# Constrained optimization with Optuna and TPESampler **URL:** \ **Category:** Ray Tune\ **Created:** [March 10, 2025, 5:01pm UTC](https://discuss.ray.io/t/constrained-optimization-with-optuna-and-tpesampler/22010 "2025-03-10T17:01:57Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![ggalan87](https://avatars.discourse-cdn.com/v4/letter/g/4bbf92/32.png) [@ggalan87](https://discuss.ray.io/u/ggalan87)\ **Post date:** [March 10, 2025, 5:01pm UTC](https://discuss.ray.io/t/constrained-optimization-with-optuna-and-tpesampler/22010/1 "2025-03-10T17:01:57Z")
**1. Severity of the issue: (select one)** None: I’m just curious or want clarification. Low: Annoying but doesn’t hinder my work. Medium: Significantly affects my productivity but can find a workaround. High: Completely blocks me. **2. Environment:** - Ray version: 2.42.1 - Python version: 3.10 - OS: Ubuntu/Linux - Cloud/Infrastructure: - - Other libs/tools (if relevant): - **3. What happened vs. what you expected:** - Expected: - - Actual: - Hello, I am interested in experimenting with objective constraints. Up to now I have followed the sugestions from [another topic](https://discuss.ray.io/t/air-tune-custom-hyperparameter-constraints/9456) for penalizing erroneous configurations. From optuna docs, TPESampler is able to handle constraints directly with `constraints_func` argument. I was wondering if such functionality is available to ray, since it depends on setting the optuna trial’s attributes, e.g. as shown in a [relevant medium article](https://medium.com/optuna/announcing-optuna-4-2-98148689e626). Can we access search algorithm internals (i.e. optuna trials) from within ray objective function or from a callback passed as RunConfig? In the later case callback is called with ray Trial param, however I couldn’t find a relation to the corresponding Optuna trial, as they are probably maintained separately. Thanks George