``NLOpt_Algorithm``
==========================================================

.. py:module:: optimeed.optimize.optiAlgorithms.NLOpt_Algorithm


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   optimeed.optimize.optiAlgorithms.NLOpt_Algorithm.ConvergenceManager
   optimeed.optimize.optiAlgorithms.NLOpt_Algorithm.NLOpt_Algorithm




.. py:class:: ConvergenceManager


   .. py:method:: add_point(newObj)


   .. py:method:: set_pop_size(popSize)



.. py:class:: NLOpt_Algorithm


   Bases: :py:obj:`optimeed.optimize.optiAlgorithms.algorithmInterface.AlgorithmInterface`, :py:obj:`optimeed.core.Option_class`

   Interface for the optimization algorithm

   .. py:attribute:: ALGORITHM
      :value: 0

      

   .. py:attribute:: POPULATION_SIZE
      :value: 1

      

   .. py:method:: initialize(initialVectorGuess, listOfOptimizationVariables)

      This function is called once parameters can't be changed anymore, before "get_convergence".

      :param initialVectorGuess: list of variables that describe the initial individual
      :param listOfOptimizationVariables: list of :class:`optimeed.optimize.optiVariable.OptimizationVariable`
      :return:


   .. py:method:: compute()

      Launch the optimization

      :return: vector of optimal variables


   .. py:method:: __set_optimizationVariables(listOfOptimizationVariables, theOptimizationAlgorithm)
      :staticmethod:


   .. py:method:: set_evaluationFunction(evaluationFunction, callback_on_evaluation, numberOfObjectives, numberOfConstraints, array_evaluator)

      Set the evaluation function and all the necessary callbacks

      :param evaluationFunction: check :meth:`~optimeed.optimize.optimizer.evaluateObjectiveAndConstraints`
      :param callback_on_evaluation: check :meth:`~optimeed.optimize.optimizer.callback_on_evaluation`. Call this function after performing the evaluation of the individuals
      :param numberOfObjectives: int, number of objectives
      :param numberOfConstraints: int, number of constraints
      :param array_evaluator: If True, evaluate each generation at once using numpy array. Use it only with care, as it dismisses some features (expert mode)


   .. py:method:: set_maxtime(maxTime)

      Set maximum optimization time (in seconds)


   .. py:method:: __str__()

      Return str(self).


   .. py:method:: get_convergence()

      Get the convergence of the optimization

      :return: :class:`~optimeed.optimize.optiAlgorithms.convergence.interfaceConvergence.InterfaceConvergence`