matrix1.py
#!/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')
