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This sampler performs exhaustive search on the defined search space. Example: .. testcode:: import optuna def objective(trial): c = trial.suggest_categorical("c", ["float", "int"]) if c == "float": return trial.suggest_float("x", 1, 3, step=0.5) elif c == "int": a = trial.suggest_int("a", 1, 3) b = trial.suggest_int("b", a, 3) return a + b study = optuna.create_study(sampler=optuna.samplers.BruteForceSampler()) study.optimize(objective) Note: The defined search space must be finite. Therefore, when using :class:`~optuna.distributions.FloatDistribution` or :func:`~optuna.trial.Trial.suggest_float`, ``step=None`` is not allowed. Note: The sampler may fail to try the entire search space in when the suggestion ranges or parameters are changed in the same :class:`~optuna.study.Study`. Args: seed: A seed to fix the order of trials as the search order randomly shuffled. Please note that it is not recommended using this option in distributed optimization settings since this option cannot ensure the order of trials and may increase the number of duplicate suggestions during distributed optimization. avoid_premature_stop: If :obj:`True`, the sampler performs a strict exhaustive search. Please note that enabling this option may increase the likelihood of duplicate sampling. When this option is not enabled (default), the sampler applies a looser criterion for determining when to stop the search, which may result in incomplete coverage of the search space. For more information, see https://github.com/optuna/optuna/issues/5780. 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