pyepo.func.blackbox
===================

.. py:module:: pyepo.func.blackbox

.. autoapi-nested-parse::

   Differentiable Black-box optimization function



Classes
-------

.. autoapisummary::

   pyepo.func.blackbox.blackboxOpt
   pyepo.func.blackbox.blackboxOptFunc
   pyepo.func.blackbox.negativeIdentity
   pyepo.func.blackbox.negativeIdentityFunc


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

.. py:class:: blackboxOpt(optmodel, lambd=10, processes=1, solve_ratio=1, dataset=None)

   Bases: :py:obj:`pyepo.func.abcmodule.optModule`


   An autograd module for differentiable black-box optimizer, which yield
   an optimal solution and derive a gradient.

   For differentiable block-box, the objective function is linear and
   constraints are known and fixed, but the cost vector needs to be predicted
   from contextual data.

   The block-box approximates the gradient of the optimizer by interpolating
   the loss function. Thus, it allows us to design an algorithm based on
   stochastic gradient descent.

   Reference: <https://arxiv.org/abs/1912.02175>


   .. py:attribute:: lambd
      :value: 10



   .. py:attribute:: dbb


   .. py:method:: forward(pred_cost)

      Forward pass



.. py:class:: blackboxOptFunc(*args, **kwargs)

   Bases: :py:obj:`torch.autograd.Function`


   A autograd function for differentiable black-box optimizer


   .. py:method:: forward(ctx, pred_cost, module)
      :staticmethod:


      Forward pass for DBB

      :param pred_cost: a batch of predicted values of the cost
      :type pred_cost: torch.tensor
      :param module: blackboxOpt module
      :type module: optModule

      :returns: predicted solutions
      :rtype: torch.tensor



   .. py:method:: backward(ctx, grad_output)
      :staticmethod:


      Backward pass for DBB



.. py:class:: negativeIdentity(optmodel, processes=1, solve_ratio=1, dataset=None)

   Bases: :py:obj:`pyepo.func.abcmodule.optModule`


   An autograd module for the differentiable optimizer, which yields optimal a
   solution and use negative identity as a gradient on the backward pass.

   For negative identity backpropagation, the objective function is linear and
   constraints are known and fixed, but the cost vector needs to be predicted
   from contextual data.

   If the interpolation hyperparameter λ aligns with an appropriate step size,
   then the identity update is equivalent to DBB. However, the identity update
   does not require an additional call to the solver during the backward pass
   and tuning an additional hyperparameter λ.

   Reference: <https://arxiv.org/abs/2205.15213>


   .. py:attribute:: nid


   .. py:method:: forward(pred_cost)

      Forward pass



.. py:class:: negativeIdentityFunc(*args, **kwargs)

   Bases: :py:obj:`torch.autograd.Function`


   A autograd function for differentiable black-box optimizer


   .. py:method:: forward(ctx, pred_cost, module)
      :staticmethod:


      Forward pass for NID

      :param pred_cost: a batch of predicted values of the cost
      :type pred_cost: torch.tensor
      :param module: blackboxOpt module
      :type module: optModule

      :returns: predicted solutions
      :rtype: torch.tensor



   .. py:method:: backward(ctx, grad_output)
      :staticmethod:


      Backward pass for NID