bluemira.optimisation._nlopt.functions
======================================

.. py:module:: bluemira.optimisation._nlopt.functions


Attributes
----------

.. autoapisummary::

   bluemira.optimisation._nlopt.functions._FloatOrArrayT


Classes
-------

.. autoapisummary::

   bluemira.optimisation._nlopt.functions.ConstraintType
   bluemira.optimisation._nlopt.functions._NloptFunction
   bluemira.optimisation._nlopt.functions.ObjectiveFunction
   bluemira.optimisation._nlopt.functions.Constraint


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

.. py:data:: _FloatOrArrayT

.. py:class:: ConstraintType(*args, **kwds)

   Bases: :py:obj:`enum.Enum`

   .. autoapi-inheritance-diagram:: bluemira.optimisation._nlopt.functions.ConstraintType
      :parts: 1
      :private-bases:


   Enumeration of constraint types.


   .. py:attribute:: EQUALITY


   .. py:attribute:: INEQUALITY


.. py:class:: _NloptFunction(f: collections.abc.Callable[[numpy.ndarray], _FloatOrArrayT], bounds: tuple[_FloatOrArrayT, _FloatOrArrayT])

   Base class for NLOpt objective/constraint functions.

   Implements the ability to calculate a numerical gradient, if an
   analytical one is not provided.


   .. py:attribute:: f


   .. py:attribute:: f0
      :type:  float | numpy.ndarray
      :value: 0



   .. py:attribute:: bounds


   .. py:method:: _approx_derivative(x: numpy.ndarray) -> numpy.ndarray

      :returns: The approximated derivative of the function at `x`.



   .. py:method:: set_approx_derivative_lower_bound(lower_bound: numpy.ndarray) -> None

      Set the lower bounds for use in derivative approximation.



   .. py:method:: set_approx_derivative_upper_bound(upper_bound: numpy.ndarray) -> None

      Set the upper bounds for use in derivative approximation.



.. py:class:: ObjectiveFunction(f: bluemira.optimisation.typed.ObjectiveCallable, df: bluemira.optimisation.typed.OptimiserCallable | None, n_variables: int, bounds: tuple[_FloatOrArrayT, _FloatOrArrayT] = (-np.inf, np.inf))

   Bases: :py:obj:`_NloptFunction`

   .. autoapi-inheritance-diagram:: bluemira.optimisation._nlopt.functions.ObjectiveFunction
      :parts: 1
      :private-bases:


   Holds an objective function for an NLOpt optimiser.

   Adapts the given objective function, and optional derivative, to a
   form understood by NLOpt.

   If no optimiser derivative is given, and the algorithm is gradient
   based, a numerical approximation of the gradient is calculated.


   .. py:attribute:: f
      :type:  bluemira.optimisation.typed.ObjectiveCallable


   .. py:attribute:: f0
      :type:  float


   .. py:attribute:: df


   .. py:attribute:: history
      :type:  list[tuple[numpy.ndarray, float]]
      :value: []



   .. py:attribute:: prev_iter


   .. py:method:: call(x: numpy.ndarray, grad: numpy.ndarray) -> float

      Execute the NLOpt objective function.

      :returns: The value of the objective function at `x`.



   .. py:method:: call_with_history(x: numpy.ndarray, grad: numpy.ndarray) -> float

      Execute the NLOpt objective function, recording the iteration history.

      :returns: The value of the objective function at `x`,
                with the iteration history recorded.



   .. py:method:: _call_inner(x: numpy.ndarray, grad: numpy.ndarray) -> float

      Execute the objective function in the form required by NLOpt.

      :returns: The value of the objective function at `x`.



   .. py:method:: _store_x(x: numpy.ndarray) -> None

      Store ``x`` in ``self.prev_iter``.



.. py:class:: Constraint(constraint_type: ConstraintType, f: bluemira.optimisation.typed.OptimiserCallable, tolerance: numpy.ndarray, df: bluemira.optimisation.typed.OptimiserCallable | None = None, bounds: tuple[_FloatOrArrayT, _FloatOrArrayT] = (-np.inf, np.inf))

   Bases: :py:obj:`_NloptFunction`

   .. autoapi-inheritance-diagram:: bluemira.optimisation._nlopt.functions.Constraint
      :parts: 1
      :private-bases:


   Holder for NLOpt constraint functions.


   .. py:attribute:: f
      :type:  bluemira.optimisation.typed.OptimiserCallable


   .. py:attribute:: f0
      :type:  numpy.ndarray


   .. py:attribute:: constraint_type


   .. py:attribute:: tolerance


   .. py:attribute:: df


   .. py:attribute:: history
      :type:  list[tuple[numpy.ndarray, Ellipsis]]
      :value: []



   .. py:method:: call(result: numpy.ndarray, x: numpy.ndarray, grad: numpy.ndarray) -> None

      Execute the constraint function in the form required by NLOpt.

      https://nlopt.readthedocs.io/en/latest/NLopt_Python_Reference/#vector-valued-constraints



   .. py:method:: call_with_history(result: numpy.ndarray, x: numpy.ndarray, grad: numpy.ndarray) -> None