Mixed Procedures For Stochastic Optimization
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.
Some of the notes created during the research process for both deterministic and stochastic cases: PDF1, PDF2
The experimental results from the presentation were generated and processed in Python before using Mathematica to render the graphs.
![](/research/epsilonalphasample.png)
This is an ongoing work, so expect to see another post about this before 2016!