def test_10(self):
N = 64
M = 4
Nd = 8
D = cp.random.randn(Nd, Nd, M)
X0 = cp.zeros((N, N, M))
xr = cp.random.randn(N, N, M)
xp = cp.abs(xr) > 3
X0[xp] = cp.random.randn(X0[xp].size)
S = cp.sum(fftconv(D, X0, axes=(0, 1)), axis=2)
lmbda = 1e-4
rho = 1e-1
opt = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': 500,
'RelStopTol': 1e-3,
'rho': rho,
'AutoRho': {
'Enabled': False
}
})
b = cbpdn.ConvBPDN(D, S, lmbda, opt)
b.solve()
X1 = b.Y.squeeze()
assert rrs(X0, X1) < 5e-5
Sr = b.reconstruct().squeeze()
assert rrs(S, Sr) < 1e-4
def test_11(self):
N = 63
M = 4
Nd = 8
D = cp.random.randn(Nd, Nd, M)
X0 = cp.zeros((N, N, M))
xr = cp.random.randn(N, N, M)
xp = cp.abs(xr) > 3
X0[xp] = cp.random.randn(X0[xp].size)
S = cp.sum(ifftn(
fftn(D, (N, N), (0, 1)) * fftn(X0, None, (0, 1)), None,
(0, 1)).real,
axis=2)
lmbda = 1e-2
L = 1e3
opt = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': 2000,
'RelStopTol': 1e-9,
'L': L,
'BackTrack': {
'Enabled': False
}
})
b = cbpdn.ConvBPDN(D, S, lmbda, opt)
b.solve()
X1 = b.X.squeeze()
assert rrs(X0, X1) < 5e-4
Sr = b.reconstruct().squeeze()
assert rrs(S, Sr) < 2e-4
def test_10(self):
N = 64
M = 4
Nd = 8
D = cp.random.randn(Nd, Nd, M)
X0 = cp.zeros((N, N, M))
xr = cp.random.randn(N, N, M)
xp = cp.abs(xr) > 3
X0[xp] = cp.random.randn(X0[xp].size)
S = cp.sum(sl.fftconv(D, X0), axis=2)
lmbda = 1e-4
rho = 1e-1
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 500,
'RelStopTol': 1e-3, 'rho': rho,
'AutoRho': {'Enabled': False}})
b = cbpdn.ConvBPDN(D, S, lmbda, opt)
b.solve()
X1 = b.Y.squeeze()
assert sl.rrs(X0, X1) < 5e-5
Sr = b.reconstruct().squeeze()
assert sl.rrs(S, Sr) < 1e-4
def test_09(self):
rho = 1e-1
N = 32
M = 16
K = 8
D = util.complex_randn(N, N, 1, 1, M)
X = util.complex_randn(N, N, 1, K, M)
S = cp.sum(D * X, axis=4, keepdims=True)
Z = (D.conj()*cp.sum(D*X, axis=4, keepdims=True) + \
rho*X - D.conj()*S) / rho
Xslv = linalg.solvedbi_sm(D, rho, D.conj() * S + rho * Z)
assert (linalg.rrs(
D.conj() * cp.sum(D * Xslv, axis=4, keepdims=True) + rho * Xslv,
D.conj() * S + rho * Z) < 1e-11)
def test_08(self):
rho = 1e-1
N = 128
M = 64
K = 32
D = cp.random.randn(N, M)
X = cp.random.randn(M, K)
S = D.dot(X)
Z = (D.dot(X).dot(X.T) + rho * D - S.dot(X.T)) / rho
c, lwr = linalg.cho_factor(X, rho)
Dslv = linalg.cho_solve_AATI(X, rho, S.dot(X.T) + rho * Z, c, lwr)
assert (linalg.rrs(
Dslv.dot(X).dot(X.T) + rho * Dslv,
S.dot(X.T) + rho * Z) < 1e-11)
def test_06(self):
rho = 1e-1
N = 128
M = 64
K = 32
D = cp.random.randn(N, M)
X = cp.random.randn(M, K)
S = D.dot(X)
Z = (D.T.dot(D).dot(X) + rho * X - D.T.dot(S)) / rho
c, lwr = linalg.cho_factor(D, rho)
Xslv = linalg.cho_solve_ATAI(D, rho, D.T.dot(S) + rho * Z, c, lwr)
assert (linalg.rrs(
D.T.dot(D).dot(Xslv) + rho * Xslv,
D.T.dot(S) + rho * Z) < 1e-14)
def test_11(self):
N = 63
M = 4
Nd = 8
D = cp.random.randn(Nd, Nd, M)
X0 = cp.zeros((N, N, M))
xr = cp.random.randn(N, N, M)
xp = cp.abs(xr) > 3
X0[xp] = cp.random.randn(X0[xp].size)
S = cp.sum(sl.ifftn(sl.fftn(D, (N, N), (0, 1)) *
sl.fftn(X0, None, (0, 1)), None, (0, 1)).real,
axis=2)
lmbda = 1e-2
L = 1e3
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 2000,
'RelStopTol': 1e-9, 'L': L,
'BackTrack': {'Enabled': False}})
b = cbpdn.ConvBPDN(D, S, lmbda, opt)
b.solve()
X1 = b.X.squeeze()
assert sl.rrs(X0, X1) < 5e-4
Sr = b.reconstruct().squeeze()
assert sl.rrs(S, Sr) < 2e-4
def test_10(self):
N = 32
M = 16
K = 8
D = util.complex_randn(N, N, 1, 1, M)
X = util.complex_randn(N, N, 1, K, M)
S = cp.sum(D * X, axis=4, keepdims=True)
d = 1e-1 * (cp.random.randn(N, N, 1, 1, M).astype('complex') +
cp.random.randn(N, N, 1, 1, M).astype('complex') * 1.0j)
Z = (D.conj() * cp.sum(D * X, axis=4, keepdims=True) + d * X -
D.conj() * S) / d
Xslv = linalg.solvedbd_sm(D, d, D.conj() * S + d * Z)
assert (linalg.rrs(
D.conj() * cp.sum(D * Xslv, axis=4, keepdims=True) + d * Xslv,
D.conj() * S + d * Z) < 1e-11)
def test_13(self):
rho = 1e-1
N = 32
M = 16
K = 8
D = util.complex_randn(N, N, 1, 1, M)
X = util.complex_randn(N, N, 1, K, M)
S = cp.sum(D * X, axis=4, keepdims=True)
Xop = lambda x: cp.sum(X * x, axis=4, keepdims=True)
XHop = lambda x: cp.sum(cp.conj(X) * x, axis=3, keepdims=True)
Z = (XHop(Xop(D)) + rho * D - XHop(S)) / rho
Dslv = linalg.solvemdbi_rsm(X, rho, XHop(S) + rho * Z, 3)
assert linalg.rrs(XHop(Xop(Dslv)) + rho * Dslv,
XHop(S) + rho * Z) < 1e-11
