import numpy as np
from scipy import optimize
def logL(a):
return -(10*np.log(a)-7.7*a) # for minimization
optimize.minimize(logL, 2)
fun: 7.38635235865695 hess_inv: array([[0.16802217]]) jac: array([3.57627869e-06]) message: 'Optimization terminated successfully.' nfev: 14 nit: 6 njev: 7 status: 0 success: True x: array([1.29870189])