def test_cg_sparse(self):
# Create sparse problem.
num_unknowns = 100
A = self._create_sparse_hpd_matrix( num_unknowns )
rhs = np.ones( (num_unknowns,1) )
x0 = np.zeros( (num_unknowns,1) )
# Solve using CG.
tol = 1.0e-11
out = nm.cg(A, rhs, x0, tol=tol)
# Make sure the method converged.
#self.assertEqual(info, 0)
# Check the residual.
res = rhs - A * out['xk']
self.assertAlmostEqual( np.linalg.norm(res)/np.linalg.norm(rhs), 0.0, delta=tol )
def test_cg_dense(self):
# Create dense problem.
num_unknowns = 5
A = self._create_hpd_matrix( num_unknowns )
rhs = np.ones( (num_unknowns,1) )
x0 = np.zeros( (num_unknowns,1) )
# Solve using CG.
tol = 1.0e-13
out = nm.cg( A, rhs, x0, tol=tol )
# Make sure the method converged.
self.assertEqual(out['info'], 0)
# Check the residual.
res = rhs - np.dot(A, out['xk'])
self.assertAlmostEqual( np.linalg.norm(res)/np.linalg.norm(rhs), 0.0, delta=tol )
def test_cg_sparse(self):
# Create sparse problem.
num_unknowns = 100
A = self._create_sparse_hpd_matrix(num_unknowns)
rhs = np.ones((num_unknowns, 1))
x0 = np.zeros((num_unknowns, 1))
# Solve using CG.
tol = 1.0e-11
out = nm.cg(A, rhs, x0, tol=tol)
# Make sure the method converged.
#self.assertEqual(info, 0)
# Check the residual.
res = rhs - A * out['xk']
self.assertAlmostEqual(np.linalg.norm(res) / np.linalg.norm(rhs),
0.0,
delta=tol)
def test_cg_dense(self):
# Create dense problem.
num_unknowns = 5
A = self._create_hpd_matrix(num_unknowns)
rhs = np.ones((num_unknowns, 1))
x0 = np.zeros((num_unknowns, 1))
# Solve using CG.
tol = 1.0e-13
out = nm.cg(A, rhs, x0, tol=tol)
# Make sure the method converged.
self.assertEqual(out['info'], 0)
# Check the residual.
res = rhs - np.dot(A, out['xk'])
self.assertAlmostEqual(np.linalg.norm(res) / np.linalg.norm(rhs),
0.0,
delta=tol)
