Optuna is a next-generation hyperparameter optimization framework that introduces new design criteria for efficient and flexible optimization software. The framework allows users to dynamically construct the parameter search space through a define-by-run API, which enables the user to specify the search space during runtime rather than in advance. This approach allows for more flexible and adaptable optimization strategies. Optuna also features efficient sampling and pruning strategies, which help in reducing the computational cost of the optimization process. The framework is designed to be versatile and easy to set up, supporting a wide range of applications, from lightweight experiments to large-scale distributed computing. Optuna's define-by-run API is particularly significant as it allows users to dynamically generate hyperparameters for each trial, making it easier to handle complex and diverse parameter spaces. This is demonstrated through examples where Optuna's API is used to optimize the architecture of neural networks and the hyperparameters of stochastic gradient descent. The framework also supports distributed computing, enabling parallel processing of multiple trials, which is crucial for large-scale optimization tasks. In terms of efficiency, Optuna incorporates advanced sampling and pruning algorithms. The pruning algorithm, based on the Asynchronous Successive Halving (ASHA) method, allows for early termination of unpromising trials, significantly reducing the computational cost. The framework also supports various storage backends, making it easy to deploy in different environments, including local machines and distributed systems. Optuna has been evaluated against other hyperparameter optimization frameworks, demonstrating its effectiveness in terms of performance and efficiency. The framework has been successfully applied in real-world scenarios, including machine learning projects and benchmarking tasks. Its open-source nature allows for continuous improvement and integration with the broader community, making it a valuable tool for the development of next-generation optimization frameworks.Optuna is a next-generation hyperparameter optimization framework that introduces new design criteria for efficient and flexible optimization software. The framework allows users to dynamically construct the parameter search space through a define-by-run API, which enables the user to specify the search space during runtime rather than in advance. This approach allows for more flexible and adaptable optimization strategies. Optuna also features efficient sampling and pruning strategies, which help in reducing the computational cost of the optimization process. The framework is designed to be versatile and easy to set up, supporting a wide range of applications, from lightweight experiments to large-scale distributed computing. Optuna's define-by-run API is particularly significant as it allows users to dynamically generate hyperparameters for each trial, making it easier to handle complex and diverse parameter spaces. This is demonstrated through examples where Optuna's API is used to optimize the architecture of neural networks and the hyperparameters of stochastic gradient descent. The framework also supports distributed computing, enabling parallel processing of multiple trials, which is crucial for large-scale optimization tasks. In terms of efficiency, Optuna incorporates advanced sampling and pruning algorithms. The pruning algorithm, based on the Asynchronous Successive Halving (ASHA) method, allows for early termination of unpromising trials, significantly reducing the computational cost. The framework also supports various storage backends, making it easy to deploy in different environments, including local machines and distributed systems. Optuna has been evaluated against other hyperparameter optimization frameworks, demonstrating its effectiveness in terms of performance and efficiency. The framework has been successfully applied in real-world scenarios, including machine learning projects and benchmarking tasks. Its open-source nature allows for continuous improvement and integration with the broader community, making it a valuable tool for the development of next-generation optimization frameworks.