英文:
Alternatives to Scipy in Global optimization
问题
我正在尝试找到一个多元函数的全局最小值（ndim = 9）。该函数具有非常窄的底部，X的轻微变化会导致输出值迅速上升。我尝试了各种scipy方法，如differential_evolution、basin_hopping、dual_annealing和shgo。它们表现得相当不错，但我仍然最终陷入了局部最小值而不是绝对全局最小值。
您建议尝试哪些其他软件包/算法？
英文:
I'm trying to find a global minimum of a multivariate function (ndim = 9). The function has very narrow bottoms and a slight change to the X causes the output value to rise quite quickly. I tried various scipy methods, like differential_evolution, basin_hopping, dual_annealing, shgo. They perform quite well, but I still end up in the local minimum rather than the absolute global minimum.
What other packages/algorithms would you suggest trying?
答案1
得分: 1
你可以尝试使用nlopt。我已经使用过它几次，主要用于局部优化，大部分情况下是在C++中（它还有Python绑定），但看起来它可能也具有一些SciPy没有的全局优化算法。我不确定它是否会更好，但也许值得一试。祝你好运！
英文:
You could try nlopt. I've used it a few times, mostly for local optimization and mostly in C++ (it also has Python bindings), but it looks like it also might have some global optimization algorithms that SciPy doesn't. I'm not sure if it will be better, but perhaps worth a shot. Good luck!
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