import numpy as np
from scipy.optimize import minimize


def func(x):
    return ((x[0]*((0.2292 + x[1])/2)) + ((0.9-x[0])*((x[2] +
    x[1])/2))-0.08)


def func_deriv(x):
    """ Derivative of objective function """
    dfdx0 = (0.2292 - x[2])/2
    dfdx1 = 0.89/2
    dfdx2 = (0.89 - x[0])/2
    return np.array([dfdx0, dfdx1, dfdx2])

cons = ({'type': 'ineq',
         'fun': lambda x: np.array([x[1]-x[2]]),
         'jac': lambda x: np.array([0.0, 1.0, -1.0])},
        {'type': 'ineq',
         'fun': lambda x: np.array([0.2292-x[1]]),
         'jac': lambda x: np.array([0.0, -1.0, 0.0])})

res = minimize(func, [0.43, 0.18, 0.10], jac=func_deriv,
               method='SLSQP',
               options={'disp': True, 'maxiter': 100})