如何处理scipy最小化ValueError：没有足够的值要解压预期4，得到3？

问题描述

I am trying to minimize the following function by use of the scipy library:
from scipy.optimize import minimize

def constraint1(bet):
    a,b = bet
    return 100 - a + b

con1 = {'type': 'ineq','fun': constraint1}
cons = [con1]
b0,b1 = (0,100),(0,100)   
bnds = (b0,b1)

def f(bet,sign = -1,*args):
    d0,d1,p0,p1 = args
    a,b = bet
    wins0 = a * (d0-1)
    wins1 = b * (d1-1)
    loss0 = b
    loss1 = a
    log0 = np.log(bank + wins0 - loss0)
    log1 = np.log(bank + wins1 - loss1)
    
    objective = (log0 * p0 + log1 * p1)
    return sign * objective

bet = [0,0]
minimize(f,bet,args = (1,2,3,4,),method = 'trust-constr',bounds = bnds,constraints = cons)

但这会导致ValueError:

 d0,p1 = args (Think this is where the error occurs)
 ValueError: not enough values to unpack (expected 4,got 3)

试图省略,，使其看起来像这样：(1,4)，但这也不起作用。

有什么用！

解决方法

您无法minimize使用可选参数。该功能必须如下所示：

fun(x,*args)

没有位置放置可选参数。因此，您要做的就是调用您的函数，以显式提供-1作为args之一：

minimize(f,bet,args = (-1,1,2,3,4,),method = 'trust-constr',bounds = bnds,constraints = cons)

这是documentation的链接。