Issues with Acquistion Function Optimizations (optimize_acqf and LogEI)

[ohassn]

Hello,

I had wanted to discuss two topics regarding acquisition function optimizations.

1. Going from version 0.11.3 to 0.13 addresses an issue where LogEI exhibits overly exploratory behaviour. For instance, if one were to run optimize_acqf 100 times (without updating the model) and plot the candidates, there is a clear issue on version 0.11.3:

The generated candidates are entirely different, quite often at the bounds. This issue doesn't persist in version 0.13:

However, there is still the occasional outlier (which is not seen with UCB). Is there a possibility of fixing this?

2. Why does optimize_acqf perform the optimization over unnormalized bounds? I tested two cases using a SingleTaskGP:

• Defined the GP with an input_transform using Normalize, with the unnormalized bounds for optimize_acqf.

• Defined the GP without an input_transform but instead a manual min-max transformation on the inputs. Using the normalized inputs as the bounds for optimize_acqf.

The latter method is much quicker, my tests show a 5x speed boost. Is there a reason this isn't done by default?

Thank you in advance! Please let me know if you need any additional information.

[Balandat]

Can you share a bit more about your setup and ideally a repro with the code you used to generate these plots?

Going from version 0.11.3 to 0.13 addresses an issue where LogEI exhibits overly exploratory behaviour.

Great to hear. Do you happen to know what changes in particular fixed this? Mostly so that we can be on the lookout for similar issues in the future. My suspicion is that this may be a change @SebastianAment made to avoid using Boltzmann sampling on the initial candidates to avoid "overexploitative" behaviors in the random restarts.

However, there is still the occasional outlier (which is not seen with UCB). Is there a possibility of fixing this?

I would think so, but we'd first have to understand the mechanism that causes this. One obvious guess would be that as we're optimizing a nonconvex function (over a potentially high-dimensional domain) our random restart initializations miss the basin of attraction of the global optimum (or are otherwise degenerate) and so the optimization only finds a local optimum. However, this could also be some other failure of the optimization. If you provide a reproducible example (ideally with a fixed seed or set of initial conditions) we could take a look at what's going on when this failure occurs.

Why does optimize_acqf perform the optimization over unnormalized bounds?

The main reason this is done by default is that when that was not the case we've observed many users in the past struggle b/c they were not normalizing their inputs. So we opted to do this automatically for them by default.

The latter method is much quicker, my tests show a 5x speed boost. Is there a reason this isn't done by default?

Seems like the difference in speed comes from getting rid of the overhead of the transform during the optimization, but it would be good to profile this. I'd need to know more about the problem here to say something more intelligible - what dimension, how many observations, what exactly are you optimizing, etc. That said, a 5x boost seems quite substantial. @saitcakmak you have observed some slowdowns from the transforms, but IIRC not nearly as large - any thoughts on this?

[saitcakmak]

That said, a 5x boost seems quite substantial. @saitcakmak you have observed some slowdowns from the transforms, but IIRC not nearly as large - any thoughts on this?

The overhead from transforms should be nowhere near that. I'd be curious to see a repro for this.

[sdaulton]

Does using bounds that are not [0,1] affect how the initial step size should be set for LBFGS-B (and affect runtime)?

[ohassn]

Do you happen to know what changes in particular fixed this?

Not really, I tried going through the release notes but I didn't find anything regarding this.

Can you share a bit more about your setup and ideally a repro with the code you used to generate these plots?

training_set.csv
acq_func.txt
acq_func2.txt

I have a 6D input space and I'm using a SingleTaskGP to optimize on an observed quantity. The first file is some data I collected while running the code. The next two files are recreating the GP state at the end of optimization based on this data and the model parameters at the time, I then evaluate optimize_acqf over 100 trials while timing it.

The second file is the regular method: unnormalized bounds, input transform for normalization), and the third file is using normalized bounds everywhere.

Context behind this work can be found in this paper. My PyTorch version is 2.6.0+cpu and BoTorch is 0.13.0.

Thank you for the responses, do let me know if anything else is needed!