Toshihiko Yanase

Takuya Akiba, Shotaro Sano, Toshihiko Yanase et al.

The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).

Yoshihiko Ozaki, Shuhei Watanabe, Toshihiko Yanase

Black-box optimization (BBO) drives advances in domains such as AutoML and Materials Informatics, yet research efforts often remain fragmented across domains. We introduce OptunaHub (https://hub.optuna.org/), a community platform that centralizes BBO methods and benchmarks. OptunaHub provides unified Python APIs, a contributor package registry, and a web interface to promote searchability and cross-domain research. OptunaHub aims to foster a virtuous cycle of contributions and applications. The source code is publicly available in the optunahub, optunahub-registry, and optunahub-web repositories under the Optuna organization on GitHub (https://github.com/optuna/).

Akira Hatakeyama, Shota Ito, Toshihiko Yanase et al.

Two-point boundary value problems for cislunar trajectories present significant challenges in circler restricted three body problem, making traditional analytical methods like Lambert's problem inapplicable. This study proposes a novel approach using a prefixed patch time series Transformer model that automates the solution of two-point boundary value problems from lunar flyby to arbitrary terminal conditions. Using prefix tokens of terminal conditions in our deep generative model enables solving boundary value problems in three-body dynamics. The training dataset consists of trajectories obtained through forward propagation rather than solving boundary value problems directly. The model demonstrates potential practical utility for preliminary trajectory design in cislunar mission scenarios.