def test_inv_barrier_function(self, tol=1e-3, tol_const=1e-3):
x0 = np.array([[0.1], [0.1]])
start = time.time()
x_opt = opt.barrier_function(self.p, x0, mode='inv', 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(self.p.eq_const(x_opt[i]))
for i in range(len(x_opt))
])
c_i = np.array([
np.linalg.norm(np.minimum(self.p.ineq_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.plot(np.arange(len(x_opt)),
c_i,
label='Inequality Constraint Norm')
plt.xticks(np.arange(len(x_opt)))
plt.xlabel('Iteration')
plt.legend()
fig.savefig('./fig/ba-pD.eps', format='eps')
print('\nProblem D, Barrier Function')
print('arg min v(x) =\n', x_opt[-1])
print('time =\n', end - start, 's')
def test_log_barrier_function(self, tol=1e-4, tol_const=1e-4):
x0 = np.array([[1], [1]])
x = opt.barrier_function(self.p, x0, mode='log')
def test_inv_barrier_function(self, tol=1e-4, tol_const=1e-4):
x0 = np.array([[1], [1]])
x = opt.barrier_function(self.p, x0, mode='inv')
print(x)
print()
def test_log_barrier_function(self, tol=1e-3, tol_const=1e-3):
x0 = np.array([[0.1], [0.1]])
x = opt.barrier_function(self.p, x0, mode='log')
self.assertTrue(np.linalg.norm(x - self.x_opt) < 1e-3)