def run_with_small_matrix(self):
a = np.random.normal(0, 1, (10, 10))
A = np.dot(a, a.T)
C = np.random.uniform(0.01, 1, 10)
precon = lambda x: x * C
matvec = lambda x: np.dot(A, x)
b = np.random.normal(0, 1, 10)
return get_subspace(matvec, b, 5, precon=precon)
def run_with_small_matrix(self):
a = np.random.normal(0, 1, (10, 10))
A = np.dot(a, a.T)
C = np.random.uniform(0.01, 1, 10)
precon = lambda x: x * C
matvec = lambda x: np.dot(A, x)
b = np.random.normal(0, 1, 10)
return get_subspace(matvec, b, 5, precon=precon)
def setUp(self):
a = np.random.normal(0, 1, (10, 10))
A = np.dot(a, a.T)
matvec = lambda x: np.dot(A, x)
b = np.random.normal(0, 1, 10)
P, H = get_subspace(matvec, b, 5)
L = np.linalg.cholesky(H)
self.P = P
self.H = H
self.L = L
def setUp(self):
a = np.random.normal(0, 1, (10, 10))
A = np.dot(a, a.T)
matvec = lambda x: np.dot(A, x)
b = np.random.normal(0, 1, 10)
P, H = get_subspace(matvec, b, 5)
L = np.linalg.cholesky(H)
self.P = P
self.H = H
self.L = L
