pyepo.func.jax.regularized
==========================

.. py:module:: pyepo.func.jax.regularized

.. autoapi-nested-parse::

   Regularized differentiable optimization function (L2 Frank-Wolfe)



Attributes
----------

.. autoapisummary::

   pyepo.func.jax.regularized.RFWO
   pyepo.func.jax.regularized.RFY


Classes
-------

.. autoapisummary::

   pyepo.func.jax.regularized.regularizedFrankWolfeOpt
   pyepo.func.jax.regularized.regularizedFrankWolfeFenchelYoung


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

.. py:class:: regularizedFrankWolfeOpt(optmodel, lambd=1.0, max_iter=10000, tol=1e-06, processes=1, solve_ratio=1.0, dataset=None)

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


   L2-Regularized Frank-Wolfe Optimizer (RFWO) -- differentiable smoothed solver.

   Returns the L2-regularized minimizer over conv(S), solved by batched
   Frank-Wolfe (the only oracle is the standard linear ``optModel`` solve).
   Returns a regularized solution, not a loss -- pair with a task loss, or use
   ``regularizedFrankWolfeFenchelYoung``. The FW loop needs an accurate linear minimization oracle;
   prefer the callback path with an exact solver (MPAX is approximate here).

   Reference: Dalle et al. (2022) `<https://arxiv.org/abs/2207.13513>`_


   .. py:attribute:: lambd


   .. py:attribute:: max_iter
      :value: 10000



   .. py:attribute:: tol
      :value: 1e-06



   .. py:method:: forward(pred_cost)

      Forward pass



.. py:class:: regularizedFrankWolfeFenchelYoung(optmodel, lambd=1.0, max_iter=10000, tol=1e-06, processes=1, solve_ratio=1.0, reduction: pyepo.func.runtime.Reduction = 'mean', dataset=None)

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


   L2-Regularized Frank-Wolfe with Fenchel-Young loss (RFYL).

   Pairs the RFWO regularized solver with the Fenchel-Young loss of the L2
   regularizer: a convex scalar comparing the predicted cost to the true
   optimum directly. By Danskin's theorem the gradient is the residual
   ``w - r_sol``, so the backward needs no implicit differentiation.

   Reference: Dalle et al. (2022) `<https://arxiv.org/abs/2207.13513>`_


   .. py:attribute:: lambd


   .. py:attribute:: max_iter
      :value: 10000



   .. py:attribute:: tol
      :value: 1e-06



   .. py:method:: forward(pred_cost, true_sol)

      Forward pass



.. py:data:: RFWO

.. py:data:: RFY