CSC with a Spatial Mask¶
This example demonstrates the use of cbpdn.AddMaskSim for
convolutional sparse coding with a spatial mask
[50]. If the sporco-cuda extension is
installed and a GPU is available, a GPU accelerated version is used. The
example problem is inpainting of randomly distributed corruption of a
greyscale image.
from __future__ import print_function
from builtins import input
import pyfftw # See https://github.com/pyFFTW/pyFFTW/issues/40
import numpy as np
from sporco import util
from sporco import signal
from sporco import metric
from sporco import plot
plot.config_notebook_plotting()
from sporco.admm import tvl2
from sporco.admm import cbpdn
from sporco.fft import fftconv
from sporco import cuda
# If running in a notebook, try to use wurlitzer so that output from the CUDA
# code will be properly captured in the notebook.
sys_pipes = util.notebook_system_output()
Load a reference image.
img = util.ExampleImages().image('monarch.png', zoom=0.5, scaled=True,
gray=True, idxexp=np.s_[:, 160:672])
Create random mask and apply to reference image to obtain test image.
(The call to numpy.random.seed ensures that the pseudo-random noise
is reproducible.)
np.random.seed(12345)
frc = 0.5
msk = signal.rndmask(img.shape, frc, dtype=np.float32)
imgw = msk * img
Define pad and crop functions.
pn = 8
spad = lambda x: np.pad(x, pn, mode='symmetric')
zpad = lambda x: np.pad(x, pn, mode='constant')
crop = lambda x: x[pn:-pn, pn:-pn]
Construct padded mask and test image.
mskp = zpad(msk)
imgwp = spad(imgw)
\(\ell_2\)-TV denoising with a spatial mask as a non-linear lowpass filter. The highpass component is the difference between the test image and the lowpass component, multiplied by the mask for faster convergence of the convolutional sparse coding (see [60]).
lmbda = 0.05
opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 200,
'DFidWeight': mskp, 'gEvalY': False,
'AutoRho': {'Enabled': True}})
b = tvl2.TVL2Denoise(imgwp, lmbda, opt)
sl = b.solve()
sh = mskp * (imgwp - sl)
Load dictionary.
D = util.convdicts()['G:8x8x128']
Set up admm.cbpdn.ConvBPDN options.
lmbda = 2e-2
opt = cbpdn.ConvBPDN.Options({'Verbose': True, 'MaxMainIter': 200,
'HighMemSolve': True, 'RelStopTol': 5e-3,
'AuxVarObj': False, 'RelaxParam': 1.8,
'rho': 5e1*lmbda + 1e-1, 'AutoRho': {'Enabled': False,
'StdResiduals': False}})
Construct admm.cbpdn.AddMaskSim wrapper for
admm.cbpdn.ConvBPDN and solve via wrapper. This example
could also have made use of admm.cbpdn.ConvBPDNMaskDcpl (see
example cbpdn_md_gry), which has similar performance in this
application, but admm.cbpdn.AddMaskSim has the advantage of
greater flexibility in that the wrapper can be applied to a variety of
CSC solver objects. If the sporco-cuda extension is installed and a
GPU is available, use the CUDA implementation of this combination.
if cuda.device_count() > 0:
ams = None
print('%s GPU found: running CUDA solver' % cuda.device_name())
tm = util.Timer()
with sys_pipes(), util.ContextTimer(tm):
X = cuda.cbpdnmsk(D, sh, mskp, lmbda, opt)
t = tm.elapsed()
imgr = crop(sl + np.sum(fftconv(D, X, axes=(0, 1)), axis=-1))
else:
ams = cbpdn.AddMaskSim(cbpdn.ConvBPDN, D, sh, mskp, lmbda, opt=opt)
X = ams.solve()
t = ams.timer.elapsed('solve')
imgr = crop(sl + ams.reconstruct().squeeze())
GeForce RTX 2080 Ti GPU found: running CUDA solver
Itn Fnc DFid Regℓ1 r s ρ
----------------------------------------------------------------
0 3.07e+01 4.16e-02 1.53e+03 9.94e-01 1.85e-01 1.10e+00
1 2.69e+01 2.13e-01 1.33e+03 8.05e-01 2.20e-01 1.10e+00
2 2.51e+01 3.39e-01 1.24e+03 4.52e-01 1.83e-01 1.10e+00
3 2.41e+01 4.19e-01 1.19e+03 3.05e-01 1.48e-01 1.10e+00
4 2.34e+01 4.80e-01 1.14e+03 2.30e-01 1.24e-01 1.10e+00
5 2.29e+01 5.29e-01 1.12e+03 1.87e-01 1.07e-01 1.10e+00
6 2.27e+01 5.69e-01 1.11e+03 1.60e-01 9.26e-02 1.10e+00
7 2.26e+01 6.02e-01 1.10e+03 1.41e-01 8.09e-02 1.10e+00
8 2.25e+01 6.33e-01 1.09e+03 1.27e-01 7.18e-02 1.10e+00
9 2.24e+01 6.61e-01 1.09e+03 1.17e-01 6.47e-02 1.10e+00
10 2.22e+01 6.88e-01 1.07e+03 1.09e-01 5.94e-02 1.10e+00
11 2.19e+01 7.15e-01 1.06e+03 1.02e-01 5.54e-02 1.10e+00
12 2.16e+01 7.42e-01 1.04e+03 9.68e-02 5.24e-02 1.10e+00
13 2.13e+01 7.68e-01 1.03e+03 9.21e-02 5.03e-02 1.10e+00
14 2.10e+01 7.92e-01 1.01e+03 8.81e-02 4.86e-02 1.10e+00
15 2.08e+01 8.13e-01 9.99e+02 8.45e-02 4.73e-02 1.10e+00
16 2.05e+01 8.33e-01 9.86e+02 8.13e-02 4.61e-02 1.10e+00
17 2.03e+01 8.50e-01 9.74e+02 7.84e-02 4.52e-02 1.10e+00
18 2.02e+01 8.66e-01 9.64e+02 7.56e-02 4.40e-02 1.10e+00
19 2.00e+01 8.82e-01 9.57e+02 7.31e-02 4.23e-02 1.10e+00
20 1.99e+01 8.97e-01 9.51e+02 7.08e-02 4.06e-02 1.10e+00
21 1.97e+01 9.13e-01 9.40e+02 6.85e-02 3.89e-02 1.10e+00
22 1.94e+01 9.30e-01 9.24e+02 6.63e-02 3.74e-02 1.10e+00
23 1.90e+01 9.47e-01 9.03e+02 6.41e-02 3.61e-02 1.10e+00
24 1.86e+01 9.66e-01 8.80e+02 6.20e-02 3.50e-02 1.10e+00
25 1.81e+01 9.85e-01 8.56e+02 5.99e-02 3.37e-02 1.10e+00
26 1.77e+01 1.00e+00 8.33e+02 5.79e-02 3.24e-02 1.10e+00
27 1.73e+01 1.02e+00 8.13e+02 5.60e-02 3.12e-02 1.10e+00
28 1.69e+01 1.04e+00 7.95e+02 5.42e-02 3.00e-02 1.10e+00
29 1.67e+01 1.06e+00 7.80e+02 5.24e-02 2.88e-02 1.10e+00
30 1.64e+01 1.08e+00 7.66e+02 5.08e-02 2.79e-02 1.10e+00
31 1.62e+01 1.10e+00 7.54e+02 4.92e-02 2.69e-02 1.10e+00
32 1.60e+01 1.11e+00 7.44e+02 4.77e-02 2.61e-02 1.10e+00
33 1.58e+01 1.12e+00 7.35e+02 4.63e-02 2.53e-02 1.10e+00
34 1.57e+01 1.14e+00 7.27e+02 4.50e-02 2.45e-02 1.10e+00
35 1.55e+01 1.15e+00 7.20e+02 4.37e-02 2.37e-02 1.10e+00
36 1.54e+01 1.16e+00 7.13e+02 4.26e-02 2.30e-02 1.10e+00
37 1.53e+01 1.17e+00 7.06e+02 4.15e-02 2.23e-02 1.10e+00
38 1.51e+01 1.17e+00 6.98e+02 4.04e-02 2.17e-02 1.10e+00
39 1.50e+01 1.18e+00 6.89e+02 3.94e-02 2.11e-02 1.10e+00
40 1.48e+01 1.19e+00 6.79e+02 3.84e-02 2.06e-02 1.10e+00
41 1.46e+01 1.20e+00 6.69e+02 3.74e-02 2.02e-02 1.10e+00
42 1.44e+01 1.21e+00 6.60e+02 3.65e-02 1.98e-02 1.10e+00
43 1.42e+01 1.22e+00 6.51e+02 3.56e-02 1.93e-02 1.10e+00
44 1.41e+01 1.23e+00 6.44e+02 3.48e-02 1.88e-02 1.10e+00
45 1.40e+01 1.23e+00 6.37e+02 3.40e-02 1.83e-02 1.10e+00
46 1.39e+01 1.24e+00 6.31e+02 3.32e-02 1.78e-02 1.10e+00
47 1.37e+01 1.25e+00 6.25e+02 3.24e-02 1.73e-02 1.10e+00
48 1.36e+01 1.25e+00 6.19e+02 3.17e-02 1.68e-02 1.10e+00
49 1.35e+01 1.26e+00 6.14e+02 3.10e-02 1.64e-02 1.10e+00
50 1.34e+01 1.27e+00 6.08e+02 3.03e-02 1.61e-02 1.10e+00
51 1.33e+01 1.27e+00 6.03e+02 2.96e-02 1.57e-02 1.10e+00
52 1.32e+01 1.28e+00 5.97e+02 2.90e-02 1.54e-02 1.10e+00
53 1.31e+01 1.28e+00 5.92e+02 2.83e-02 1.51e-02 1.10e+00
54 1.30e+01 1.28e+00 5.87e+02 2.77e-02 1.48e-02 1.10e+00
55 1.29e+01 1.29e+00 5.82e+02 2.72e-02 1.45e-02 1.10e+00
56 1.28e+01 1.29e+00 5.78e+02 2.66e-02 1.42e-02 1.10e+00
57 1.28e+01 1.29e+00 5.73e+02 2.61e-02 1.39e-02 1.10e+00
58 1.27e+01 1.30e+00 5.69e+02 2.56e-02 1.36e-02 1.10e+00
59 1.26e+01 1.30e+00 5.64e+02 2.52e-02 1.33e-02 1.10e+00
60 1.25e+01 1.30e+00 5.60e+02 2.47e-02 1.30e-02 1.10e+00
61 1.24e+01 1.30e+00 5.56e+02 2.43e-02 1.28e-02 1.10e+00
62 1.23e+01 1.31e+00 5.51e+02 2.38e-02 1.25e-02 1.10e+00
63 1.22e+01 1.31e+00 5.47e+02 2.34e-02 1.23e-02 1.10e+00
64 1.22e+01 1.31e+00 5.42e+02 2.30e-02 1.21e-02 1.10e+00
65 1.21e+01 1.31e+00 5.38e+02 2.26e-02 1.19e-02 1.10e+00
66 1.20e+01 1.32e+00 5.33e+02 2.22e-02 1.17e-02 1.10e+00
67 1.19e+01 1.32e+00 5.29e+02 2.18e-02 1.15e-02 1.10e+00
68 1.18e+01 1.32e+00 5.25e+02 2.14e-02 1.13e-02 1.10e+00
69 1.18e+01 1.32e+00 5.22e+02 2.11e-02 1.11e-02 1.10e+00
70 1.17e+01 1.33e+00 5.19e+02 2.07e-02 1.10e-02 1.10e+00
71 1.16e+01 1.33e+00 5.16e+02 2.04e-02 1.08e-02 1.10e+00
72 1.16e+01 1.33e+00 5.13e+02 2.01e-02 1.05e-02 1.10e+00
73 1.15e+01 1.34e+00 5.11e+02 1.98e-02 1.03e-02 1.10e+00
74 1.15e+01 1.34e+00 5.08e+02 1.95e-02 1.01e-02 1.10e+00
75 1.15e+01 1.34e+00 5.06e+02 1.92e-02 9.91e-03 1.10e+00
76 1.14e+01 1.34e+00 5.03e+02 1.89e-02 9.72e-03 1.10e+00
77 1.13e+01 1.34e+00 5.00e+02 1.86e-02 9.54e-03 1.10e+00
78 1.13e+01 1.35e+00 4.97e+02 1.83e-02 9.38e-03 1.10e+00
79 1.12e+01 1.35e+00 4.94e+02 1.80e-02 9.24e-03 1.10e+00
80 1.12e+01 1.35e+00 4.90e+02 1.77e-02 9.10e-03 1.10e+00
81 1.11e+01 1.35e+00 4.87e+02 1.74e-02 8.98e-03 1.10e+00
82 1.10e+01 1.36e+00 4.84e+02 1.71e-02 8.87e-03 1.10e+00
83 1.10e+01 1.36e+00 4.81e+02 1.69e-02 8.75e-03 1.10e+00
84 1.09e+01 1.36e+00 4.78e+02 1.66e-02 8.63e-03 1.10e+00
85 1.09e+01 1.36e+00 4.75e+02 1.64e-02 8.50e-03 1.10e+00
86 1.08e+01 1.36e+00 4.73e+02 1.61e-02 8.36e-03 1.10e+00
87 1.08e+01 1.37e+00 4.71e+02 1.59e-02 8.23e-03 1.10e+00
88 1.07e+01 1.37e+00 4.68e+02 1.57e-02 8.10e-03 1.10e+00
89 1.07e+01 1.37e+00 4.66e+02 1.54e-02 7.97e-03 1.10e+00
90 1.07e+01 1.37e+00 4.64e+02 1.52e-02 7.84e-03 1.10e+00
91 1.06e+01 1.37e+00 4.62e+02 1.50e-02 7.71e-03 1.10e+00
92 1.06e+01 1.38e+00 4.60e+02 1.48e-02 7.59e-03 1.10e+00
93 1.05e+01 1.38e+00 4.58e+02 1.46e-02 7.47e-03 1.10e+00
94 1.05e+01 1.38e+00 4.56e+02 1.44e-02 7.35e-03 1.10e+00
95 1.05e+01 1.38e+00 4.54e+02 1.42e-02 7.23e-03 1.10e+00
96 1.04e+01 1.38e+00 4.53e+02 1.40e-02 7.12e-03 1.10e+00
97 1.04e+01 1.39e+00 4.51e+02 1.38e-02 7.00e-03 1.10e+00
98 1.04e+01 1.39e+00 4.49e+02 1.36e-02 6.90e-03 1.10e+00
99 1.03e+01 1.39e+00 4.47e+02 1.34e-02 6.79e-03 1.10e+00
100 1.03e+01 1.39e+00 4.46e+02 1.32e-02 6.68e-03 1.10e+00
101 1.03e+01 1.39e+00 4.44e+02 1.30e-02 6.58e-03 1.10e+00
102 1.02e+01 1.39e+00 4.42e+02 1.28e-02 6.48e-03 1.10e+00
103 1.02e+01 1.39e+00 4.41e+02 1.27e-02 6.39e-03 1.10e+00
104 1.02e+01 1.40e+00 4.39e+02 1.25e-02 6.31e-03 1.10e+00
105 1.01e+01 1.40e+00 4.37e+02 1.23e-02 6.23e-03 1.10e+00
106 1.01e+01 1.40e+00 4.35e+02 1.21e-02 6.16e-03 1.10e+00
107 1.01e+01 1.40e+00 4.33e+02 1.20e-02 6.09e-03 1.10e+00
108 1.00e+01 1.40e+00 4.32e+02 1.18e-02 6.01e-03 1.10e+00
109 1.00e+01 1.40e+00 4.30e+02 1.17e-02 5.94e-03 1.10e+00
110 9.97e+00 1.40e+00 4.28e+02 1.15e-02 5.87e-03 1.10e+00
111 9.93e+00 1.40e+00 4.27e+02 1.14e-02 5.80e-03 1.10e+00
112 9.90e+00 1.40e+00 4.25e+02 1.12e-02 5.72e-03 1.10e+00
113 9.87e+00 1.40e+00 4.23e+02 1.11e-02 5.65e-03 1.10e+00
114 9.85e+00 1.41e+00 4.22e+02 1.09e-02 5.57e-03 1.10e+00
115 9.82e+00 1.41e+00 4.21e+02 1.08e-02 5.49e-03 1.10e+00
116 9.79e+00 1.41e+00 4.19e+02 1.07e-02 5.41e-03 1.10e+00
117 9.76e+00 1.41e+00 4.18e+02 1.05e-02 5.34e-03 1.10e+00
118 9.74e+00 1.41e+00 4.16e+02 1.04e-02 5.27e-03 1.10e+00
119 9.71e+00 1.41e+00 4.15e+02 1.03e-02 5.20e-03 1.10e+00
120 9.69e+00 1.41e+00 4.14e+02 1.01e-02 5.13e-03 1.10e+00
121 9.66e+00 1.41e+00 4.13e+02 1.00e-02 5.06e-03 1.10e+00
122 9.64e+00 1.41e+00 4.11e+02 9.88e-03 4.99e-03 1.10e+00
123 9.61e+00 1.41e+00 4.10e+02 9.75e-03 4.93e-03 1.10e+00
124 9.59e+00 1.41e+00 4.09e+02 9.63e-03 4.87e-03 1.10e+00
125 9.57e+00 1.41e+00 4.08e+02 9.51e-03 4.81e-03 1.10e+00
126 9.54e+00 1.41e+00 4.06e+02 9.40e-03 4.75e-03 1.10e+00
127 9.52e+00 1.42e+00 4.05e+02 9.28e-03 4.69e-03 1.10e+00
128 9.50e+00 1.42e+00 4.04e+02 9.17e-03 4.63e-03 1.10e+00
129 9.47e+00 1.42e+00 4.03e+02 9.05e-03 4.57e-03 1.10e+00
130 9.45e+00 1.42e+00 4.02e+02 8.95e-03 4.51e-03 1.10e+00
131 9.43e+00 1.42e+00 4.00e+02 8.84e-03 4.46e-03 1.10e+00
132 9.41e+00 1.42e+00 3.99e+02 8.73e-03 4.40e-03 1.10e+00
133 9.38e+00 1.42e+00 3.98e+02 8.62e-03 4.35e-03 1.10e+00
134 9.36e+00 1.42e+00 3.97e+02 8.52e-03 4.30e-03 1.10e+00
135 9.34e+00 1.42e+00 3.96e+02 8.42e-03 4.24e-03 1.10e+00
136 9.32e+00 1.42e+00 3.95e+02 8.32e-03 4.19e-03 1.10e+00
137 9.30e+00 1.42e+00 3.94e+02 8.22e-03 4.13e-03 1.10e+00
138 9.29e+00 1.42e+00 3.93e+02 8.13e-03 4.08e-03 1.10e+00
139 9.27e+00 1.42e+00 3.92e+02 8.03e-03 4.03e-03 1.10e+00
140 9.25e+00 1.42e+00 3.91e+02 7.94e-03 3.98e-03 1.10e+00
141 9.23e+00 1.43e+00 3.90e+02 7.85e-03 3.93e-03 1.10e+00
142 9.21e+00 1.43e+00 3.89e+02 7.76e-03 3.89e-03 1.10e+00
143 9.20e+00 1.43e+00 3.88e+02 7.67e-03 3.84e-03 1.10e+00
144 9.18e+00 1.43e+00 3.88e+02 7.58e-03 3.80e-03 1.10e+00
145 9.16e+00 1.43e+00 3.87e+02 7.50e-03 3.75e-03 1.10e+00
146 9.14e+00 1.43e+00 3.86e+02 7.41e-03 3.71e-03 1.10e+00
147 9.12e+00 1.43e+00 3.85e+02 7.33e-03 3.67e-03 1.10e+00
148 9.10e+00 1.43e+00 3.84e+02 7.25e-03 3.63e-03 1.10e+00
149 9.09e+00 1.43e+00 3.83e+02 7.16e-03 3.59e-03 1.10e+00
150 9.07e+00 1.43e+00 3.82e+02 7.08e-03 3.56e-03 1.10e+00
151 9.05e+00 1.43e+00 3.81e+02 7.00e-03 3.52e-03 1.10e+00
152 9.03e+00 1.43e+00 3.80e+02 6.93e-03 3.48e-03 1.10e+00
153 9.02e+00 1.43e+00 3.79e+02 6.85e-03 3.45e-03 1.10e+00
154 9.00e+00 1.43e+00 3.78e+02 6.77e-03 3.41e-03 1.10e+00
155 8.99e+00 1.43e+00 3.78e+02 6.70e-03 3.37e-03 1.10e+00
156 8.97e+00 1.43e+00 3.77e+02 6.63e-03 3.33e-03 1.10e+00
157 8.96e+00 1.43e+00 3.76e+02 6.55e-03 3.29e-03 1.10e+00
158 8.94e+00 1.43e+00 3.75e+02 6.48e-03 3.25e-03 1.10e+00
159 8.93e+00 1.44e+00 3.75e+02 6.41e-03 3.21e-03 1.10e+00
160 8.92e+00 1.44e+00 3.74e+02 6.35e-03 3.18e-03 1.10e+00
161 8.90e+00 1.44e+00 3.73e+02 6.28e-03 3.14e-03 1.10e+00
162 8.89e+00 1.44e+00 3.73e+02 6.21e-03 3.10e-03 1.10e+00
163 8.88e+00 1.44e+00 3.72e+02 6.15e-03 3.06e-03 1.10e+00
164 8.86e+00 1.44e+00 3.71e+02 6.08e-03 3.03e-03 1.10e+00
165 8.85e+00 1.44e+00 3.71e+02 6.02e-03 2.99e-03 1.10e+00
166 8.84e+00 1.44e+00 3.70e+02 5.95e-03 2.96e-03 1.10e+00
167 8.82e+00 1.44e+00 3.69e+02 5.89e-03 2.93e-03 1.10e+00
168 8.81e+00 1.44e+00 3.69e+02 5.83e-03 2.90e-03 1.10e+00
169 8.80e+00 1.44e+00 3.68e+02 5.77e-03 2.87e-03 1.10e+00
170 8.79e+00 1.44e+00 3.67e+02 5.70e-03 2.84e-03 1.10e+00
171 8.77e+00 1.44e+00 3.67e+02 5.64e-03 2.81e-03 1.10e+00
172 8.76e+00 1.44e+00 3.66e+02 5.58e-03 2.79e-03 1.10e+00
173 8.75e+00 1.44e+00 3.65e+02 5.52e-03 2.76e-03 1.10e+00
174 8.73e+00 1.44e+00 3.65e+02 5.47e-03 2.73e-03 1.10e+00
175 8.72e+00 1.44e+00 3.64e+02 5.41e-03 2.71e-03 1.10e+00
176 8.71e+00 1.44e+00 3.63e+02 5.36e-03 2.68e-03 1.10e+00
177 8.70e+00 1.44e+00 3.63e+02 5.30e-03 2.64e-03 1.10e+00
178 8.69e+00 1.44e+00 3.62e+02 5.25e-03 2.61e-03 1.10e+00
179 8.68e+00 1.44e+00 3.62e+02 5.20e-03 2.58e-03 1.10e+00
180 8.67e+00 1.44e+00 3.61e+02 5.15e-03 2.55e-03 1.10e+00
181 8.66e+00 1.44e+00 3.61e+02 5.10e-03 2.51e-03 1.10e+00
182 8.65e+00 1.44e+00 3.61e+02 5.05e-03 2.48e-03 1.10e+00
183 8.64e+00 1.44e+00 3.60e+02 5.00e-03 2.46e-03 1.10e+00
----------------------------------------------------------------
Display solve time and reconstruction performance.
print("AddMaskSim wrapped ConvBPDN solve time: %.2fs" % t)
print("Corrupted image PSNR: %5.2f dB" % metric.psnr(img, imgw))
print("Recovered image PSNR: %5.2f dB" % metric.psnr(img, imgr))
AddMaskSim wrapped ConvBPDN solve time: 1.49s
Corrupted image PSNR: 9.10 dB
Recovered image PSNR: 24.56 dB
Display reference, test, and reconstructed image
fig = plot.figure(figsize=(21, 7))
plot.subplot(1, 3, 1)
plot.imview(img, title='Reference image', fig=fig)
plot.subplot(1, 3, 2)
plot.imview(imgw, title='Corrupted image', fig=fig)
plot.subplot(1, 3, 3)
plot.imview(imgr, title='Reconstructed image', fig=fig)
fig.show()
Display lowpass component and sparse representation
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(sl, cmap=plot.cm.Blues, title='Lowpass component', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(np.sum(abs(X).squeeze(), axis=-1), cmap=plot.cm.Blues,
title='Sparse representation', fig=fig)
fig.show()
Plot functional value, residuals, and rho (not available if GPU implementation used).
if ams is not None:
its = ams.getitstat()
fig = plot.figure(figsize=(21, 7))
plot.subplot(1, 3, 1)
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)
plot.subplot(1, 3, 2)
plot.plot(np.vstack((its.PrimalRsdl, its.DualRsdl)).T, ptyp='semilogy',
xlbl='Iterations', ylbl='Residual', lgnd=['Primal', 'Dual'],
fig=fig)
plot.subplot(1, 3, 3)
plot.plot(its.Rho, xlbl='Iterations', ylbl='Penalty Parameter', fig=fig)
fig.show()