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)