How to set equality constraints for bayesian optimization？

[yuanquan010]

Hello, thanks a lot for this excellent project， but when I use it to optimize the parameters of the simulation model, I run into some problems.
For the setting of SearchSpace, there are three types of inequality constraints---linear, order, and sum constraints. However, if I want to set some complex hierarchical equality parameter constraints, such as a * b+b * c = v*p, how can I achieve this? Here a, b, c, v, and p are RangeParameters.

[bernardbeckerman]

Hi @yuanquan010, thanks for reporting this! This issue was previously reported and currently is being tracked in our wishlist issue, so I am closing this out. Apologies that our wishlist issue obfuscated that this was already being tracked, and again, thanks a lot for reporting!

[bernardbeckerman]

I think though that there is an easier way to do this. Particularly, in your equation

a * b + b * c = v * p

you have 5 parameters, but really only 4 degrees of freedom (i.e., the value of p is fixed given a, b, c, and v). I think the proper way to tackle this problem is to have only 4 range parameters (say, a, b, c, and v), and then create the 5th in your workflow via

p = (a * b + b * c) / v

That way, the 5 values of these variables will always be satisfied. Does that make sense?

[yuanquan010]

I think though that there is an easier way to do this. Particularly, in your equation

a * b + b * c = v * p

you have 5 parameters, but really only 4 degrees of freedom (i.e., the value of p is fixed given a, b, c, and v). I think the proper way to tackle this problem is to have only 4 range parameters (say, a, b, c, and v), and then create the 5th in your workflow via

p = (a * b + b * c) / v

That way, the 5 values of these variables will always be satisfied. Does that make sense?

Okay， I get it, thank you

[sgbaird]

xref: #786