1 # coding=utf-8
2
3 from scipy
import optimize
4 import numpy as np
5 15 def get(args):
16 a, b, c, d, e, f, g, h =
args
17 fun =
lambda x:a*x[0]**g+b*x[0]*x[1]+c*x[1]**h+d*x[0]+e*x[1] +
f
18 #fun = lambda x:(x[0] - 1) ** h + (x[1] - 2.5) ** h
19 return fun
20
21
22 def con(args):
23 # Equality constraint means that the constraint function result is to be zero whereas inequality means that it is to be non-negative
24 x1min, x1max, x2min, x2max =
args
25 cons = ({
'type':
'ineq',
'fun':
lambda x: x[0] -
x1min},\
26 {
'type':
'ineq',
'fun':
lambda x: -x[0] +
x1max},\
27 {
'type':
'ineq',
'fun':
lambda x: x[1] -
x2min},\
28 {
'type':
'ineq',
'fun':
lambda x: -x[1] +
x2max})
29 return cons
30
31
32 if __name__ ==
"__main__":
33 args = (2, 3, 7, 8, 9, 10, 2, 2)
#a, b, c, d, e, f,g,h
34 args1 = (-1000, 1000, -1000, 1000)
#x1min, x1max, x2min, x2max
35 x0 =
np.asarray((0, 0))
36 fun =
get(args)
37 cons =
con(args1)
38 res = optimize.minimize(fun, x0, method=
'SLSQP', constraints=
cons)
39 print(res.fun)
40 print(res.success)
41 print(res.x)
转载于:https://www.cnblogs.com/shizhenqiang/p/8184395.html
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