pyepo.model.mpax
================

.. py:module:: pyepo.model.mpax

.. autoapi-nested-parse::

   Optimization Model based on MPAX



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/pyepo/model/mpax/knapsack/index
   /autoapi/pyepo/model/mpax/mpaxmodel/index
   /autoapi/pyepo/model/mpax/shortestpath/index


Classes
-------

.. autoapisummary::

   pyepo.model.mpax.optMpaxModel
   pyepo.model.mpax.shortestPathModel
   pyepo.model.mpax.knapsackModel


Package Contents
----------------

.. py:class:: optMpaxModel(A=None, b=None, G=None, h=None, l=None, u=None, use_sparse_matrix=True, minimize=True)

   Bases: :py:obj:`pyepo.model.opt.optModel`


   This is an abstract class for MPAX-based optimization model

   .. attribute:: A

      The matrix of equality constraints.

      :type: jnp.ndarray, BCOO or BCSR

   .. attribute:: b

      The right hand side of equality constraints.

      :type: jnp.ndarray

   .. attribute:: G

      The matrix for inequality constraints.

      :type: jnp.ndarray, BCOO or BCSR

   .. attribute:: h

      The right hand side of inequality constraints.

      :type: jnp.ndarray

   .. attribute:: l

      The lower bound of the variables.

      :type: jnp.ndarray

   .. attribute:: u

      The upper bound of the variables.

      :type: jnp.ndarray

   .. attribute:: use_sparse_matrix

      Whether to use sparse matrix format, by default True.

      :type: bool

   .. attribute:: minimize

      Whether to minimize objective, by default True.

      :type: bool


   .. py:attribute:: l


   .. py:attribute:: u


   .. py:attribute:: use_sparse_matrix
      :value: True



   .. py:attribute:: modelSense
      :value: 1



   .. py:attribute:: device
      :value: None



   .. py:attribute:: jitted_solve
      :value: None



   .. py:attribute:: batch_optimize


   .. py:method:: __repr__()


   .. py:property:: num_cost

      number of cost to be predicted


   .. py:method:: setObj(c)

      A method to set objective function

      :param c: cost of objective function
      :type c: np.ndarray / list



   .. py:method:: solve()

      A method to solve model

      :returns: optimal solution (list) and objective value (jnp.float32)
      :rtype: tuple



   .. py:method:: _jitted_solve(c, A, b, G, h, l, u, use_sparse_matrix)
      :staticmethod:


      A static method for JIT complile



   .. py:method:: copy()

      A method to copy model

      :returns: new copied model
      :rtype: optModel



   .. py:method:: addConstr(coefs, rhs)

      A method to add new constraint

      :param coefs: coeffcients of new constraint
      :type coefs: np.ndarray / list
      :param rhs: right-hand side of new constraint
      :type rhs: jnp.float32

      :returns: new model with the added constraint
      :rtype: optModel



   .. py:method:: _getModel()

      Placeholder method for MPAX. MPAX does not require an explicit model creation.



.. py:class:: shortestPathModel(grid)

   Bases: :py:obj:`pyepo.model.mpax.mpaxmodel.optMpaxModel`


   This class is optimization model for shortest path problem

   .. attribute:: grid

      Size of grid network

      :type: tuple of int

   .. attribute:: arcs

      List of arcs

      :type: list


   .. py:attribute:: grid


   .. py:attribute:: arcs
      :value: []



   .. py:method:: _getArcs()

      A method to get list of arcs for grid network

      :returns: arcs
      :rtype: list



   .. py:method:: _constructMatrix()

      Constructs the incidence matrix A, supply/demand vector b, and upper bound u
      for the shortest path problem.

      :returns: Incidence matrix for flow conservation
                b (jnp.ndarray): Supply/demand vector
                u (jnp.ndarray): Upper bound for flow variables
      :rtype: A (jnp.ndarray)



.. py:class:: knapsackModel(weights, capacity)

   Bases: :py:obj:`pyepo.model.mpax.mpaxmodel.optMpaxModel`


   This class is optimization model for relexed knapsack problem

   .. attribute:: _model

      Gurobi model

      :type: GurobiPy model

   .. attribute:: weights

      Weights of items

      :type: np.ndarray / list

   .. attribute:: capacity

      Total capacity

      :type: np.ndarray / listy

   .. attribute:: items

      List of item index

      :type: list


   .. py:attribute:: weights


   .. py:attribute:: capacity


   .. py:attribute:: items


   .. py:method:: _constructMatrix()

      Constructs the inequality constraint matrix G, right-hand side h, and
      upper bound u for the knapsack problem.

      :returns: Weights of items
                h (jnp.ndarray): Total capacity
                u (jnp.ndarray): Upper bound for item selection
      :rtype: G (jnp.ndarray)