使用scipy进行优化 [英] Optimisation using scipy

问题描述

在以下脚本中:

import numpy as np
from scipy.optimize import minimise

a=np.array(range(4))
b=np.array(range(4,8))

def sm(x,a,b):
      sm=np.zeros(1)
      a=a*np.exp(x)
      sm += sum(b-a)
      return sm

 x0=np.zeros(4)
 print sm(x0,a,b) #checking my function

 opt = minimize(sm,x0,args=(a,b),method='nelder-mead', 
 options={'xtol': 1e-8,     'disp': True})        

我正在尝试针对x进行优化，但是我收到以下消息:

I am trying to optimise for x but I am having the following message:

警告:超过了功能评估的最大数量.

Warning: Maximum number of function evaluations has been exceeded.

结果是:

array([-524.92769674，276.6657959，185.98604937，729.5822923])

array([-524.92769674, 276.6657959 , 185.98604937, 729.5822923 ])

这不是最佳选择. 我的问题是我是否收到此消息和结果，因为起点不正确?

Which is not the optimal. My question is am I having this message and result because my starting points are not correct?

推荐答案

您的函数sm似乎是不受限制的.随着x的增加，sm的负值将变得越来越大，因此，它会降到-inf.

Your function sm appears to be unbounded. As you increase x, sm will get ever more negative, hence the fact that it is going to -inf.

Re:注释-如果要使sm()尽可能接近零，请修改函数定义的最后一行以读取return abs(sm).

Re: comment - if you want to make sm() as close to zero as possible, modify the last line in your function definition to read return abs(sm).

这使函数的绝对值最小化，使其接近零.

This minimised the absolute value of the function, bringing it close to zero.

您的示例结果:

>>> opt = minimize(sm,x0,args=(a,b),method='nelder-mead', options={'xtol': 1e-8,     'disp': True})
Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 153
         Function evaluations: 272
>>> opt
  status: 0
    nfev: 272
 success: True
     fun: 2.8573836630130245e-09
       x: array([-1.24676625,  0.65786454,  0.44383101,  1.73177358])
 message: 'Optimization terminated successfully.'
     nit: 153

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