build(deps): bump optuna from 4.0.0 to 4.1.0

[dependabot]

Bumps optuna from 4.0.0 to 4.1.0.

Release notes

Sourced from optuna's releases.

v4.1.0

This is the release note of v4.1.0. 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: supported by Python 3.8 - 3.13
• Optuna Integration 4.1: supported by Python 3.8 - 3.12
• Optuna Dashboard 0.17.0: supported by Python 3.8 - 3.13

Highlights

AutoSampler: Automatic Selection of Optimization Algorithms

AutoSampler automatically selects a sampler from those implemented in Optuna, depending on the situation. Using AutoSampler, as in the code example below, users can achieve optimization performance equal to or better than Optuna's default without being aware of which optimization algorithm to use.

$ pip install optunahub cmaes torch scipy

import optuna
import optunahub
auto_sampler_module = optunahub.load_module("samplers/auto_sampler")
study = optuna.create_study(sampler=auto_sampler_module.AutoSampler())

See the Medium blog post for details.

Enhanced RDB Storage Backend

This release incorporates comprehensive performance tuning on Optuna’s RDBStorage, leading to significant performance improvements. The table below shows the comparison results of execution times between versions 4.0 and 4.1.

# trials	v4.0.0	v4.1.0	Diff
1000	72.461 sec (±1.026)	59.706 sec (±1.216)	-17.60%
10000	1153.690 sec (±91.311)	664.830 sec (±9.951)	-42.37%
50000	12118.413 sec (±254.870)	4435.961 sec (±190.582)	-63.39%

For fair comparison, all experiments were repeated 10 times, and the mean execution time was compared. Additional detailed benchmark settings include the following:

• Objective Function: Each trial consists of 10 parameters and 10 user attributes
• Storage: MySQL 8.0 (with PyMySQL)
• Sampler: RandomSampler
• Execution Environment: Kubernetes Pod with 5 cpus and 8Gi RAM

... (truncated)

Commits

• f8e1e6d Merge pull request #5751 from c-bata/bump-up-to-v4.1.0
• 604d45e Bump the version up to v4.1.0
• d9327ca Merge pull request #5741 from porink0424/fix/introduce-upsert-in-set_trial_sy...
• 2af7c61 Resolve a small linting problem
• d834afb Merge pull request #5745 from y0z/feature/update-docs
• d44be32 Merge pull request #5744 from nzw0301/update-actions/upload-artifact
• 5d678de Merge upstream
• 4689713 Add link to the autosampler article
• 8b648e9 Update docs/source/reference/samplers/index.rst
• 3eadc92 Add information about AutoSampler to the docs.
• Additional commits viewable in compare view

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Summary by Sourcery

Build:

• Bump Optuna dependency from version 4.0.0 to 4.1.0.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR updates the Optuna dependency from version 4.0.0 to 4.1.0. The new version introduces significant improvements including an AutoSampler for automatic algorithm selection, enhanced RDB storage performance, and support for Python 3.13.

No diagrams generated as the changes look simple and do not need a visual representation.

File-Level Changes

Change	Details	Files
Dependency version update in package configuration	Update Optuna version from 4.0.0 to 4.1.0 Add support for Python 3.13 Improve RDB storage performance by up to 63% Add new AutoSampler feature for automatic optimization algorithm selection	poetry.lock

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