Sourced from optuna's releases.

v4.4.0

This is the release note of v4.4.0.

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.

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. Of course, other LLM clients like VSCode or Cline can also be used similarly. You can also access it via Docker. If you want to persist the results, you can use the — storage option. For details, please refer to the repository.

{
  "mcpServers": {
    … (Other MCP Servers' settings)
    "Optuna": {
      "command": "uvx",
      "args": [
        "optuna-mcp"
      ]
    }
  }
}

Gaussian Process-Based Multi-objective Optimization

Optuna’s GPSampler, introduced in version 3.6, offers superior speed and performance compared to existing Bayesian optimization frameworks, particularly when handling objective functions with discrete variables. In Optuna v4.4, we have extended this GPSampler to support multi-objective optimization problems. The applications of multi-objective optimization are broad, and the new multi-objective capabilities introduced in this GPSampler are expected to find applications in fields such as material design, experimental design problems, and high-cost hyperparameter optimization.

GPSampler can be easily integrated into your program and performs well against the existing BoTorchSampler. We encourage you to try it out with your multi-objective optimization problems.

sampler = optuna.samplers.GPSampler()
study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)

New Features in OptunaHub

During the development period of Optuna v4.4, several new features were also introduced to OptunaHub, the feature-sharing platform for Optuna:

• A sampler utilizing Google Vizier is now newly available.
• The CMA-ES-based sampler with restart strategy, which was previously part of the Optuna core, has been migrated to OptunaHub, making it simpler and more user-friendly.
• A benchmark problem solving aircraft design as a black-box optimization task has been added, further enhancing the convenience of algorithm development using OptunaHub.
• A visualization feature has also been added, allowing users to see how the acquisition function of the default TPESampler evolves as trials progress.
• A novel mutation operation has been added to the MOEA/D evolutionary computation algorithm for multi-objective optimization.

| Vizier sampler performance |

... (truncated)

• 0742587 Merge pull request #6148 from HideakiImamura/bump-version-4.4
• becfc27 Bump up version number to v4.4.0
• 5ccdf30 Merge pull request #6145 from c-bata/fix-blackdoc-checks
• f32571d [hotfix] Add version constraint on blackdoc at reviewdog.yml
• 9622bb8 [hotfix] Add version constraint on blackdoc
• cad5190 Merge pull request #6143 from Shubham05122002/refactor-return-tuple-style-mod...
• 42daf94 Refactor return statement style in optuna/storages/_rdb/models.py for consist...
• b0f1482 Merge pull request #6141 from saishreyakumar/fix-return-tuple-test-trial
• 6f528b9 Merge pull request #6139 from Prashantdhaka23/fix-return-tuple
• 59559cf Merge upstream/master into fix-return-tuple-test-trial
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