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Welcome to PyEPO’s documentation!

This is the documentation of PyEPO (PyTorch-based End-to-End Predict-then-Optimize Library Tool), which aims to provide end-to-end methods for predict-then-optimize tasks.

Sample Code

import random

import gurobipy as gp
from gurobipy import GRB

from pyepo.model.grb import optGrbModel

class myModel(optGrbModel):

    def _getModel(self):
        # create a model
        m = gp.Model()
        # variables
        x = m.addVars(5, name="x", vtype=GRB.BINARY)
        # model sense
        m.modelSense = GRB.MAXIMIZE
        # constraints
        m.addConstr(3 * x[0] + 4 * x[1] + 3 * x[2] + 6 * x[3] + 4 * x[4] <= 12)
        m.addConstr(4 * x[0] + 5 * x[1] + 2 * x[2] + 3 * x[3] + 5 * x[4] <= 10)
        m.addConstr(5 * x[0] + 4 * x[1] + 6 * x[2] + 2 * x[3] + 3 * x[4] <= 15)
        return m, x

# set optimization model
optmodel = myModel()
# init SPO+ loss
spo = pyepo.func.SPOPlus(optmodel, processes=1)

Contents:

Indices and Tables