我正在尝试使用scipy.optimize函数来查找具有多个参数的复杂函数的全局最小值.scipy.optimize.minimize似乎最好的工作,即'Nelder-Mead'方法.但是,它倾向于从参数'域中进入区域(将负值分配给只能为正的参数),从而在这种情况下返回错误.有没有办法来限制的论点范围的内scipy.optimize.minimize的功能本身？或者也许在其他scipy.optimize功能中？
我找到了以下建议:
当参数超出允许范围时,返回一个非常大的数字(远离要安装的数据).这将(希望)对这种参数的选择进行过多的惩罚,以便
curve_fit将其他一些允许的参数集合作为最佳参数.
在上一个答案中给出,但在我的情况下,该过程将花费大量的计算时间.
I come upon the following optimization problem:
The target function is a multivariate and non-differentiable function which takes as argument a list of scalars and return a scalar. It is non-differentiable in the sense that the computation within the function is based on pandas and a series of rolling, std, etc. actions.
The pseudo code is below:
def target_function(x: list) -> float:
# calculations
return output
Besides, each component of the x argument has its own bounds defined as a …