from newton_interior_points import mPI_Newton
problem = 'adlittle'
solution = 2.2549496316E+05
print('---------------------------------------------',
'Problema {}:'.format(problem),
'---------------------------------------------',
sep='\n')
problem_path = 'test_problems/{}.mat'.format(problem)
file = sio.loadmat(problem_path)
A = file['A']
b = file['b'].flatten()
c = file['c'].flatten()
A = scipy.sparse.csr_matrix.todense(A)
b = b.reshape(b.shape[0], 1)
c = c.reshape(c.shape[0], 1)
ans = mPI_Newton(A, b, c, warm_initial_solution=False
) #See also with warm_initial_solution = True :)
print('- - - - - - - - - - - - - - - - - - - - - - -',
'Nuestra solución: {} '.format(ans['óptimo']),
'Solución oficial: {}'.format(solution),
'Número de iteraciones: {}'.format(ans['iter']),
'Valor de ||diag(x)z||_inf: {}'.format(
norm(np.dot(np.diag(ans['x'].T[0]), ans['z']), np.inf)),
'- - - - - - - - - - - - - - - - - - - - - - -\n',
sep='\n')
c = np.concatenate((c, np.zeros(m)))
return (A, b, c)
m = [10, 12, 14, 16]
for i in m:
A, b, c = genera_klee_minty(i)
b = b.reshape(b.shape[0], 1)
c = c.reshape(c.shape[0], 1)
ans = mPI_Newton(A,
b,
c,
warm_initial_solution=True,
show_iteration_and_norm=False)
print('-----------------------------------------------------------------')
print('Klee-Minty con m =', i, "(iniciando con una 'warm solution')")
print('Nuestra solución:', ans['óptimo'])
print('Solución teorica:', -((2**i) - 1))
print('Número de iteraciones:', ans['iter'])
ans = mPI_Newton(A, b, c, show_iteration_and_norm=False)
print('\nKlee-Minty con m =', i, "(sin iniciar con una 'warm solution')")
print('Nuestra solución:', ans['óptimo'])
print('Solución teorica:', -((2**i) - 1))
print('Número de iteraciones:', ans['iter'])