#!/usr/bin/env python3.7
# Copyright 2021, Gurobi Optimization, LLC
# This example formulates and solves the following simple MIP model
# using the matrix API:
# maximize
# x + y + 2 z
# subject to
# x + 2 y + 3 z <= 4
# x + y >= 1
# x, y, z binary
import gurobipy as gp
from gurobipy import GRB
import numpy as np
import scipy.sparse as sp
try:
# Create a new model
m = gp.Model("matrix1")
# Create variables
x = m.addMVar(shape=3, vtype=GRB.BINARY, name="x")
# Set objective
obj = np.array([1.0, 1.0, 2.0])
m.setObjective(obj @ x, GRB.MAXIMIZE)
# Build (sparse) constraint matrix
val = np.array([1.0, 2.0, 3.0, -1.0, -1.0])
row = np.array([0, 0, 0, 1, 1])
col = np.array([0, 1, 2, 0, 1])
A = sp.csr_matrix((val, (row, col)), shape=(2, 3))
# Build rhs vector
rhs = np.array([4.0, -1.0])
# Add constraints
m.addConstr(A @ x <= rhs, name="c")
# Optimize model
m.optimize()
print(x.X)
print('Obj: %g' % m.objVal)
except gp.GurobiError as e:
print('Error code ' + str(e.errno) + ": " + str(e))
except AttributeError:
print('Encountered an attribute error')