数据优化 [英] Optimization of Data

问题描述

我有一些猜测的输入数据(X0)，我想按以下所述在多个功能中对它们进行优化.

I have some guessed input data (X0) which I want to optimize them in multiple functions as described below.

X0 = [A，B，C，D，E，F，G，H，I，J，K]#每个元素都是浮点值

X0 = [A, B, C, D, E, F, G, H, I, J, K] #each element is a float value

功能:

F1 = A + B + C + D-200 = 0

F1 = A + B + C + D - 200 = 0

F2 = C + D + E-50 = 0

F2 = C + D + E - 50 = 0

F3 = C + D + E + F + G-45 = 0

F3 = C + D + E + F + G - 45 = 0

F4 = E + F + G + H + I + J + K-67 = 0

F4 = E + F + G + H + I + J + K - 67 = 0

F5 = H + I + J + K-64 = 0

F5 = H + I + J + K - 64 = 0

我不确定scipy如何优化多种功能中的输入数据. 我在下面准备了一个脚本；我不确定它是否反应灵敏.

I'm not sure how scipy can optimize the input data in multiple functions. I prepared a script below; I'm not sure if it's responsive.

from scipy.optimize import minimize    

x0 = np.array([1. for i in range(11)])    
def my_function(A, B, C, D, E, F, G, H, I, J, K):
    F1 = A + B + C + D - 200
    F2 = C + D + E - 50
    F3 = C + D + E + F + G - 45
    F4 = E + F + G + H + I + J + K - 67
    F5 = H + I + J + K - 64
    return F1 + F2 +F3 +F4 + F5

cons = ({'type': 'ineq', 'my_function': lambda A, B, C, D:  A + B + C + D - 200},
    {'type': 'ineq', 'my_function': lambda C, D, E: C + D + E - 50},
    {'type': 'ineq', 'my_function': lambda C, D, E, F, G: C + D + E + F + G - 45},
    {'type': 'ineq', 'my_function': lambda E, F, G, H, I, J, K: E + F + G + H + I + J + K - 67},
    {'type': 'ineq', 'my_function': lambda H, I, J, K: H + I + J + K - 64})

res = minimize(my_function, x0, method='BFGS', constraints=cons )

推荐答案

您很亲密.使用类型eq(等于)代替不等式.另外，您的约束应该只接收一个参数，即值的数组，并且您只需访问它们的位置即可.

You are close. Use type eq (equality) instead of inequality. In addition, your constraints should only receive one argument, which is the array of values, and you just access their positions.

检查以下内容:

from scipy.optimize import minimize    

x0 = np.random.random(size=[11])
def my_function(X):
    A, B, C, D, E, F, G, H, I, J, K = X
    F1 = A + B + C + D - 200
    F2 = C + D + E - 50
    F3 = C + D + E + F + G - 45
    F4 = E + F + G + H + I + J + K - 67
    F5 = H + I + J + K - 64
    return F1 + F2 +F3 +F4 + F5

cons = ({'type': 'eq', 'fun': lambda X: X[0] + X[1] + X[2] + X[3] - 200},
    {'type': 'eq', 'fun': lambda X: X[2] + X[3] + X[4] - 50},
    {'type': 'eq', 'fun': lambda X: X[2] + X[3] + X[4] + X[5] + X[6] - 45},
    {'type': 'eq', 'fun': lambda X: X[4] + X[5] + X[6] + X[7] + X[8] + X[9] + X[10] - 67},
    {'type': 'eq', 'fun': lambda X: X[7] + X[8] + X[9] + X[10] - 64})

res = minimize(my_function, x0, constraints=cons)

返回

success: True
x: array([79.27328348, 78.72671652, 21.16500123, 20.83499877,  8.        ,
       -2.5794818 , -2.4205182 , 15.7738023 , 16.59847106, 15.92703282,
       15.70069382])

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