SciPy Recipes

How it works...

This is represented in the following code:

from scipy.optimize import minimize, rosen, rosen_der

x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
res = minimize(rosen, x0, method='Nelder-Mead', tol=1e-6)
res.x
array([ 1.,  1.,  1.,  1.,  1.])

res = minimize(rosen, x0, method='BFGS', jac=rosen_der,
...                options={'gtol': 1e-6, 'disp': True})
Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 26
         Function evaluations: 31
         Gradient evaluations: 31
res.x
array([ 1.,  1.,  1.,  1.,  1.])
print(res.message)
Optimization terminated successfully.
res.hess_inv
array([[ 0.00749589,  0.01255155,  0.02396251,  0.04750988,  0.09495377],  # may vary
       [ 0.01255155,  0.02510441,  0.04794055,  0.09502834,  0.18996269],
 [ 0.02396251, 0.04794055, ...