5.梯度寻优

迭代法求方程组的解：

import numpy as np
from numpy import *
from common_libs import *
import matplotlib.pyplot as plt

#消元法求原方程组的解
A = mat([[8,-3,2],[4,11,-1],[6,3,12]])
b = mat([20,33,36])
result = linalg.solve(A,b.T)
print(result)

#迭代法求方程组的解
error = 1.0e-6
steps = 100
n=3
xk = zeros((n,1))
errorlist=[]
B0 = mat([[0,3/8,-1/4],[-4/11,0,1/11],[-1/2,-1/4,0]])
f = mat([5/2,3,3]).T
for k in range(steps):
    xk_1 = xk
    xk = B0*xk + f
    errorlist.append(linalg.norm(xk - xk_1))
    if errorlist[-1] < error:
        print(k+1)
        break

print(xk)

matpts = zeros((2,k+1))
matpts[0] = linspace(1,k+1,k+1)
matpts[1] = array(errorlist)
drawScatter(plt,matpts)
plt.show()

原文地址：https://www.cnblogs.com/xiaochi/p/11101722.html