Description
<div align="center"><img src="https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png" width="800"/></div> # Optuna: A hyperparameter optimization framework [](https://www.python.org) [](https://pypi.python.org/pypi/optuna) [](https://anaconda.org/conda-forge/optuna) [](https://github.com/optuna/optuna) [](https://optuna.readthedocs.io/en/stable/) :link: [**Website**](https://optuna.org/) | :page_with_curl: [**Docs**](https://optuna.readthedocs.io/en/stable/) | :gear: [**Install Guide**](https://optuna.readthedocs.io/en/stable/installation.html) | :pencil: [**Tutorial**](https://optuna.readthedocs.io/en/stable/tutorial/index.html) | :bulb: [**Examples**](https://github.com/optuna/optuna-examples) | [**Twitter**](https://twitter.com/OptunaAutoML) | [**LinkedIn**](https://www.linkedin.com/showcase/optuna/) | [**Medium**](https://medium.com/optuna) *Optuna* is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, *define-by-run* style user API. Thanks to our *define-by-run* API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. ## :loudspeaker: News Help us create the next version of Optuna! Optuna 5.0 Roadmap published for review. Please take a look at [the planned improvements to Optuna](https://medium.com/optuna/optuna-v5-roadmap-ac7d6935a878), and share your feedback in [the github issues](https://github.com/optuna/optuna/labels/v5). PR contributions also welcome! Please take a few minutes to fill in [this survey](https://forms.gle/wVwLCQ9g6st6AXuq9), and let us know how you use Optuna now and what improvements you'd like.🤔 All questions are optional. 🙇♂️ <!-- TODO: when you add a new line, please delete the oldest line --> * **Jan 19, 2026**: Optuna 4.7.0 is out! Check out [the release note](https://github.com/optuna/optuna/releases/tag/v4.7.0) for details. * **Nov 10, 2025**: A new article [Announcing Optuna 4.6](https://medium.com/optuna/announcing-optuna-4-6-a9e82183ab07) has been published. * **Oct 28, 2025**: A new article [AutoSampler: Full Support for Multi-Objective & Constrained Optimization](https://medium.com/optuna/autosampler-full-support-for-multi-objective-constrained-optimization-c1c4fc957ba2) has been published. * **Sep 22, 2025**: A new article [[Optuna v4.5] Gaussian Process-Based Sampler (GPSampler) Can Now Perform Constrained Multi-Objective Optimization](https://medium.com/optuna/optuna-v4-5-81e78d8e077a) has been published. * **Jun 16, 2025**: Optuna 4.4.0 has been released! Check out [the release blog](https://medium.com/optuna/announcing-optuna-4-4-ece661493126). * **May 26, 2025**: Optuna 5.0 roadmap has been published! See [the blog](https://medium.com/optuna/optuna-v5-roadmap-ac7d6935a878) for more details. ## :fire: Key Features Optuna has modern functionalities as follows: - [Lightweight, versatile, and platform agnostic architecture](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/001_first.html) - Handle a wide variety of tasks with a simple installation that has few requirements. - [Pythonic search spaces](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html) - Define search spaces using familiar Python syntax including conditionals and loops. - [Efficient optimization algorithms](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/003_efficient_optimization_algorithms.html) - Adopt state-of-the-art algorithms for sampling hyperparameters and efficiently pruning unpromising trials. - [Easy parallelization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/004_distributed.html) - Scale studies to tens or hundreds of workers with little or no changes to the code. - [Quick visualization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/005_visualization.html) - Inspect optimization histories from a variety of plotting functions. ## Basic Concepts We use the terms *study* and *trial* as follows: - Study: optimization based on an objective function - Trial: a single execution of the objective function Please refer to the sample code below. The goal of a *study* is to find out the optimal set of hyperparameter values (e.g., `regressor` and `svr_c`) through multiple *trials* (e.g., `n_trials=100`). Optuna is a framework designed for automation and acceleration of optimization *studies*. <details open> <summary>Sample code with scikit-learn</summary> [ | Low | 4/21/2026 |
| v4.8.0 | This is the release note of [v4.8.0](https://github.com/optuna/optuna/milestone/73?closed=1). # Highlights ## Support for Constant Liar Strategy to GPSampler A constant liar strategy for efficient parallelization has been introduced to GPSampler by @sawa3030. The figures (left: v4.7.0, right: v4.8.0) show that the overlap of search points has decreased, and a wider variety of solutions are being explored. The experiment uses `n_jobs = 10` and `n_trials = 100`. Currently, this feature su | Low | 3/16/2026 |
| v4.7.0 | This is the release note of [v4.7.0](https://github.com/optuna/optuna/milestone/72?closed=1). # Highlights ## Two New Multi-Objective Samplers Added to OptunaHub! <img width="1487" height="946" alt="hype-sampler" src="https://github.com/user-attachments/assets/717752e7-4f55-4519-a407-70b1e0502052" /> @hrntsm introduces two new multi-objective samplers—SPEA-II (Strength Pareto Evolutionary Algorithm 2) and HypE (Hypervolume Estimation Algorithm)—to OptunaHub. SPEA-II is an improved mu | Low | 1/19/2026 |
| v4.6.0 | This is the release note of [v4.6.0](https://github.com/optuna/optuna/milestone/71?closed=1). # Highlights ## Optuna Dashboard LLM Integration [Optuna Dashboard](https://github.com/optuna/optuna-dashboard) is a web-based tool that helps you easily explore and visualize your Optuna optimization history. The latest release, v0.20.0, introduces LLM integration, enabling the natural language-based Trial filtering and automatic Plotly chart generation. Please refer to the release blog for mo | Low | 11/10/2025 |
| v4.5.0 | This is the release note of [v4.5.0](https://github.com/optuna/optuna/milestone/70?closed=1). # Highlights ## `GPSampler` for constrained multi-objective optimization `GPSampler` is now able to handle multiple objective and constraints simultaneously using the newly introduced constrained LogEHVI acquisition function. The figures below show the difference between `GPSampler` (LogEHVI, unconstrained) vs `GPSampler` (constrained LogEHVI, new feature). The 3-dimensional version of the C2D | Low | 8/18/2025 |
| v4.4.0 | This is the release note of [v4.4.0](https://github.com/optuna/optuna/milestone/69?closed=1). # Highlights In addition to new features, bug fixes, and improvements in documentation and testing, version 4.4 introduces a new tool called the [Optuna MCP Server](https://github.com/optuna/optuna-mcp). ## Optuna MCP Server The Optuna MCP server can be accessed by any MCP client via uv — for instance, with Claude Desktop, simply add the following configuration to your MCP server settings file | Low | 6/16/2025 |
| v4.3.0 | This is the release note of [v4.3.0](https://github.com/optuna/optuna/milestone/66?closed=1). # Highlights This has various bug fixes and improvements to the documentation and more. # Breaking Changes - [fix] lgbm 4.6.0 compatibility (https://github.com/optuna/optuna-integration/pull/207, thanks @ffineis!) # Enhancements - Accept custom objective in `LightGBMTuner` (https://github.com/optuna/optuna-integration/pull/203, thanks @sawa3030!) - Improve time complexity of `Intersec | Low | 4/14/2025 |
| v4.2.1 | This is the release note of v4.2.1. This release includes a bug fix addressing an issue where Optuna was unable to import if an older version of the grpcio package was installed. ## Bug - [backport] Use `_LazyImport` for grpcio package (#5965) ## Other - Bump up version number to v4.2.1 (#5964) ## Thanks to All the Contributors! This release was made possible by the authors and the people who participated in the reviews and discussions. @c-bata @HideakiImamura @nabenabe0928 | Low | 2/12/2025 |
| v3.6.2 | This is the release note of v3.6.2. # Bug Fixes - [Backport] Fix the default sampler of `load_study` function. # Other - Bump up version number to v3.6.2 | Low | 1/27/2025 |
| v3.5.1 | This is the release note of v3.5.1. # Bug Fixes - [Backport] Fix the default sampler of `load_study` function. # Other - Bump up version number to v3.5.1. | Low | 1/27/2025 |
| v3.4.1 | This is the release note of v3.4.1. # Bug Fixes - [Backport] Fix the default sampler of `load_study` function. # Other - Bump up version number to v3.4.1. | Low | 1/27/2025 |
| v4.2.0 | This is the release note of [v4.2.0](https://github.com/optuna/optuna/releases/tag/v4.2.0). In conjunction with the Optuna release, OptunaHub 0.2.0 is released. Please refer to [the release note of OptunaHub 0.2.0](https://github.com/optuna/optunahub/releases/tag/v0.2.0) for more details. Highlights of this release include: - 🚀gRPC Storage Proxy for Scalable Hyperparameter Optimization - 🤖 SMAC3: Support for New State-of-the-art Optimization Algorithm by AutoML.org (@automl) - 📁 Optun | Low | 1/20/2025 |
| v4.1.0 | This is the release note of [v4.1.0](https://github.com/optuna/optuna/milestone/64?closed=1). Highlights of this release include: - 🤖 AutoSampler: Automatic Selection of Optimization Algorithms - 🚀 More scalable RDB Storage Backend - 🧑💻 Five New Algorithms in OptunaHub (MO-CMA-ES, MOEA/D, etc.) - 🐍 Support Python 3.13 The updated list of tested and supported Python releases is as follows: - [Optuna 4.1](https://github.com/optuna/optuna/releases/tag/v4.1.0): supported by Python 3.8 | Low | 11/11/2024 |
| v4.0.0 | Here is the release note of [v4.0.0](https://github.com/optuna/optuna/milestone/63?closed=1). Please also check out the [release blog post](https://medium.com/optuna/announcing-optuna-4-0-3325a8420d10). If you want to update the Optuna version of your existing projects to v4.0, please see the [migration guide](https://github.com/optuna/optuna/discussions/5573). We have also published blog posts about the development items. Please check them out! - [OptunaHub, a Feature-Sharing Platform fo | Low | 9/2/2024 |
| v4.0.0-b0 | This is the release note of [v4.0.0-b0](https://github.com/optuna/optuna/milestone/61?closed=1). If you want to update your existing projects from Optuna v3.x to Optuna v4, please see the [migration guide](https://github.com/optuna/optuna/discussions/5573) and try out Optuna v4. # Highlights ## OptunaHub Beta Release The Optuna team released the beta version of OptunaHub, the feature-sharing platform for Optuna. Registered features can be easily implemented on users’ code and contrib | Low | 7/16/2024 |
| v3.6.1 | This is the release note of [v3.6.1](https://github.com/optuna/optuna/milestone/62?closed=1). # Bug Fixes - [Backport] Fix Wilcoxon pruner bug when best_trial has no intermediate value #5370 - [Backport] Address issue#5358 (#5371) - [Backport] Fix `average_is_best` implementation in `WilcoxonPruner` (#5373) # Other - Bump up version number to v3.6.1 (#5372) # Thanks to All the Contributors! This release was made possible by the authors and the people who participated in the r | Low | 4/1/2024 |
| v3.6.0 | This is the release note of [v3.6.0](https://github.com/optuna/optuna/milestone/60?closed=1). # Highlights Optuna 3.6 newly supports the following new features. See [our release blog](https://medium.com/optuna/announcing-optuna-3-6-f5d7efeb5620) for more detailed information. - Wilcoxon Pruner: New Pruner Based on Wilcoxon Signed-Rank Test - Lightweight Gaussian Process (GP)-Based Sampler - Speeding up Importance Evaluation with PED-ANOVA - Stricter Verification Logic for FrozenTrial | Low | 3/18/2024 |
| v3.5.0 | This is the release note of [v3.5.0](https://github.com/optuna/optuna/milestone/59?closed=1). # Highlights This is a maintenance release with various bug fixes and improvements to the documentation and more. # Breaking Changes - Isolate the fast.ai module from optuna (https://github.com/optuna/optuna-integration/pull/49, thanks @sousu4!) - Change `n_objectives` condition to be greater than 4 in candidates functions (#5121, thanks @adjeiv!) # New Features - Support constraints | Low | 12/11/2023 |
| v3.4.0 | This is the release note of [v3.4.0](https://github.com/optuna/optuna/milestone/58?closed=1). # Highlights Optuna 3.4 newly supports the following new features. See [our release blog](https://medium.com/optuna/announcing-optuna-3-4-0087644c92fa) for more detailed information. * Preferential Optimization (Optuna Dashboard) * Optuna Artifact * Jupyter Lab Extension * VS Code Extension * User-defined Distance for Categorical Parameters in TPE * Constrained Optimization Support for V | Low | 10/17/2023 |
| v3.3.0 | This is the release note of [v3.3.0](https://github.com/optuna/optuna/milestone/57?closed=1). # Highlights ## CMA-ES with Learning Rate Adaptation A new variant of CMA-ES has been added. By setting the `lr_adapt` argument to `True` in `CmaEsSampler`, you can utilize it. For multimodal and/or noisy problems, adapting the learning rate can help avoid getting trapped in local optima. For more details, please refer to #4817. We want to thank @nomuramasahir0, one of the authors of LRA-CMA-ES | Low | 8/7/2023 |
| v3.2.0 | This is the release note of [v3.2.0](https://github.com/optuna/optuna/milestone/54?closed=1). # Highlights ## Human-in-the-loop optimization With the latest release, we have incorporated support for human-in-the-loop optimization. It enables an interactive optimization process between users and the optimization algorithm. As a result, it opens up new opportunities for the application of Optuna in tuning Generative AI. For further details, please check out [our human-in-the-loop optimiza | Low | 5/30/2023 |
| v3.1.1 | This is the release note of [v3.1.1](https://github.com/optuna/optuna/milestone/56?closed=1). # Enhancements - [Backport] Import `cmaes` package lazily (#4573) # Bug Fixes - [Backport] Fix botorch dependency (#4569) - [Backport] Fix param_mask for multivariate TPE with constant_liar (#4570) - [Backport] Mitigate a blocking issue while running migrations with SQLAlchemy 2.0 (#4571) - [Backport] Fix bug of CMA-ES with margin on `RDBStorage` or `JournalStorage` (#4572) - [Backport] | Low | 4/6/2023 |
| v3.0.6 | This is the release note of [v3.0.6](https://github.com/optuna/optuna/milestone/55?closed=1). # Installation - Fix a project metadata for scipy version constraint (#4494) # Other - Bump up version number to v3.0.6 (#4493) # Thanks to All the Contributors! This release was made possible by the authors and the people who participated in the reviews and discussions. @c-bata @HideakiImamura | Low | 3/9/2023 |
| v3.1.0 | This is the release note of [v3.1.0](https://github.com/optuna/optuna/milestone/53?closed=1). This is not something you have to read from top to bottom to learn about the summary of Optuna v3.1. The recommended way is reading [the release blog](https://medium.com/optuna/announcing-optuna-3-1-7b4c5fac227c). # Highlights ## New Features ### CMA-ES with Margin > | CMA-ES | CMA-ES with Margin | > | ------- | -------- | > | . # Highlights ## CMA-ES with Margin support > | CMA-ES | CMA-ES with Margin | > | ------- | -------- | > |  |  | > > “The animation is referred from https://github.co | Low | 12/22/2022 |
| v3.0.5 | This is the release note of [v3.0.5](https://github.com/optuna/optuna/milestone/52?closed=1). # Bug Fixes - [Backport] Fix bugs in `constant_liar` option (#4257) # Other - Bump up version number to 3.0.5 (#4256) # Thanks to All the Contributors! This release was made possible by the authors and the people who participated in the reviews and discussions. @HideakiImamura, @eukaryo, @toshihikoyanase | Low | 12/19/2022 |
| v3.0.4 | This is the release note of [v3.0.4](https://github.com/optuna/optuna/milestone/51?closed=1). # Bug Fixes - [Backport] Specify object type to numpy array init to avoid unintended str cast (#4218) # Other - Bump up version to v3.0.4 (#4214) # Thanks to All the Contributors! This release was made possible by the authors and the people who participated in the reviews and discussions. @HideakiImamura, @contramundum53 | Low | 12/1/2022 |
| v3.0.3 | This is the release note of [v3.0.3](https://github.com/optuna/optuna/milestone/50?closed=1). # Enhancements - [Backport] Fix the data migration script for v3 release (#4053) # Bug Fixes - [Backport] Skip to add `intermediate_value_type` and `value_type` columns if exists (#4052) # Installation - Backport #4036 and #4044 to pass tests on `release-v3.0.3` branch (#4043) # Other - Bump up version to v3.0.3 (#4041) # Thanks to All the Contributors! This release was mad | Low | 10/11/2022 |
| v3.0.2 | This is the release note of [v3.0.2](https://github.com/optuna/optuna/milestone/49?closed=1). # Highlights ## Bug fix for DB migration with SQLAlchemy v1.3 In v3.0.0 or v3.0.1, DB migration fails with SQLAlchemy v1.3. We fixed this issue in v3.0.2. ## Removing typing-extensions from dependency In v3.0.0, typing-extensions was used for fine-grained type checking. However, that resulted in import failures when using older versions of typing-extensions. We made the dependency optiona | Low | 9/15/2022 |
| v3.0.1 | This is the release note of [v3.0.1](https://github.com/optuna/optuna/milestone/48?closed=1). # Highlights ## Bug fix for `GridSampler` with RDB In v3.0.0, `GridSampler` with RDB raises an error. This patch fixes this combination. # Bug Fixes - Backport #3957 (#3972) # Others - Bump up version number to v3.0.1 (#3973) # Thanks to All the Contributors! @HideakiImamura, @contramundum53, @not522 This release was made possible by the authors and the people who participa | Low | 9/8/2022 |
| v3.0.0 | This is the release note of [v3.0.0](https://github.com/optuna/optuna/milestone/46?closed=1). This is not something you have to read from top to bottom to learn about the summary of Optuna v3. The recommended way is reading the [release blog](https://medium.com/optuna/optuna-3-part-1-4c6a9022a648). If you want to update your existing projects from Optuna v2.x to Optuna v3, please see the [migration guide](https://github.com/optuna/optuna/discussions/3930) and try out Optuna v3. # Highl | Low | 8/29/2022 |
| v3.0.0-rc0 | This is the release note of [v3.0.0-rc0](https://github.com/optuna/optuna/milestone/44?closed=1). **_This is a release candidate of Optuna V3. We plan to release the major version within a few weeks. Please try this version and report bugs!_** # Highlights ## Constrained Optimization Support for TPE `TPESampler`, the default sampler of Optuna, now supports constrained optimization. It takes a function `constraints_func` as an argument, and examines whether trials are feasible or not. Fea | Low | 8/8/2022 |
| v2.10.1 | This is the release note of [v2.10.1](https://github.com/optuna/optuna/milestone/45?closed=1). This is a patch release to resolve the issues on the document build. No feature updates are included. # Installation - Fix document build of v2.10.1 (#3642) # Documentation - Backport #3590: Replace `youtube.com` with `youtube-nocookie.com` (#3633) # Other - Bump up version to v2.10.1 (#3635) # Thanks to All the Contributors! This release was made possible by the authors | Low | 6/13/2022 |
| v3.0.0-b1 | This is the release note of [v3.0.0-b1](https://github.com/optuna/optuna/milestone/43?closed=1). # Highlights ## A Samplers Comparison Table We added a sampler comparison table on [the samplers' documentation page](https://optuna.readthedocs.io/en/latest/reference/samplers/index.html). It includes supported options (parameter types, pruning, multi-objective optimization, constrained optimization, etc.), time complexity, and recommended budgets for each sampler. Please use this to select a | Low | 6/6/2022 |
| v3.0.0-b0 | This is the release note of [v3.0.0-b0](https://github.com/optuna/optuna/milestone/41?closed=1). # Highlights ## Simplified Distribution Classes: Float, Int and Categorical Search space definitions, which consist of `BaseDistribution` and its child classes in Optuna, are greatly simplified. We have introduced `FloatDistribution`, `IntDistribution`, and `CategoricalDistribution`. If you use the suggest API and `Study.optimize`, the search space information is stored as these three distri | Low | 4/12/2022 |
| v3.0.0-a2 | This is the release note of [v3.0.0-a2](https://github.com/optuna/optuna/milestone/42?closed=1). # Highlights ## `Study.optimize` Warning Configuration Fix This is a small release that fixes a bug that the same warning message was emitted more than once when calling `Study.optimize`. # Bug Fixes - [Backport] Allow `fail_state_trials` show warning when heartbeat is enabled (#3303) # Other - Bump Optuna (#3302) # Thanks to All the Contributors! This release was made poss | Low | 2/14/2022 |
| v3.0.0-a1 | This is the release note of [v3.0.0-a1](https://github.com/optuna/optuna/milestone/40?closed=1). # Highlights Second alpha pre-release in preparation for the upcoming major version update v3. Included are several new features, improved optimization algorithms, removals of deprecated interfaces and many quality of life improvements. _To read about the entire v3 roadmap, please refer to the [Wiki](https://github.com/optuna/optuna/wiki/Optuna-V3-Roadmap)._ _While this is a pre-releas | Low | 2/7/2022 |
| v3.0.0-a0 | This is the release note of [v3.0.0-a0](https://github.com/optuna/optuna/milestone/39?closed=1). # Highlights First alpha pre-release in preparation for the upcoming major version update v3. Included are several new features, improved optimization algorithms, removals of deprecated interfaces and many quality of life improvements. *To read about the entire v3 roadmap, please refer to the [Wiki](https://github.com/optuna/optuna/wiki/Optuna-V3-Roadmap).* *While this is a pre-releas | Low | 12/6/2021 |
| v2.10.0 | This is the release note of [v2.10.0](https://github.com/optuna/optuna/milestone/37?closed=1). # Highlights ## New CLI Subcommand for Analyzing Studies New subcommands `optuna trials`, `optuna best-trial` and `optuna best-trials` have been introduced to Optuna’s CLI for listing trials in studies with RDB storages. It allows direct interaction with trial data from the command line in various formats including human readable tables, JSON or YAML. See the following examples: Show all tr | Low | 10/4/2021 |
| v2.9.1 | This is the release note of [v2.9.1](https://github.com/optuna/optuna/milestone/38?closed=1). # Highlights ## Ask-and-Tell CLI Fix The storage URI and the study name are no longer logged by `optuna ask` and `optuna tell`. The former could contain sensitive information. # Enhancements - Remove storage URI from `ask` and `tell` CLI subcommands (#2838) # Other - Bump to v2.9.1 (#2839) # Thanks to All the Contributors! This release was made possible by the authors and the | Low | 8/3/2021 |
| v2.9.0 | This is the release note of [v2.9.0](https://github.com/optuna/optuna/milestone/36?closed=1). Help us create the next version of Optuna! Please take a few minutes to fill in this survey, and let us know how you use Optuna now and what improvements you'd like. https://forms.gle/TtJuuaqFqtjmbCP67 # Highlights ## Ask-and-Tell CLI: Optuna from the Command Line The built-in CLI which you can use to upgrade storages or check the installed version with `optuna --version`, now provides expe | Low | 8/2/2021 |
| v2.8.0 | This is the release note of [v2.8.0](https://github.com/optuna/optuna/milestone/35?closed=1). ## New Examples Repository The number of Optuna examples has grown as the number of integrations have increased, and we’ve moved them to their own repository: [optuna/optuna-examples](https://github.com/optuna/optuna-examples/). # Highlights ## TPE Sampler Improvements ### Constant Liar for Distributed Optimization In distributed environments, the TPE sampler may sample many points in | Low | 6/7/2021 |
| v2.7.0 | This is the release note for [v2.7.0](https://github.com/optuna/optuna/milestone/33?closed=1). # Highlights ## New `optuna-dashboard` Repository A new dashboard [`optuna-dashboard`](https://github.com/optuna/optuna-dashboard) is being developed in a separate repository under the Optuna organization. Install it with `pip install optuna-dashboard` and run it with `optuna-dashboard $STORAGE_URL`. The previous `optuna dashboard` command is now deprecated. ## Deprecate `n_jobs` Argument | Low | 4/5/2021 |
| v2.6.0 | This is the release note of [v2.6.0](https://github.com/optuna/optuna/milestone/32?closed=1). # Highlights ## Warm Starting CMA-ES and sep-CMA-ES Support Two new CMA-ES variants are available. Warm starting CMA-ES enables transferring prior knowledge on similar tasks. More specifically, CMA-ES can be initialized based on existing results of similar tasks. sep-CMA-ES is an algorithm which constrains the covariance matrix to be diagonal and is suitable for separable objective functions. S | Low | 3/8/2021 |
| v2.5.0 | This is the release note of [v2.5.0](https://github.com/optuna/optuna/milestone/31?closed=1). # Highlights ## Ask-and-Tell The ask-and-tell interface is a new complement to `Study.optimize`. It allows users to construct `Trial` instances without the need of an objective function callback, giving more flexibility in how to define search spaces, ask for suggested hyperparameters and how to evaluate objective functions. The interface is made out of two methods, `Study.ask` and `Study.tell | Low | 2/1/2021 |
| v2.4.0 | This is the release note of [v2.4.0](https://github.com/optuna/optuna/milestone/30?closed=1). # Highlights ## Python 3.9 Support This is the first version to officially support Python 3.9. Everything is tested with the exception of certain integration modules under `optuna.integration`. We will continue to extend the support in the coming releases. ## Multi-objective Optimization Multi-objective optimization in Optuna is now a stable first-class citizen. Multi-objective optimizati | Low | 1/12/2021 |
| v2.3.0 | This is the release note of [v2.3.0](https://github.com/optuna/optuna/milestone/29?closed=1). # Highlights ## Multi-objective TPE sampler TPE sampler now supports multi-objective optimization. This new algorithm is implemented in `optuna.multi_objective` and used via`optuna.multi_objective.samplers.MOTPEMultiObjectiveSampler`. See #1530 for the details. . In this release, we drop support for Python 3.5. If you are using Python 3.5, please consider upgrading your Python environment to Python 3.6 or newer, or install older versions of Optuna. # Highlights ## Multivariate TPE sampler `TPESampler` is updated with an experimental option to enable multivariate sampling. This algorithm captures dependencies among hyperparameters better than the prev | Low | 10/5/2020 |
| v2.1.0 | This is the release note of [v2.1.0](https://github.com/optuna/optuna/milestone/27?closed=1). *Optuna v2.1.0 will be the last version to support Python 3.5. See #1067.* # Highlights ## Allowing `objective(study.best_trial)` `FrozenTrial` used to subclass `object` but now implements `BaseTrial`. It can be used in places where a `Trial` is expected, including user-defined objective functions. Re-evaluating the objective functions with the best parameter configuration is now straight | Low | 9/7/2020 |
| v2.0.0 | This is the release note of [v2.0.0](https://github.com/optuna/optuna/milestone/26?closed=1). # Highlights The second major version of Optuna 2.0 is released. It accommodates a multitude of new features, including Hyperband pruning, hyperparameter importance, built-in CMA-ES support, grid sampler, and LightGBM integration. Storage access is also improved, significantly speeding up optimization. Documentation has been revised and navigation is made easier. See the [blog](https://medium.com/ | Low | 7/29/2020 |
| v2.0.0-rc0 | A release candidate for the second major version of Optuna [v2.0.0-rc0](https://github.com/optuna/optuna/milestone/25?closed=1) is released! This release includes a lot of new features, cleaned up interfaces, performance improvements, internal refactorings and more. If you find any problems with this release candidate, please feel free to report them via GitHub Issues or Gitter. # Highlights ## Hyperband Pruner The stable version of `HyperbandPruner` is available. It has a more simple i | Low | 7/6/2020 |
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