如何处理scipy最小化值错误：没有足够的值来解包（预期为4，得到3）？

Question

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)

然而，这会导致ValueError:

 d0, d1, p0, p1 = args (Think this is where the error occurs)
 ValueError: not enough values to unpack (expected 4, got 3)

尝试省略,，因此看起来是：(1,2,3,4)，但这也不起作用

什么都行

Answer 1

不能使用可选参数执行minimize函数。函数必须如下所示：

fun(x, *args)

没有地方可供选择的参数。因此，您要做的是调用函数，显式地提供-1作为args之一：

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

这是到documentation的链接