pyepo.model.ort.ortmodel
========================

.. py:module:: pyepo.model.ort.ortmodel

.. autoapi-nested-parse::

   Abstract optimization model based on Google OR-Tools (pywraplp)



Classes
-------

.. autoapisummary::

   pyepo.model.ort.ortmodel.optOrtModel


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

.. py:class:: optOrtModel(solver: str = 'scip')

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


   Abstract base class for OR-Tools pywraplp (LP/MIP) models.

   Subclasses implement ``_getModel`` to build a ``pywraplp.Solver`` and
   return ``(model, variables)``. Unlike ``optGrbModel``, the objective
   sense is **not** detected automatically -- set ``self.modelSense =
   EPO.MAXIMIZE`` in ``_getModel`` for maximization problems (default is
   minimization). Solver output is silenced by default. The backend solver
   is selected at construction time via the ``solver`` argument (e.g.,
   ``"scip"``, ``"glop"``, ``"cbc"``).

   :ivar _model: underlying OR-Tools linear solver
   :vartype _model: pywraplp.Solver
   :ivar solver: pywraplp backend name

   :vartype solver: str


   .. py:attribute:: solver
      :value: 'scip'



   .. py:method:: get_config() -> dict

      Return the constructor configuration for this model.



   .. py:method:: setObj(c: numpy.ndarray | torch.Tensor | list) -> None

      A method to set the objective function

      :param c: cost of objective function



   .. py:method:: solve() -> tuple[numpy.ndarray, float]

      A method to solve the model

      :returns: optimal solution (list) and objective value (float)
      :rtype: tuple



   .. py:method:: copy() -> Self

      A method to copy the model

      :returns: new copied model
      :rtype: optModel



   .. py:method:: addConstr(coefs: numpy.ndarray | torch.Tensor | list, rhs: float) -> Self

      A method to add a new constraint

      :param coefs: coefficients of new constraint
      :param rhs: right-hand side of new constraint

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