The methods in scipy.optimize are great if your function is serial, cheap, deterministic, and convex. Many ML models and real world problems don't fit into this

The methods in scipy.optimize are great if your function is serial, cheap, deterministic, and convex. Many ML models and real world problems don't fit into this context though. We have a Jupyter notebook showing how SigOpt compares to several scipy.optimize methods as well as standard methods like grid/random on a simple non-convex problem here [1]. These results only get more striking as the dimensionality increases. [1]: https://github.com/sigopt/sigopt-examples/blob/master/ipython-notebook-example/SigOpt_Introduction.ipynb https://github.com/sigopt/sigopt-examples/blob/master/ipytho...