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Bayesian optimization in PyTorch

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# πŸ› Bug I am new to BoTorch and programming and when implementing a simple Bayesian Optimization Loop I get this error: warnings.warn("Fitting failed on all retries.", RuntimeWarning) I looked...

bug

Summary: Adding unit test to check that correct options are passed from `fit_gpytorch_model` to scipy optimizer. Reviewed By: saitcakmak Differential Revision: D38713285

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Hi, I encountered a problem with the `get_polytope_samples` when the individual variables live in different dimensions. Here is a small didactic example: ``` from torch import tensor from botorch.utils.sampling import...

## Motivation As outlined in issue https://github.com/pytorch/botorch/issues/1272, `optimize_acqf_list` cannot be used to optimize over mixed domains. For this reason, this PR introduces the argument `fixed_features_list` for `Γ²ptimize_acqf_list`. Calling `optimize_acqf_list` with...

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# πŸ› Bug Despite my model and all tensors in the script being on the GPU, fit_gpytorch_model complains about tensors existing on both cuda:0 and CPU. ## To reproduce This...

bug

# πŸ› Bug ## To reproduce ** Code snippet to reproduce ** ```python Sorry to say code repo is too much to provide. I am using a DKL-GP model, which...

bug
upstream issue

# πŸš€ Feature Request I find myself searching through the docstrings or the API reference whenever I am not sure what model to use. These typically have enough information to...

enhancement

# πŸš€ Feature Request My design space is fully discrete(every parameter support few levels/integer values) and extremely huge so that can not be enumerated ahead and provide to `optimize_acqf_discrete`. I...

enhancement

## Issue description I'm trying to implement multi-objective parallel bayesian optimization with constraints according to the tutorial. However, the generated queries are not respecting the constraint. So I would mainly...

Hi, In [ConstrainedBaseTestProblem](https://github.com/pytorch/botorch/blob/a7560d20b69e55cedd39b4534a2f8b81ba786cf4/botorch/test_functions/base.py#L68), constraints are expressed in the form of `c(x) >= 0`, while in [ConstrainedMaxPosteriorSampling](https://github.com/pytorch/botorch/blob/a7560d20b69e55cedd39b4534a2f8b81ba786cf4/botorch/generation/sampling.py#L242), constraints have the form of `c(x)