def test(self):
rhs = self.rhs
Ainv = BicgJacobi(self.A, symmetric=True)
solb = Ainv*rhs
for i in range(3):
err = np.linalg.norm(
self.A*solb[:, i] - rhs[:, i]) / np.linalg.norm(rhs[:, i])
self.assertLess(err, TOL)
Ainv.clean()
def test(self):
rhs = self.rhs
Ainv = BicgJacobi(self.A, symmetric=True)
solb = Ainv * rhs
for i in range(3):
err = np.linalg.norm(self.A * solb[:, i] -
rhs[:, i]) / np.linalg.norm(rhs[:, i])
self.assertLess(err, TOL)
Ainv.clean()
def test_T(self):
rhs = self.rhs
Ainv = BicgJacobi(self.A, symmetric=True)
Ainv.maxIter = 2000
AinvT = Ainv.T
solb = AinvT*rhs
for i in range(3):
err = np.linalg.norm(
self.A.T*solb[:, i] - rhs[:, i]) / np.linalg.norm(rhs[:, i])
self.assertLess(err, TOL)
Ainv.clean()
def test_T(self):
rhs = self.rhs
Ainv = BicgJacobi(self.A, symmetric=True)
Ainv.maxIter = 2000
AinvT = Ainv.T
solb = AinvT * rhs
for i in range(3):
err = np.linalg.norm(self.A.T * solb[:, i] -
rhs[:, i]) / np.linalg.norm(rhs[:, i])
self.assertLess(err, TOL)
Ainv.clean()