]
-8.8817841970012523e-16
References
----------
.. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function
minimization", The Computer Journal, 7, pp. 308-313
.. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned, Now
Respectable", in Numerical Analysis 1995, Proceedings of the
1995 Dundee Biennial Conference in Numerical Analysis, D.F.
Griffiths and G.A. Watson (Eds.), Addison Wesley Longman,
Harlow, UK, pp. 191-208.
"""
opts = {'xatol': xtol,
'fatol': ftol,
'maxiter': maxiter,
'maxfev': maxfun,
'disp': disp,
'return_all': retall,
'initial_simplex': initial_simplex}
callback = _wrap_callback(callback)
res = _minimize_neldermead(func, x0, args, callback=callback, **opts)
if full_output:
retlist = res['x'], res['fun'], res['nit'], res['nfev'], res['status']
if retall:
retlist += (res['allvecs'], )
return retlist
else:
if retall:
return res['x'], res['allvecs']
else:
return res['x']
def _minimize_neldermead(func, x0, args=(), callback=None,
maxiter=None, maxfev=None, disp=False,
return_all=False, initial_simplex=None,
xatol=1e-4, fatol=1e-4, adaptive=False, bounds=None,
**unknown_options):
"""
Minimization of scalar function of one or more variables using the
Nelder-Mead algorithm.
Options
-------
disp : bool
Set to True to print convergence messages.
maxiter, maxfev : int
Maximum allowed number of iterations and function evaluations.
Will default to ``N*200``, where ``N`` is the number of
variables, if neither `maxiter` or `maxfev` is set. If both
`maxiter` and `maxfev` are set, minimization will stop at the
first reached.
return_all : bool, optional
Set to True to return a list of the best solution at each of the
iterations.
initial_simplex : array_like of shape (N + 1, N)
Initial simplex. If given, overrides `x0`.
``initial_simplex[j,:]`` should contain the coordinates of
the jth vertex of the ``N+1`` vertices in the simplex, where
``N`` is the dimension.
xatol : float, optional
Absolute error in xopt between iterations that is acceptable for
convergence.
fatol : number, optional
Absolute error in func(xopt) between iterations that is acceptable for
convergence.
adaptive : bool, optional
Adapt algorithm parameters to dimensionality of problem. Useful for
high-dimensional minimization [1]_.
bounds : sequence or `Bounds`, optional
Bounds on variables. There are two ways to specify the bounds:
1. Instance of `Bounds` class.
2. Sequence of ``(min, max)`` pairs for each element in `x`. None
is used to specify no bound.
Note that this just clips all vertices in simplex based on
the bounds.
References
----------
.. [1] Gao, F. and Han, L.
Implementing the Nelder-Mead simplex algorithm with adaptive
parameters. 2012. Computational Optimization and Applications.
51:1, pp. 259-277
"""
_check_unknown_options(unknown_options)
maxfun = maxfev
retall = return_all
x0 = np.atleast_1d(x0).flatten()
x0 = np.asfarray(x0, x0.dtype)
if adaptive:
dim = float(len(x0))
rho = 1
chi = 1 + 2/dim
psi = 0.75 - 1/(2*dim)
sigma = 1 - 1/dim
else:
rho = 1
chi = 2
psi = 0.5
sigma = 0.5
nonzdelt = 0.05
zdelt = 0.00025
if bounds is not None:
lower_bound, upper_bound = bounds.lb, bounds.ub
# check bounds
if (lower_bound > upper_bound).any():
raise ValueError("Nelder Mead - one of the lower bounds is greater than an upper bound.")
if np.any(lower_bound > x0) or np.any(x0 > upper_bound):
warnings.warn("Initial guess is not within the specified bounds",
OptimizeWarning, 3)
if bounds is not None:
x0 = np.clip(x0, lower_bound, upper_bound)
if initial_simplex is None:
N = len(x0)
sim = np.empty((N + 1, N), dtype=x0.dtype)
sim[0] = x0
for k in range(N):
y = np.array(x0, copy=True)
if y[k] != 0:
y[k] = (1 + nonzdelt)*y[k]
else:
y[k] = zdelt
sim[k + 1] = y
else:
sim = np.atleast_2d(initial_simplex).copy()
sim = np.asfarray(sim, sim.dtype)
if sim.ndim != 2 or sim.shape[0] != sim.shape[1] + 1:
raise ValueError("`initial_simplex` should be an array of shape (N+1,N)")
if len(x0) != sim.shape[1]:
raise ValueError("Size of `initial_simplex` is not consistent with `x0`")
N = sim.shape[1]
if retall:
allvecs = [sim[0]]
# If neither are set, then set both to default
if maxiter is None and maxfun is None:
maxiter = N * 200
maxfun = N * 200
elif maxiter is None:
# Convert remaining Nones, to np.inf, unless the other is np.inf, in
# which case use the default to avoid unbounded iteration
if maxfun == np.inf:
maxiter = N * 200
else:
maxiter = np.inf
elif maxfun is None:
if maxiter == np.inf:
maxfun = N * 200
else:
maxfun = np.inf
if bounds is not None:
sim = np.clip(sim, lower_bound, upper_bound)
one2np1 = list(range(1, N + 1))
fsim = np.full((N + 1,), np.inf, dtype=float)
fcalls, func = _wrap_scalar_function_maxfun_validation(func, args, maxfun)
try:
for k in range(N + 1):
fsim[k] = func(sim[k])
except _MaxFuncCallError:
pass
finally:
ind = np.argsort(fsim)
sim = np.take(sim, ind, 0)
fsim = np.take(fsim, ind, 0)
ind = np.argsort(fsim)
fsim = np.take(fsim, ind, 0)
# sort so sim[0,:] has the lowest function value
sim = np.take(sim, ind, 0)
iterations = 1
while (fcalls[0] < maxfun and iterations < maxiter):
try:
if (np.max(np.ravel(np.abs(sim[1:] - sim[0]))) <= xatol and
np.max(np.abs(fsim[0] - fsim[1:])) <= fatol):
break
xbar = np.add.reduce(sim[:-1], 0) / N
xr = (1 + rho) * xbar - rho * sim[-1]
if bounds is not None:
xr = np.clip(xr, lower_bound, upper_bound)
fxr = func(xr)
doshrink = 0
if fxr < fsim[0]:
xe = (1 + rho * chi) * xbar - rho * chi * sim[-1]
if bounds is not None:
xe = np.clip(xe, lower_bound, upper_bound)
fxe = func(xe)
if fxe < fxr:
sim[-1] = xe
fsim[-1] = fxe
else:
sim[-1] = xr
fsim[-1] = fxr
else: # fsim[0] <= fxr
if fxr < fsim[-2]:
sim[-1] = xr
fsim[-1] = fxr
else: # fxr >= fsim[-2]
# Perform contraction
if fxr < fsim[-1]:
xc = (1 + psi * rho) * xbar - psi * rho * sim[-1]
if bounds is not None:
xc = np.clip(xc, lower_bound, upper_bound)
fxc = func(xc)
if fxc <= fxr:
sim[-1] = xc
fsim[-1] = fxc
else:
doshrink = 1
else:
# Perform an inside contraction
xcc = (1 - psi) * xbar + psi * sim[-1]
if bounds is not None:
xcc = np.clip(xcc, lower_bound, upper_bound)
fxcc = func(xcc)
if fxcc < fsim[-1]:
sim[-1] = xcc
fsim[-1] = fxcc
else:
doshrink = 1
if doshrink:
for j in one2np1:
sim[j] = sim[0] + sigma * (sim[j] - sim[0])
if bounds is not None:
sim[j] = np.clip(
sim[j], lower_bound, upper_bound)
fsim[j] = func(sim[j])
iterations += 1
except _MaxFuncCallError:
pass
finally:
ind = np.argsort(fsim)
sim = np.take(sim, ind, 0)
fsim = np.take(fsim, ind, 0)
if retall:
allvecs.append(sim[0])
intermediate_result = OptimizeResult(x=sim[0], fun=fsim[0])
if _call_callback_maybe_halt(callback, intermediate_result):
break
x = sim[0]
fval = np.min(fsim)
warnflag = 0
if fcalls[0] >= maxfun:
warnflag = 1
msg = _status_message['maxfev']
if disp:
warnings.warn(msg, RuntimeWarning, 3)
elif iterations >= maxiter:
warnflag = 2
msg = _status_message['maxiter']