python 非线性规划方式(scipy.optimize.minimize)

# coding=utf-8
from scipy.optimize import minimize
import numpy as np
 
#demo 1
#计算 1/x+x 的最小值
 def fun(args):
  a=args
  v=lambda x:a/x[0] +x[0]
  return v
 
 if __name__ == "__main__":
  args = (1) #a
  x0 = np.asarray((2)) # 初始猜测值
  res = minimize(fun(args), x0, method='SLSQP')
  print(res.fun)
  print(res.success)
  print(res.x)

# coding=utf-8
from scipy.optimize import minimize
import numpy as np
 
# demo 2
#计算 (2+x1)/(1+x2) - 3*x1+4*x3 的最小值 x1,x2,x3的范围都在0.1到0.9 之间
def fun(args):
 a,b,c,d=args
 v=lambda x: (a+x[0])/(b+x[1]) -c*x[0]+d*x[2]
 return v
def con(args):
 # 约束条件 分为eq 和ineq
 #eq表示 函数结果等于0 ； ineq 表示 表达式大于等于0 
 x1min, x1max, x2min, x2max,x3min,x3max = args
 cons = ({'type': 'ineq', 'fun': lambda x: x[0] - x1min},    {'type': 'ineq', 'fun': lambda x: -x[0] + x1max},    {'type': 'ineq', 'fun': lambda x: x[1] - x2min},    {'type': 'ineq', 'fun': lambda x: -x[1] + x2max},   {'type': 'ineq', 'fun': lambda x: x[2] - x3min},    {'type': 'ineq', 'fun': lambda x: -x[2] + x3max})
 return cons
 
if __name__ == "__main__":
 #定义常量值
 args = (2,1,3,4) #a,b,c,d
 #设置参数范围/约束条件
 args1 = (0.1,0.9,0.1, 0.9,0.1,0.9) #x1min, x1max, x2min, x2max
 cons = con(args1)
 #设置初始猜测值 
 x0 = np.asarray((0.5,0.5,0.5))
 
 res = minimize(fun(args), x0, method='SLSQP',constraints=cons)
 print(res.fun)
 print(res.success)
 print(res.x)