简单的线性规划-scipy

 

根据描述，我们用线性规划带约束来求解问题

# coding=utf-8

from scipy.optimize import linprog
import numpy as np


def maxGain(args):

    xg,yg,naifenx,naifeny,kaofeix,kaofeiy,sukx,suky,naifenmax,kaofeimax,sukmax = args
    # c = np.array([0.7, 1.2])
    # A = np.array([[9, 4], [4, 5], [3, 10]])
    # b = np.array([3600, 2000, 3000])
    c = np.array([xg, yg])
    A = np.array([[naifenx, naifeny], [kaofeix, kaofeiy], [sukx, suky]])
    b = np.array([naifenmax, kaofeimax, sukmax])
    x0_bounds = (0, None)
    x1_bounds = (0, None)

    res = linprog(-c, A_ub=A,b_ub=b,\
                bounds=(x0_bounds,x1_bounds),\
                options={"disp": True}
               )
    return res

if __name__ == "__main__":

    args = (0.7,1.2,9,4,4,5,3,10,3600,2000,3000) #11个参数，，，，，，，，，
    res = maxGain(args)
    print(res.x)
    #print(res)
    print(-res.fun)

可看注释掉的代码，根据图片显示的位置，进行阅读。