尝试线性模型的acipy模块

盘荣博/线性规划/optimiaze模块/scipy函数.py

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+from scipy.optimize import linprog
+import time
+import numpy as np
+'''
+目标函数：
+min z= -x1 + 4 x2 
+
+约束条件：
+-3x1 + x2 < = 6
+x1 + 2x2 <= 4 
+x2 >= -3 
+
+'''
+begin = time.time()
+c=[-1,4]
+A=[[-3,1],[1,2]]
+b=[[6],[4]]
+bounds=((None , None),(-3,None))
+res= linprog(c,A,b,None,None,bounds)
+end= time.time()
+print("结果为：",res.fun)
+print("x的值位",res.x," ",type(res.x))
+print(end-begin,"\n",'----------------------------------------')
+'''
+线性规划问题
+ max z = x1  - 2x2 - 3x3 
+ 约束条件
+ -2x1 + x2 + x3 <= 9
+ -3x1 + x2 + 2x3 >= 4 
+ 4x1 -2x2 -x3 = -6 
+ x1 >= -10 ,x2 >=0
+'''
+'''
+scipy标准形式：
+min w= -x1 + 2x2  + 3x3 
+
+约束： 
+-2x1 + x2 + x3 <= 9
+3x1 - x2 - 2x3 <= -4
+'''
+c= (-np.array([1,-2,-3])).tolist()
+A=[[-2,1,1],[-3,1,2]]
+A[1]= (-np.array(A)[1]).tolist()
+b= [[9],[4]]
+b[1]=(-np.array(b[1])).tolist()
+Aeq=[[4,-2,-1]]
+beq =[[-6]]
+LB =[-10,0,None]
+UB = [None]*len(c)
+bounds= tuple(zip(LB,UB))
+res= linprog(c,A,b,Aeq,beq,bounds)
+end= time.time()
+print("结果为：",res.fun)
+print("x的值位",res.x," ",type(res.x))
+print(end-begin,"\n",'----------------------------------------')
+print(res)

盘荣博/线性规划/optimiaze模块/例 5.3.py

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+import time
+
+import numpy as np
+from scipy.optimize import linprog
+start = time.time()
+c = [-110,-120,-130,-110,-115,150]
+c = (-np.array(c)).tolist()
+A=[[1,1,0,0,0,0],
+   [0,0,1,1,1,0],
+   [8.8,6.1,2.0,4.2,5.0,-6],
+   [8.8,6.1,2.0,4.2,5.0,-3]
+   ]
+A[3]=(-np.array(A[3])).tolist()
+b=[[200],[250],[0],[0]]
+Aeq =[[1,1,1,1,1,-1]]
+beq =  [[0]]
+LB= [0]*len(c)
+UB= [None]*len(c)
+bounds= tuple(zip(LB,UB))
+res = linprog(c,A,b,Aeq,beq,bounds)
+end = time.time()
+print("最优解：\n",res.x)
+print("目标函数最小值：\n",-res.fun)