This is a significant generalization of my earlier stochastic optimization efforts. I presented as an author at the INFORMS 2015 Annual Conference in Philadelphia.
A big part of the generalization was defining a more abstract setting for the problem. In a nutshell, this work concentrates on two things:
- Using interval estimation to provide confidence levels and error tolerances.
- Attempting to optimize over continuous and discrete domains.
The experimental results from the presentation were generated and processed in Python before using Mathematica to render the graphs.
This is an ongoing work, so expect to see another post about this before 2016!