.. _AcquisitionOptimizer:


Acquisition optimizer
---------------------


The module optimizes the acquisition function  of the main objective of the study by a combination of a  heuristic global optimization followed by a local convergence  of the best result.

.. _ActiveLearning.acquisition_optimizer.num_initial_samples:


num_initial_samples (int)
"""""""""""""""""""""""""
   Number of initial samples for maximizing  the acquisition function. 

   Default: Automatic choice depending on the dimensionality of the parameter space and the required time for the client to return an observation for a given suggestion.

.. _ActiveLearning.acquisition_optimizer.max_num_model_evals:


max_num_model_evals (int)
"""""""""""""""""""""""""
   Maximum number of evaluations of the surrogates for finding the maximum of the acquisition function. 

   Default: Automatic choice depending on the dimensionality of the parameter space and the required time for the client to return an observation for a given suggestion.

.. _ActiveLearning.acquisition_optimizer.adaptive_local_search:


adaptive_local_search (bool)
""""""""""""""""""""""""""""
   The maximization of the acquisition function consists of a global heuristic search followed by a local convergence of the best samples. By default, the local search effort adapts to the value of ``max_num_model_evals``. In some cases far better samples are computed, if the local search is only stopped when convergence to a local minimum was obtained. 

   Default: ``True``

.. _ActiveLearning.acquisition_optimizer.num_training_samples:


num_training_samples (int)
""""""""""""""""""""""""""
   Number of pseudo-random initial samples before the samples are drawn according to the acquisition function. 

   Default: Automatic choice depending depending on dimensionality of design space.

.. _ActiveLearning.acquisition_optimizer.compute_suggestion_in_advance:


compute_suggestion_in_advance (bool)
""""""""""""""""""""""""""""""""""""
   If true, a suggestion is computed in advance while waiting for the next request to speed up the provision of new suggestions.  The pre-computation is only used if the mismatch to the current environment of the pre-computed sample is small. 

   Default: ``True``