使用scipy最小化也采用非变分参数的函数 [英] Using scipy to minimize a function that also takes non variational parameters

问题描述

我想使用scipy.optimize模块来最小化功能.假设我的功能是f(x,a):

I want to use the scipy.optimize module to minimize a function. Let's say my function is f(x,a):

def f(x,a):
 return a*x**2

对于固定的a，我想相对于x最小化f(x,a).

For a fixed a, I want to minimize f(x,a) with respect to x.

使用scipy，我可以导入例如fmin函数(我有一个旧版本:v.0.9.0)，给出一个初始值x0，然后进行优化(

With scipy I can import for example the fmin function (I have an old scipy: v.0.9.0), give an initial value x0 and then optimize (documentation):

from scipy.optimize import fmin
x0 = [1]
xopt = fmin(f, x0, xtol=1e-8)

失败，因为f接受两个参数，而fmin仅传递一个参数(实际上，我什至还没有定义a).如果我这样做:

which fails because f takes two arguments and fmin is passing only one (actually, I haven't even defined a yet). If I do:

from scipy.optimize import fmin
x0 = [1]
a = 1
xopt = fmin(f(x,a), x0, xtol=1e-8)

计算也将失败，因为"x未定义".但是，如果我定义x，则没有可优化的变分参数.

the calculation will also fail because "x is not defined". However, if I define x then there is no variational parameter to optimize.

在这里如何允许将非变数参数用作函数参数?

How do I allow non-variational parameters to be used as function arguments here?

推荐答案

了解args参数.在其文档字符串中进行optimize.fmin.html> fmin ，并使用

Read about the args argument to fmin in its docstring, and use

a = 1
x0 = 1
xopt = fmin(f, x0, xtol=1e-8, args=(a,))

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