Comments (3)
Hi @riyadparvez, we don't have any immediate plans. We do have a tutorial notebook on using CMA-ES to optimize acquisition functions in BoTorch, but haven't had any use-cases for zeroth-order optimizers within Ax itself. Our goal has been to accelerate experimentation in cases where experiments are costly to run—so our focus has been around algorithms that perform well with less than a few hundred iterations.
What use-case did you have in mind for population-based optimization with Ax?
That being said, it should be fairly straightforward to add a model bridge for Nevergrad.
If you do end up putting something together, we'd be happy to review any PRs and add it to Ax!
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We will now be tracking wishlist items / feature requests in a master issue for improved visibility: #566. Of course please feel free to still open new feature requests issues; we'll take care of thinking them through and adding them to the master issue.
For this specific issue, there is currently ongoing work that may address it; we will make sure to comment here if/when the prospects become clearer there.
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Related Issues (20)
- MOO not respecting nonlinear constraints HOT 1
- Problem with Fixed parameters if nonlinear_inequality_constraint is imposed HOT 1
- Safe optimization in the Service API HOT 5
- The same point is evaluated multiple times during Integer Optimization with BO. HOT 5
- ax_client.generation_strategy.trials_as_df HOT 9
- Managing Objective Function Evaluation Failures in Ax for MOO HOT 6
- [GENERAL SUPPORT]: Managing Objective Function Evaluation Failures in Ax for MOO HOT 3
- [GENERAL SUPPORT]: Using qNegIntegratedPosteriorVariance HOT 3
- [GENERAL SUPPORT]: Reference point for multi-objective bayesian optimization HOT 4
- [GENERAL SUPPORT]: Adjusting search space or accommodating out-of-bounds initial data HOT 19
- [GENERAL SUPPORT]: Manual configuration, HOT 1
- [Bug]: Custom metric issue HOT 4
- [GENERAL SUPPORT]: CI_Level Paretofrontier
- [Bug]: Large sample time increase in ax-platform >= version 0.3.5 HOT 6
- [GENERAL SUPPORT]: Reference Point for Multi-Objective Bayesian Optimization HOT 5
- [GENERAL SUPPORT]: Plotting Pareto fronts / Posterior mean model HOT 5
- [GENERAL SUPPORT]: Getting best predicted point of a botorch model HOT 8
- [GENERAL SUPPORT]: Logical-or in outcome constraints HOT 4
- [GENERAL SUPPORT]: Parallelism and arbitrary parameter type support. HOT 5
- [GENERAL SUPPORT]: How to Standardize and Normalize Data for Service API HOT 6
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