Matlab的fminsearch的numpy/scipy类似物 [英] numpy/scipy analog of matlab's fminsearch

问题描述

我正在使用 numpy 将一些Matlab代码转换为python.一切工作都非常顺利，但是最近我遇到了 fminsearch 函数.

I am converting some Matlab code into python using numpy. Everything worked pretty smoothly but recently I encountered fminsearch function.

因此，简而言之:是否有一种简单的方法可以在python中制作像这样的东西:

So, to cut it short: is there an easy way to make in python something like this:

banana = @(x)100*(x(2)-x(1)^2)^2+(1-x(1))^2;
[x,fval] = fminsearch(banana,[-1.2, 1])

将返回

x = 1.0000    1.0000
fval = 8.1777e-010

到目前为止，我还没有发现类似numpy的任何东西.我发现类似的唯一东西是 scipy.optimize.fmin .根据定义

Up till now I have not found anything that looks similar in numpy. The only thing that I found similar is scipy.optimize.fmin. Based on the definition it

使用下坡单纯形算法最小化功能.

Minimize a function using the downhill simplex algorithm.

但是现在我找不到使用此功能编写上述Matlab代码

But right now I can not find to write the above-mentioned Matlab code using this function

推荐答案

这只是从Matlab语法到python语法的直接转换:

It's just a straight-forward conversion from Matlab syntax to python syntax:

import scipy.optimize

banana = lambda x: 100*(x[1]-x[0]**2)**2+(1-x[0])**2
xopt = scipy.optimize.fmin(func=banana, x0=[-1.2,1])

输出:

Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 85
         Function evaluations: 159
array([ 1.00002202,  1.00004222])

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