时间:2021-06-08 07:47:58 | 栏目:Python代码 | 点击:次
这里用到的是scipy.optimize的fmin和fminbound
import numpy as np from matplotlib import pyplot as plt from scipy.optimize import fmin,fminbound def f(x): return x**2+10*np.sin(x)+1 x=np.linspace(-10,10,num=500) min1=fmin(f,3)#求3附近的极小值 min2=fmin(f,0)#求0附近的极小值 min_global=fminbound(f,-10,10)#这个区域的最小值 print(min1) print(min2) print(min_global) plt.plot(x,f(x)) plt.show()
输出:
Optimization terminated successfully.
Current function value: 9.315586
Iterations: 15
Function evaluations: 30
Optimization terminated successfully.
Current function value: -6.945823
Iterations: 26
Function evaluations: 52
[3.83745117]
[-1.3064375]
-1.306440096615395
