"""
from demos.setup import np, plt, tic, toc
from numpy.linalg import norm
from compecon import NLP
''' Set up the problem '''
def f(x):
fval = np.exp(-x) - 1
fjac = -np.exp(-x)
return fval, fjac
problem = NLP(f, all_x=True)
''' Randomly generate starting point '''
problem.x0 = 10 * np.random.randn(1)
''' Compute root using Newton method '''
t0 = tic()
x1 = problem.newton()
t1 = 100 * toc(t0)
n1, x_newton = problem.fnorm, problem.x_sequence
''' Compute root using Broyden method '''
t0 = tic()
x2 = problem.broyden()
t2 = 100 * toc(t0)
n2, x_broyden = problem.fnorm, problem.x_sequence
"""
from demos.setup import np, plt, tic, toc
from numpy.linalg import norm
from compecon import NLP
''' Set up the problem '''
def f(x):
fval = np.exp(-x) - 1
fjac = -np.exp(-x)
return fval, fjac
problem = NLP(f, all_x=True)
''' Randomly generate starting point '''
problem.x0 = 10 * np.random.randn(1)
''' Compute root using Newton method '''
t0 = tic()
x1 = problem.newton()
t1 = 100 * toc(t0)
n1, x_newton = problem.fnorm, problem.x_sequence
''' Compute root using Broyden method '''
t0 = tic()
x2 = problem.broyden()
t2 = 100 * toc(t0)
n2, x_broyden = problem.fnorm, problem.x_sequence
''' Print results '''
print('Hundredths of seconds required to compute root of exp(-x)-1,')
print('via Newton and Broyden methods, starting at x = %4.2f.' % problem.x0)
print('\nMethod Time Norm of f Final x')
print('Newton %8.2f %8.0e %5.2f' % (t1, n1, x1))
from compecon import NLP
''' Set up the problem '''
def f(x):
fval = [200 * x[0] * (x[1] - x[0] ** 2) + 1 - x[0], 100 * (x[0] ** 2 - x[1])]
fjac = [[200 * (x[1] - x[0] ** 2) - 400 * x[0] ** 2 - 1, 200 * x[0]],
[200 * x[0], -100]]
return np.array(fval), np.array(fjac)
problem = NLP(f)
''' Randomly generate starting point '''
problem.x0 = np.random.randn(2)
''' Compute root using Newton method '''
t0 = tic()
x1 = problem.newton()
t1 = 100 * toc(t0)
n1 = problem.fnorm
'''Compute root using Broyden method '''
t0 = tic()
x2 = problem.broyden()
t2 = 100 * toc(t0)
n2 = problem.fnorm
''' Print results '''