共轭梯度算法求最小值-scipy

 # coding=utf-8

 #共轭梯度算法求最小值
 import numpy as np

 from scipy import optimize

 def f(x, *args):
     u, v = x
     a, b, c, d, e, f,g,h = args
     return a*u**g+ b*u*v + c*v**h + d*u + e*v + f

 def gradf(x, *args):
     u, v = x
     a, b, c, d, e, f,g,h = args
     gu = g*a*u + b*v + d     # u-component of the gradient
     gv = b*u +h*c*v + e     # v-component of the gradient
     return np.asarray((gu, gv))

 if __name__ == "__main__":
     args = (2, 3, 7, 8, 9, 10, 2, 2)  # parameter values
     x0 = np.asarray((0, 0))  # Initial guess.
     res1 = optimize.fmin_cg(f, x0, fprime=gradf, args=args)
     #res2 = optimize.fmin(f,x0,args)
     print(res1)
     #print(res2)
     print(f(res1,*args))