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))