pyepo.model.predefined
======================

.. py:module:: pyepo.model.predefined

.. autoapi-nested-parse::

   Backend-dispatching factories for the built-in problems.

   Each factory builds the requested problem on the chosen solver backend, selected
   by the ``backend`` keyword (default Gurobi) rather than by importing a
   backend-specific class.



Functions
---------

.. autoapisummary::

   pyepo.model.predefined.shortestPathModel
   pyepo.model.predefined.knapsackModel
   pyepo.model.predefined.portfolioModel
   pyepo.model.predefined.tspModel
   pyepo.model.predefined.vrpModel


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

.. py:function:: shortestPathModel(grid, *, backend='gurobi', **kwargs)

   Shortest path on a grid network.

   :param grid: grid size ``(h, w)``
   :type grid: tuple
   :param backend: solver backend; one of ``"gurobi"``, ``"copt"``, ``"pyomo"``, ``"ortools"``, ``"mpax"``
   :type backend: str


.. py:function:: knapsackModel(weights, capacity, *, backend='gurobi', **kwargs)

   Multi-dimensional knapsack.

   :param weights: item weights with shape ``(dim, n_items)``
   :type weights: ndarray
   :param capacity: per-dimension capacity with length ``dim``
   :type capacity: ndarray
   :param backend: solver backend; one of ``"gurobi"``, ``"copt"``, ``"pyomo"``, ``"ortools"``, ``"mpax"``
   :type backend: str


.. py:function:: portfolioModel(num_assets, covariance, *, backend='gurobi', **kwargs)

   Mean-variance portfolio optimization.

   :param num_assets: number of assets
   :type num_assets: int
   :param covariance: covariance matrix of the asset returns
   :type covariance: ndarray
   :param backend: solver backend; one of ``"gurobi"``, ``"copt"``, ``"pyomo"``
   :type backend: str


.. py:function:: tspModel(num_nodes, *, backend='gurobi', formulation='DFJ', **kwargs)

   Traveling salesperson.

   :param num_nodes: number of nodes
   :type num_nodes: int
   :param backend: solver backend; one of ``"gurobi"``, ``"copt"``, ``"pyomo"``
   :type backend: str
   :param formulation: ILP formulation; one of ``"DFJ"``, ``"GG"``, ``"MTZ"`` (``"DFJ"`` on gurobi and copt only)
   :type formulation: str


.. py:function:: vrpModel(num_nodes, demands, capacity, num_vehicle, *, backend='gurobi', formulation='RCI', **kwargs)

   Capacitated vehicle routing.

   :param num_nodes: number of nodes, with the depot as node 0
   :type num_nodes: int
   :param demands: per-customer demands with length ``num_nodes - 1``
   :type demands: list
   :param capacity: vehicle capacity
   :type capacity: float
   :param num_vehicle: number of vehicles
   :type num_vehicle: int
   :param backend: solver backend; one of ``"gurobi"``, ``"copt"``, ``"pyomo"``
   :type backend: str
   :param formulation: ILP formulation; ``"RCI"`` or ``"MTZ"`` (``"RCI"`` on gurobi and copt only)
   :type formulation: str