simopt.solvers.aloe
===================

.. py:module:: simopt.solvers.aloe

.. autoapi-nested-parse::

   Stochastic line search algorithm with gradient estimation.

   The solver is a stochastic line search algorithm  with the gradient estimate recomputed
   in each iteration, whether or not a step is accepted. The algorithm includes the
   relaxation of the Armijo condition by an additive constant. A detailed description of
   the solver can be found `here <https://simopt.readthedocs.io/en/latest/aloe.html>`__.





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

.. py:class:: ALOEConfig

   Bases: :py:obj:`simopt.base.SolverConfig`


   Configuration for ALOE solver.


   .. py:attribute:: r
      :type:  Annotated[int, Field(default=30, gt=0, description='number of replications taken at each solution')]


   .. py:attribute:: theta
      :type:  Annotated[float, Field(default=0.2, gt=0, lt=1, description='constant in the Armijo condition')]


   .. py:attribute:: gamma
      :type:  Annotated[float, Field(default=0.8, gt=0, lt=1, description='constant for shrinking the step size')]


   .. py:attribute:: alpha_max
      :type:  Annotated[int, Field(default=10, gt=0, description='maximum step size')]


   .. py:attribute:: alpha_0
      :type:  Annotated[int, Field(default=1, gt=0, description='initial step size')]


   .. py:attribute:: epsilon_f
      :type:  Annotated[int, Field(default=1, gt=0, description='additive constant in the Armijo condition')]


   .. py:attribute:: sensitivity
      :type:  Annotated[float, Field(default=1e-07, gt=0, description='shrinking scale for variable bounds')]


   .. py:attribute:: lambda_
      :type:  Annotated[int, Field(default=2, gt=0, description='magnifying factor for n_r in the finite difference function', alias='lambda')]


.. py:class:: ALOE(name: str = '', fixed_factors: dict | None = None)

   Bases: :py:obj:`simopt.base.Solver`


   Adaptive Line-search with Oracle Estimations.

   Initialize a solver object.

   :param name: Name of the solver. Defaults to an empty string.
   :type name: str, optional
   :param fixed_factors: Dictionary of user-specified solver
                         factors. Defaults to None.
   :type fixed_factors: dict | None, optional


   .. py:attribute:: name
      :type:  str
      :value: 'ALOE'



   .. py:attribute:: config_class
      :type:  ClassVar[type[simopt.base.SolverConfig]]

      Configuration class for the solver.


   .. py:attribute:: class_name_abbr
      :type:  ClassVar[str]
      :value: 'ALOE'


      Short name of the solver class.


   .. py:attribute:: class_name
      :type:  ClassVar[str]
      :value: 'ALOE'


      Long name of the solver class.


   .. py:attribute:: objective_type
      :type:  ClassVar[simopt.base.ObjectiveType]

      Description of objective types.


   .. py:attribute:: constraint_type
      :type:  ClassVar[simopt.base.ConstraintType]

      Description of constraint types.


   .. py:attribute:: variable_type
      :type:  ClassVar[simopt.base.VariableType]

      Description of variable types.


   .. py:attribute:: gradient_needed
      :type:  ClassVar[bool]
      :value: False


      True if gradient of objective function is needed, otherwise False.


   .. py:method:: solve(problem: simopt.base.Problem) -> None

      Run a single macroreplication of a solver on a problem.

      :param problem: Simulation-optimization problem to solve.
      :type problem: Problem

      :returns:     - list [Solution]: List of solutions recommended throughout the budget.
                    - list [int]: List of intermediate budgets when recommended solutions
                        change.
      :rtype: tuple