Year: 2025
Type: preprint
Abstract: Black-box optimization (BBO) underpins advances in domains such as AutoML and Materials Informatics, yet implementations of algorithms and benchmarks remain fragmented across research communities. We introduce OptunaHub (https://hub.optuna.org/), a community-oriented, decentralized platform for distributing BBO components under a unified Optuna-compatible interface. OptunaHub enables independent publication, discovery, and reuse of optimization algorithms and benchmark problems through a lightweight Python module, a contributor-driven registry, and a searchable web interface. The source code is publicly available in the \href{https://github.com/optuna/optunahub}{\texttt{optunahub}}, \href{https://github.com/optuna/optunahub-registry}{\texttt{optunahub-registry}}, and \href{https://github.com/optuna/optunahub-web}{\texttt{optunahub-web}} repositories under the Optuna organization on GitHub (https://github.com/optuna/).
Source: arXiv (Cornell University)
Institution: Preferred Networks (Japan)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416369524