def runTest(self):
n = 3
g = np.array([1.0, 0.0, 1.0])
H = np.array([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 2.0]])
Delta = 5.0 / 12.0
hess = Hessian(n, vals=H)
xopt = np.zeros((n, ))
sl = -1e20 * np.ones((n, ))
su = 1e20 * np.ones((n, ))
d, gnew, crvmin = trsbox(xopt, g, hess, sl, su, Delta)
true_d = np.array([-1.0 / 3.0, 0.0, -0.25])
est_min = model_value(g, hess, d)
true_min = model_value(g, hess, true_d)
# Hope to get actual correct answer
# self.assertTrue(np.all(d == true_d), 'Wrong answer')
# self.assertAlmostEqual(est_min, true_min, 'Wrong min value')
s_cauchy, red_cauchy, crvmin_cauchy = cauchy_pt(g, hess, Delta)
self.assertTrue(est_min <= red_cauchy, 'Cauchy reduction not achieved')
self.assertTrue(np.all(gnew == g + hess.vec_mul(d)), 'Wrong gnew')
self.assertAlmostEqual(crvmin, 0.0, 'Wrong crvmin')
def runTest(self):
n = 3
g = np.array([1.0, 0.0, 1.0])
H = np.array([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 2.0]])
Delta = 5.0 / 12.0
hess = Hessian(n, vals=H)
xopt = np.zeros((n, ))
sl = xopt + np.array([-0.3, -0.01, -0.1])
su = xopt + np.array([10.0, 1.0, 10.0])
d, gnew, crvmin = trsbox(xopt, g, hess, sl, su, Delta)
true_d = np.array([-1.0 / 3.0, 0.0, -0.25])
est_min = model_value(g, hess, d)
true_min = model_value(g, hess, true_d)
# Hope to get actual correct answer
# self.assertTrue(np.all(d == true_d), 'Wrong answer')
# self.assertAlmostEqual(est_min, true_min, 'Wrong min value')
s_cauchy, red_cauchy, crvmin_cauchy = cauchy_pt_box(
g, hess, Delta, sl - xopt, su - xopt)
self.assertTrue(est_min <= red_cauchy, 'Cauchy reduction not achieved')
self.assertTrue(
np.max(np.abs(gnew - g - hess.vec_mul(d))) < 1e-10, 'Wrong gnew')
print(crvmin)
self.assertAlmostEqual(crvmin, -1.0, 'Wrong crvmin')
def runTest(self):
n = 3
g = np.array([1.0, 0.0, 1.0])
H = np.array([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 2.0]])
Delta = 2.0
hess = Hessian(n, vals=H)
xopt = np.ones((n, )) # trying nonzero (since bounds inactive)
sl = xopt + np.array([-0.5, -10.0, -10.0])
su = xopt + np.array([10.0, 10.0, 10.0])
d, gnew, crvmin = trsbox(xopt, g, hess, sl, su, Delta)
true_d = np.array([-1.0, 0.0, -0.5])
est_min = model_value(g, hess, d)
true_min = model_value(g, hess, true_d)
# Hope to get actual correct answer for internal minimum?
# self.assertTrue(np.all(d == true_d), 'Wrong answer')
# self.assertAlmostEqual(est_min, true_min, 'Wrong min value')
s_cauchy, red_cauchy, crvmin_cauchy = cauchy_pt_box(
g, hess, Delta, sl - xopt, su - xopt)
# print(s_cauchy)
# print(d)
self.assertTrue(est_min <= red_cauchy, 'Cauchy reduction not achieved')
self.assertTrue(np.all(gnew == g + hess.vec_mul(d)), 'Wrong gnew')
print(crvmin)
self.assertAlmostEqual(crvmin, -1.0, 'Wrong crvmin')