非线性规划带约束-scipy.optimize.minimize

Posted 2020-10-19 null

 1 # coding=utf-8
 2 
 3 from scipy import optimize
 4 import numpy as np
 5 
 6 
 7  8      9     10     11 
12 
13 
14 
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)