def test_lag_new(self):
P = np.array([[1, 0], [0, 2]])
v = lambda x: 0.5 * x.T @ P @ x
c = [lambda x: x[0, 0]**2 + x[1, 0]**2 - 25]
p = opt.Problem(v, eq_const=c)
x0 = np.array([[1], [1]])
x = opt.lagrange_newton(p, x0, tol=1e-2)
print(x)
def test_lag_new(self):
x0 = np.array([[0.7], [0.7]])
start = time.time()
x_opt = opt.lagrange_newton(self.p, x0, tol=1e-4, hist=True)
end = time.time()
g = np.array(
[np.linalg.norm(self.p.grad(x_opt[i])) for i in range(len(x_opt))])
c_e = np.array([
np.linalg.norm(np.minimum(self.p.eq_const(x_opt[i]), 0))
for i in range(len(x_opt))
])
fig = plt.figure()
plt.plot(np.arange(len(x_opt)), g, label='Gradient Norm')
plt.plot(np.arange(len(x_opt)), c_e, label='Equality Constraint Norm')
plt.xticks(np.arange(len(x_opt)))
plt.xlabel('Iteration')
plt.legend()
fig.savefig('./fig/ln-pEEq.eps', format='eps')
print('\nProblem E-Eq, Lagrange-Newton')
print('arg min v(x) =\n', x_opt[-1])
print('time =\n', end - start, 's')
def test_lag_new(self):
x0 = np.array([[0.7], [0.7]])
x_opt = opt.lagrange_newton(self.p, x0, tol=1e-4)
print(x_opt)
def test_lag_new(self):
x0 = np.array([[0.7], [0.7]])
start = time.time()
x_opt = opt.lagrange_newton(self.p, x0, tol=1e-4)
end = time.time()
print(x_opt)