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,  0.09631614,  0.19092151,  0.38165151],
       [ 0.04750988,  0.09502834,  0.19092151,  0.38341252,  0.7664427 ],
       [ 0.09495377,  0.18996269,  0.38165151,  0.7664427,   1.53713523]])

Then we represent the lambda function:

fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2

cons = ({'type': 'ineq', 'fun': lambda x:  x[0] - 2 * x[1] + 2},
         {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
         {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})

bnds = ((0, None), (0, None))

res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,
                constraints=cons)