python中的离散优化 [英] Discrete optimization in python

问题描述

我正在尝试使用 scipy.optimize 包来优化离散优化问题(全局优化).根据文档，在 scipy.optimize.anneal 中实现的模拟退火应该是一个不错的选择.但我不确定如何强制优化器只搜索搜索空间的整数值.有人可以帮忙吗?

I am trying to use the scipy.optimize package to optimize a discrete optimization problem (global optimization). Acc to the doc, simulated annealing implemented in scipy.optimize.anneal should be a good choice for the same. But I am not sure how to force the optimizer to search only integer values of the search space. Can someone help?

一个说明性的例子:

f(x1,x2) = (1-0.4*x1)^2 + 100*(0.6*x2 -0.4*x1^2)^2

where, $x1, x2 \in I$

推荐答案

我已经检查了 scipy.optimize.anneal，但我看不到使用离散值的方法.自己实现它的方法是创建一个自定义的移动"函数，但是您必须指定时间表的方式(通过字符串)阻止您这样做.

I've checked scipy.optimize.anneal, and I can't see a way to use discrete values. The way to implement it yourself, is to create a custom "move" function, but the way you have to specify the schedule (by a string) prevents you from doing so.

我认为这是一个很大的错误，如果您可以将自定义计划类作为参数传递，您可以自定义它以使用离散变量等等.

I think it is a big mistake, if you could just pass a custom schedule class as the parameter, you could customize it for using discrete variables and many more things.

我找到的解决方案是改用这个其他实现:https://github.com/perrygeo/python-simulated-annealing

The solution I found is to use this other implementation instead: https://github.com/perrygeo/python-simulated-annealing

因为您必须提供修改状态的函数，所以您可以控制它可以具有的值，或者它们是离散的还是连续的.

Because you have to provide the function which modifies the state, you have control on what values it can have, or if they are discrete or continuous.

希望有帮助.

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