[ipsum2]

> Even more intriguingly, SigOpt helps you optimize physical experiments. For example, Clark said one of his initial customers is using SigOpt to test different chemical combinations in creating shaving creams. It shouldn’t matter what you’re testing — Clark said SigOpt’s technology just uses your data and “builds up this model of what it thinks the world looks like. … It’s designed to be a very general system.”

This is really vague, can you give some more technical details on what's actually happening?

[Zephyr314]

Great question. We build off of research in Design of Experiments [1] in general and Optimal Learning [2] in particular. Our algorithms attempt to make the tradeoff between exploration (learning more about the space we are optimizing in) and exploitation (using the information we have to achieve the best values) to find optimal parameter configurations for experiments as quickly and efficiently as possible.

This has been an active field in academia for the last few decades, and the seminal paper behind some of our algorithms was published in 1998 [3]. There have been many successful applications in different fields from drug discovery [4] to nanotechnology experiments [5].

We wrap these powerful techniques behind an API and web interface to let anyone start running optimal experiments and leverage this research right away for any experiment they are trying to optimize. We have some more examples and a use case on our site [6].

[1]: http://en.wikipedia.org/wiki/Design_of_experiments

[2]: http://optimallearning.princeton.edu

[3]: http://link.springer.com/article/10.1023/A:1008306431147#pag...

[4]: http://people.orie.cornell.edu/pfrazier/Presentations/2014.1...

[5]: http://optimallearning.princeton.edu/tutorialsciences.htm

[6]: https://sigopt.com/cases/physical_experiment