Does NEXTorch support inequality constraints?

[sgbaird]

Nice work on NEXTorch! Seems like a compelling idea, and some difficulty with implementing human-in-the-loop optimization via Ax is what led me here.

Something very important for my use-case is inequality constraints for composition-based Bayesian optimization (also prevalence-based, fractional-based, etc., i.e. everything needs to sum to 1). For example:

A + B + C <= 1

where A, B, and C are parameters to be optimized.

For me, there would technically also be D, the final parameter is determined automatically outside of the optimization loop via:

D = 1 - sum([A, B, C])

In other words, safe to ignore D, but relevant for the context of a composition-based optimization.

Is it possible to specify an inequality constraint within the current implementation of NEXTorch?

[Leon924]

same question as @sgbaird raised. I need to set some constraints on parameters to regulate the parameter space. Any idea to solve this issue?