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

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

.. autoapi-nested-parse::

   Differentiable Black-box optimization function



Attributes
----------

.. autoapisummary::

   pyepo.func.jax.blackbox.DBB
   pyepo.func.jax.blackbox.NID


Classes
-------

.. autoapisummary::

   pyepo.func.jax.blackbox.blackboxOpt
   pyepo.func.jax.blackbox.negativeIdentity


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

.. py:class:: blackboxOpt(optmodel, lambd=10, processes=1, solve_ratio=1.0, dataset=None)

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


   Differentiable Black-Box Optimizer (DBB) -- gradient via solution interpolation.

   Replaces the solver's zero gradient with an interpolation estimate: for an
   upstream gradient :math:`\mathbf{d}`, the vector-Jacobian product is
   :math:`(\mathbf{w}^*(\hat{\mathbf{c}} + \lambda \mathbf{d}) -
   \mathbf{w}^*(\hat{\mathbf{c}})) / \lambda`. Larger ``lambd`` smooths
   more (recommended 10-20).

   Reference: Vlastelica et al. (2019) `<https://arxiv.org/abs/1912.02175>`_


   .. py:attribute:: lambd


   .. py:method:: forward(pred_cost)

      Forward pass



.. py:class:: negativeIdentity(optmodel, processes=1, solve_ratio=1.0, dataset=None)

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


   Negative Identity Backpropagation (NID) -- hyperparameter-free DBB.

   Treats the solver Jacobian as a signed identity:
   :math:`\partial \mathbf{w}^* / \partial \hat{\mathbf{c}} \approx
   -\mathbf{I}` for minimization (and :math:`+\mathbf{I}` for maximization),
   a straight-through gradient estimator needing no extra solve.

   Reference: Sahoo et al. (2022) `<https://arxiv.org/abs/2205.15213>`_


   .. py:method:: forward(pred_cost)

      Forward pass



.. py:data:: DBB

.. py:data:: NID