3变量优化器使用SLSQP提供运行警告

Question

这是我在python 3.6中的当前代码，使用SLSQP方法优化受约束的3变量优化器，这是我优化到椭圆分布。

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))-0.16)


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])},
        {'type': 'ineq',
         'fun': lambda x: np.array(0.89-x[0]),
         'jac': lambda x: np.array([-1.0, 0.0, 0.0])})

res = minimize(func, [0.45, 0.20, 0.10], jac=func_deriv, constraints=cons
               method='SLSQP',
               options={'disp': True, 'maxiter': 100})

我不断得到的错误是RunTimeWarning invalid double scalar 我不知道为什么会发生这种情况

Iteration limit exceeded    (Exit mode 9)
            Current function value: nan
            Iterations: 101
            Function evaluations: 881
            Gradient evaluations: 101
C:/Users/bob19/untitled9.py:13: RuntimeWarning: overflow encountered in 
double_scalars
  return ((x[0]*((0.2292 + x[1])/2)) + ((0.9-x[0])*((x[2] + x[1])/2))-0.16)
C:/Users/bob19/untitled9.py:13: RuntimeWarning: invalid value encountered 
in double_scalars
 return ((x[0]*((0.2292 + x[1])/2)) + ((0.9-x[0])*((x[2] + x[1])/2))-0.16)

非常感谢任何帮助。

上面的错误我不再得到，而是我得到'不平等约束不兼容'

runfile('C:/Users/Robin19/opimizer v2.py', wdir='C:/Users/Robin19')
Inequality constraints incompatible    (Exit mode 4)
            Current function value: -2.77024369372e+24
            Iterations: 8
            Function evaluations: 8
            Gradient evaluations: 8

fun: -2.7702436937183577e+24
     jac: array([  1.49413376e+12,   4.45000000e-01,   9.27040055e+11,
        0.00000000e+00])
 message: 'Inequality constraints incompatible'
    nfev: 8
     nit: 8
    njev: 8
  status: 4
 success: False
       x: array([ -1.85408011e+12,  -3.00597522e+12,  -2.98826753e+12])