# 依赖: scipy >= 1.9
import numpy as np
from scipy.optimize import LinearConstraint, Bounds, milp

# 最大化 -> 目标取负；下面是一个 0-1 背包式问题
c = np.array([-8.0, -11.0])
A = np.array([[5.0, 7.0], [1.0, 0.0], [0.0, 1.0]])
con = LinearConstraint(A, -np.inf, np.array([40.0, 5.0, 4.0]))

res = milp(c=c, constraints=[con], integrality=np.ones(2), bounds=Bounds(0, 1))
assert res.success, res.message
print(f'最优值={-res.fun:.4f} 最优解={np.round(res.x)}')