Optuna v5 will be built with the power of the community! We'd love your input and participation — check out the feedback form on the roadmap:

AutoSampler now fully supports multi-objective & constrained optimization! AutoSampler lets you efficiently solve a wide range of problems without worrying about which optimization algorithm to use. Learn more in our article:

Our Optuna paper has reached over 10,000 citations! A huge thank you to the amazing co-authors, contributors, and community! 🌀🚀

We published an arXiv preprint entitled "OptunaHub: A Platform for Black-Box Optimization." The paper describes the motivation and the ecosystem overview. https://t.co/SuQQOep9aF

Kaito Baba (@kAIto47802) added constraint handling for multi-objective optimization by GPSampler, a Gaussian process-based Bayesian optimization. It is especially beneficial when many expensive metrics need to be considered. Learn more in our article:

We recently published an arXiv article about a sample-efficient black-box combinatorial optimization for TPE by considering distance structures of categorical parameters. This feature is available via categorical_distance_func in TPESampler. https://t.co/r6JGtCJTTb

Optuna v4.5 has been released! ⛏️GPSampler for constrained multi-objective optimization 🚀Significant speedup of TPESampler and plot_hypervolume_history 🦾CmaEsSampler now supports 1D search space 🐍The optunahub library is published on conda-forge https://t.co/QS314M0DaI

Optuna v4.4 is now available as of June 16! This release introduces the Optuna MCP Server, our first LLM-based toolchain. We're already hard at work on Optuna v5, following our roadmap. For full details, check out our blog post. https://t.co/TJHPWcBhgB

See the release blog for more details.

See the repository for more details of Optuna MCP server https://t.co/MvUCXPwjzW

Check out the release note!

Optuna v4.4 has been released with various new features, bug fixes, and enhancements. 🚀Optuna MCP server, which is our first LLM intensive toolchain ✅Gaussian process-based algorithm now supports multi-objective optimization 🌀A lot of new features in OptunaHub

See our blog for more details!

We've published the development roadmap for Optuna v5, the next major release! Scheduled for release next summer, v5 will focus on making Optuna even more powerful and user-friendly — especially at the intersection of generative AI and black-box optimization.

Our OptunaHub Benchmarks article is featured on AutoML Space:) Sampler benchmarking becomes easier with OptunaHub Benchmarks! https://t.co/BppvpMagXr

Optuna v4.3.0 has been released! This is a maintenance release with various minor bug fixes and improvements to the documentation and more. 👇See what’s new https://t.co/eqfw9FaBJA

In Optuna v4.2, the gRPC storage proxy has been added for large-scale distributed optimization. Check out our latest blog post for details!

Happy to be a part of PyTorch Landscape! https://t.co/mD6o4QS7yN

Excited to be a part of AutoML Space organized by world-leading researchers, a community for those interested in AutoML. Our first post covers a SOTA sampler integration by Difan Deng from @AutoML_org into OptunaHub. https://t.co/D3cnX3kksu

Kaito Baba (@kAIto47802) implemented constrained optimization for GPSampler, a Gaussian process-based Bayesian optimization! This allows handling constraints such as the maximum possible neural network size. Check out our article! https://t.co/lwM5aNqayW