Method minimize

nevergrad/optimization/base.py:611–719

Optimization (minimization) procedure Parameters ---------- objective_function: callable A callable to optimize (minimize) executor: Executor An executor object, with method :code:`submit(callable, *args, **kwargs)` and returning a Future-like

(
        self,
        objective_function: tp.Callable[..., tp.Loss],
        executor: tp.Optional[tp.ExecutorLike] = None,
        batch_mode: bool = False,
        verbosity: int = 0,
        constraint_violation: tp.Any = None,
    )

        """

    def minimize(
        self,
        objective_function: tp.Callable[..., tp.Loss],
        executor: tp.Optional[tp.ExecutorLike] = None,
        batch_mode: bool = False,
        verbosity: int = 0,
        constraint_violation: tp.Any = None,
    ) -> p.Parameter:
        """Optimization (minimization) procedure

        Parameters
        ----------
        objective_function: callable
            A callable to optimize (minimize)
        executor: Executor
            An executor object, with method :code:`submit(callable, *args, **kwargs)` and returning a Future-like object
            with methods :code:`done() -> bool` and :code:`result() -> float`. The executor role is to dispatch the execution of
            the jobs locally/on a cluster/with multithreading depending on the implementation.
            Eg: :code:`concurrent.futures.ProcessPoolExecutor`
        batch_mode: bool
            when :code:`num_workers = n > 1`, whether jobs are executed by batch (:code:`n` function evaluations are launched,
            we wait for all results and relaunch n evals) or not (whenever an evaluation is finished, we launch
            another one)
        verbosity: int
            print information about the optimization (0: None, 1: fitness values, 2: fitness values and recommendation)
        constraint_violation: list of functions or None
            each function in the list returns >0 for a violated constraint.

        Returns
        -------
        ng.p.Parameter
            The candidate with minimal value. :code:`ng.p.Parameters` have field :code:`args` and :code:`kwargs` which can
            be directly used on the function (:code:`objective_function(*candidate.args, **candidate.kwargs)`).

        Note
        ----
        for evaluation purpose and with the current implementation, it is better to use batch_mode=True
        """
        # pylint: disable=too-many-branches
        if self.budget is None:
            raise ValueError("Budget must be specified")
        if executor is None:
            executor = utils.SequentialExecutor()  # defaults to run everything locally and sequentially
            if self.num_workers > 1:
                self._warn(
                    f"num_workers = {self.num_workers} > 1 is suboptimal when run sequentially",
                    errors.InefficientSettingsWarning,
                )
        assert executor is not None
        tmp_runnings: tp.List[tp.Tuple[p.Parameter, tp.JobLike[tp.Loss]]] = []
        tmp_finished: tp.Deque[tp.Tuple[p.Parameter, tp.JobLike[tp.Loss]]] = deque()
        # go
        sleeper = ngtools.Sleeper()  # manages waiting time depending on execution time of the jobs
        remaining_budget = self.budget - self.num_ask
        first_iteration = True
        #
        while remaining_budget or self._running_jobs or self._finished_jobs:
            # # # # # Update optimizer with finished jobs # # # # #