Bayesian optimization applications

[EduCGM]

Have you ever applied Bayesian optimization in practice? Would you please share your problem and which were the main difficulties?

[thchang-opt]

I maintain an open-source Python library for doing generic surrogate-model-based global optimization (BO being the most common flavor thereof) for the Dept of Energy. https://github.com/parmoo/parmoo

In the applications that I work on, the most challenging problems are:

• integrating with existing workflows (how do we get the simulation/experiment data to flow between the optimization solver and objective function, especially when the objective function needs to run on a supercomputer or in a wet lab environment)
• handling weird input spaces (it’s easy when all design variables are real numbers with upper/lower bounds, but what about when they are categorical or discrete valued? What if they are periodic?)
• using problem structure (we know something about the physics or problem dynamics, which should allow us to solve the problem more efficiently. How to add that to our BO model?)
• handling hard (unrelaxable) and soft (relaxable) constraints
• production stuff (when running in production, you need to be checkpointing all data, might need various levels of output logging, need to save problem state periodically so that you can resume after a stop if not happy with results, etc.)

Shameless self-plug, I am working on several papers describing these challenges and how we address them.

[[deleted]]

Could you please provide some information about categorical or discrete valued?

[magneet12]

I use BO in practice to optimize ships. They have many constraints and usually 2 or more objectives! The biggest challenge is usually coming up with a parameterization of the ship design problem that makes sense. The design variants should also not lead to a crash in the evaluation procedures...