def test_13(self):
rho = 1e-1
N = 32
M = 16
K = 8
D = complex_randn(N, N, 1, 1, M)
X = complex_randn(N, N, 1, K, M)
S = np.sum(D*X, axis=4, keepdims=True)
Xop = lambda x: np.sum(X * x, axis=4, keepdims=True)
XHop = lambda x: np.sum(np.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
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 = np.sum(D*X, axis=4, keepdims=True)
Xop = lambda x: np.sum(X * x, axis=4, keepdims=True)
XHop = lambda x: np.sum(np.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
def test_08(self):
rho = 1e-1
N = 32
M = 16
K = 8
D = np.random.randn(N, N, 1, 1, M).astype('complex') + \
np.random.randn(N, N, 1, 1, M).astype('complex') * 1.0j
X = np.random.randn(N, N, 1, K, M).astype('complex') + \
np.random.randn(N, N, 1, K, M).astype('complex') * 1.0j
S = np.sum(D*X, axis=4, keepdims=True)
Xop = lambda x: np.sum(X * x, axis=4, keepdims=True)
XHop = lambda x: np.sum(np.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)
