def setUp(self):
# Initial Values
a_0 = 100
a_1 = 0.5
rho = 0.9
sigma_d = 0.05
beta = 0.95
c = 2
gamma = 50.0
theta = 0.002
ac = (a_0 - c) / 2.0
R = np.array([[0, ac, 0], [ac, -a_1, 0.5], [0., 0.5, 0]])
R = -R
Q = gamma / 2
Q_pf = 0.
A = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., rho]])
B = np.array([[0.], [1.], [0.]])
B_pf = np.zeros((3, 1))
C = np.array([[0.], [0.], [sigma_d]])
# the *_pf endings refer to an example with pure forecasting
# (see p171 in Robustness)
self.rblq_test = RBLQ(Q, R, A, B, C, beta, theta)
self.rblq_test_pf = RBLQ(Q_pf, R, A, B_pf, C, beta, theta)
self.lq_test = LQ(Q, R, A, B, C, beta)
self.methods = ['doubling', 'qz']
def setUp(self):
# Initial Values
a_0 = 100
a_1 = 0.5
rho = 0.9
sigma_d = 0.05
beta = 0.95
c = 2
gamma = 50.0
theta = 0.002
ac = (a_0 - c) / 2.0
R = np.array([[0, ac, 0],
[ac, -a_1, 0.5],
[0., 0.5, 0]])
R = -R
Q = gamma / 2
A = np.array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., rho]])
B = np.array([[0.],
[1.],
[0.]])
C = np.array([[0.],
[0.],
[sigma_d]])
self.rblq_test = RBLQ(Q, R, A, B, C, beta, theta)
self.lq_test = LQ(Q, R, A, B, C, beta)
self.Fr, self.Kr, self.Pr = self.rblq_test.robust_rule()
