pyepo.model.omo
===============

.. py:module:: pyepo.model.omo

.. autoapi-nested-parse::

   Optimization Model based on Pyomo



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/pyepo/model/omo/knapsack/index
   /autoapi/pyepo/model/omo/omomodel/index
   /autoapi/pyepo/model/omo/portfolio/index
   /autoapi/pyepo/model/omo/shortestpath/index
   /autoapi/pyepo/model/omo/tsp/index


Classes
-------

.. autoapisummary::

   pyepo.model.omo.optOmoModel
   pyepo.model.omo.shortestPathModel
   pyepo.model.omo.knapsackModel
   pyepo.model.omo.knapsackModelRel
   pyepo.model.omo.tspGGModel
   pyepo.model.omo.tspGGModelRel
   pyepo.model.omo.tspMTZModel
   pyepo.model.omo.tspMTZModelRel
   pyepo.model.omo.portfolioModel


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

.. py:class:: optOmoModel(solver='glpk')

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


   This is an abstract class for a Pyomo-based optimization model

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str


   .. py:attribute:: solver
      :value: 'glpk'



   .. py:method:: __repr__()


   .. py:method:: setObj(c)

      A method to set the objective function

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



   .. py:method:: solve()

      A method to solve the model

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



   .. py:method:: copy()

      A method to copy the model

      :returns: new copied model
      :rtype: optModel



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

      A method to add a new constraint

      :param coefs: coefficients of new constraint
      :type coefs: np.ndarray / list
      :param rhs: right-hand side of new constraint
      :type rhs: float

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



.. py:class:: shortestPathModel(grid, solver='glpk')

   Bases: :py:obj:`pyepo.model.omo.omomodel.optOmoModel`


   This class is an optimization model for the shortest path problem

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str

   .. 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:: _getModel()

      A method to build Pyomo model



.. py:class:: knapsackModel(weights, capacity, solver='glpk')

   Bases: :py:obj:`pyepo.model.omo.omomodel.optOmoModel`


   This class is an optimization model for the knapsack problem

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str

   .. attribute:: weights

      weights of items

      :type: np.ndarray

   .. attribute:: capacity

      total capacity

      :type: np.ndarray

   .. attribute:: items

      list of item index

      :type: list


   .. py:attribute:: weights


   .. py:attribute:: capacity


   .. py:attribute:: items


   .. py:method:: _getModel()

      A method to build Pyomo model



   .. py:method:: relax()

      A method to get linear relaxation model



.. py:class:: knapsackModelRel(weights, capacity, solver='glpk')

   Bases: :py:obj:`knapsackModel`


   This class is relaxed optimization model for knapsack problem.


   .. py:method:: _getModel()

      A method to build Pyomo model



   .. py:method:: relax()

      A forbidden method to relax MIP model



.. py:class:: tspGGModel(num_nodes, solver='glpk')

   Bases: :py:obj:`tspABModel`


   This class is an optimization model for the traveling salesman problem based on Gavish-Graves (GG) formulation.

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str

   .. attribute:: num_nodes

      Number of nodes

      :type: int

   .. attribute:: edges

      List of edge index

      :type: list


   .. py:method:: _getModel()

      A method to build Pyomo model

      :returns: optimization model and variables
      :rtype: tuple



   .. py:method:: setObj(c)

      A method to set the objective function

      :param c: cost vector
      :type c: list



   .. py:method:: solve()

      A method to solve model



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

      A method to add new constraint

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

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



   .. py:method:: relax()

      A method to get linear relaxation model



.. py:class:: tspGGModelRel(num_nodes, solver='glpk')

   Bases: :py:obj:`tspGGModel`


   This class is relaxation of tspGGModel.

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str

   .. attribute:: num_nodes

      Number of nodes

      :type: int

   .. attribute:: edges

      List of edge index

      :type: list


   .. py:method:: _getModel()

      A method to build Pyomo model

      :returns: optimization model and variables
      :rtype: tuple



   .. py:method:: solve()

      A method to solve model

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



   .. py:method:: relax()

      A forbidden method to relax MIP model



   .. py:method:: getTour(sol)

      A forbidden method to get a tour from solution



.. py:class:: tspMTZModel(num_nodes, solver='glpk')

   Bases: :py:obj:`tspABModel`


   This class is an optimization model for the traveling salesman problem based on Miller-Tucker-Zemlin (MTZ) formulation.

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str

   .. attribute:: num_nodes

      Number of nodes

      :type: int

   .. attribute:: edges

      List of edge index

      :type: list


   .. py:method:: _getModel()

      A method to build Pyomo model

      :returns: optimization model and variables
      :rtype: tuple



   .. py:method:: setObj(c)

      A method to set the objective function

      :param c: cost vector
      :type c: list



   .. py:method:: solve()

      A method to solve model



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

      A method to add new constraint

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

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



   .. py:method:: relax()

      A method to get linear relaxation model



.. py:class:: tspMTZModelRel(num_nodes, solver='glpk')

   Bases: :py:obj:`tspMTZModel`


   This class is relaxation of tspMTZModel.

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str

   .. attribute:: num_nodes

      Number of nodes

      :type: int

   .. attribute:: edges

      List of edge index

      :type: list


   .. py:method:: _getModel()

      A method to build Pyomo model

      :returns: optimization model and variables
      :rtype: tuple



   .. py:method:: solve()

      A method to solve model

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



   .. py:method:: relax()

      A forbidden method to relax MIP model



   .. py:method:: getTour(sol)

      A forbidden method to get a tour from solution



.. py:class:: portfolioModel(num_assets, covariance, gamma=2.25, solver='glpk')

   Bases: :py:obj:`pyepo.model.omo.omomodel.optOmoModel`


   This class is an optimization model for the portfolio problem

   .. attribute:: _model

      Pyomo model

      :type: Pyomo model

   .. attribute:: solver

      optimization solver in the background

      :type: str

   .. attribute:: num_assets

      number of assets

      :type: int

   .. attribute:: covariance

      covariance matrix of the returns

      :type: numpy.ndarray

   .. attribute:: risk_level

      risk level

      :type: float


   .. py:attribute:: num_assets


   .. py:attribute:: covariance


   .. py:attribute:: risk_level


   .. py:method:: _getRiskLevel(gamma)

      A method to calculate the risk level

      :param gamma: risk level parameter
      :type gamma: float

      :returns: risk level
      :rtype: float



   .. py:method:: _getModel()

      A method to build Pyomo model

      :returns: optimization model and variables
      :rtype: tuple