def test_callback(self):
m = pe.ConcreteModel()
m.x = pe.Var(bounds=(0, 4))
m.y = pe.Var(within=pe.Integers, bounds=(0, None))
m.obj = pe.Objective(expr=2*m.x + m.y)
m.cons = pe.ConstraintList()
def _add_cut(xval):
m.x.value = xval
return m.cons.add(m.y >= taylor_series_expansion((m.x - 2)**2))
_add_cut(0)
_add_cut(4)
opt = Gurobi()
opt.set_instance(m)
opt.set_gurobi_param('PreCrush', 1)
opt.set_gurobi_param('LazyConstraints', 1)
def _my_callback(cb_m, cb_opt, cb_where):
if cb_where == gurobipy.GRB.Callback.MIPSOL:
cb_opt.cbGetSolution(vars=[m.x, m.y])
if m.y.value < (m.x.value - 2)**2 - 1e-6:
cb_opt.cbLazy(_add_cut(m.x.value))
opt.set_callback(_my_callback)
opt.solve(m)
self.assertAlmostEqual(m.x.value, 1)
self.assertAlmostEqual(m.y.value, 1)
def test_range_constraints(self):
m = pe.ConcreteModel()
m.x = pe.Var()
m.xl = pe.Param(initialize=-1, mutable=True)
m.xu = pe.Param(initialize=1, mutable=True)
m.c = pe.Constraint(expr=pe.inequality(m.xl, m.x, m.xu))
m.obj = pe.Objective(expr=m.x)
opt = Gurobi()
opt.set_instance(m)
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -1)
m.xl.value = -3
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -3)
del m.obj
m.obj = pe.Objective(expr=m.x, sense=pe.maximize)
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, 1)
m.xu.value = 3
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, 3)
def test_fixed_var(self):
m = pe.ConcreteModel()
m.a = pe.Param(initialize=1, mutable=True)
m.b = pe.Param(initialize=1, mutable=True)
m.c = pe.Param(initialize=1, mutable=True)
m.x = pe.Var()
m.y = pe.Var()
m.obj = pe.Objective(expr=m.y)
m.con = pe.Constraint(expr=m.y >= m.a*m.x**2 + m.b*m.x + m.c)
m.x.fix(1)
opt = Gurobi()
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, 1)
self.assertAlmostEqual(m.y.value, 3)
m.x.value = 2
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, 2)
self.assertAlmostEqual(m.y.value, 7)
m.x.unfix()
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -m.b.value / (2 * m.a.value))
self.assertAlmostEqual(m.y.value, m.a.value * m.x.value ** 2 + m.b.value * m.x.value + m.c.value)
def test_quadratic_constraint_with_params(self):
m = pe.ConcreteModel()
m.a = pe.Param(initialize=1, mutable=True)
m.b = pe.Param(initialize=1, mutable=True)
m.c = pe.Param(initialize=1, mutable=True)
m.x = pe.Var()
m.y = pe.Var()
m.obj = pe.Objective(expr=m.y)
m.con = pe.Constraint(expr=m.y >= m.a * m.x**2 + m.b * m.x + m.c)
opt = Gurobi()
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -m.b.value / (2 * m.a.value))
self.assertAlmostEqual(
m.y.value,
m.a.value * m.x.value**2 + m.b.value * m.x.value + m.c.value)
m.a.value = 2
m.b.value = 4
m.c.value = -1
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -m.b.value / (2 * m.a.value))
self.assertAlmostEqual(
m.y.value,
m.a.value * m.x.value**2 + m.b.value * m.x.value + m.c.value)
def test_nonconvex(self):
if gurobipy.GRB.VERSION_MAJOR < 9:
raise unittest.SkipTest
m = pe.ConcreteModel()
m.x = pe.Var()
m.y = pe.Var()
m.obj = pe.Objective(expr=m.x**2 + m.y**2)
m.c = pe.Constraint(expr=m.y == (m.x-1)**2 - 2)
opt = Gurobi()
opt.solver_options['nonconvex'] = 2
opt.solve(m)
self.assertAlmostEqual(m.x.value, -0.3660254037844423, 2)
self.assertAlmostEqual(m.y.value, -0.13397459621555508, 2)
def test_quadratic_objective(self):
m = pe.ConcreteModel()
m.a = pe.Param(initialize=1, mutable=True)
m.b = pe.Param(initialize=1, mutable=True)
m.c = pe.Param(initialize=1, mutable=True)
m.x = pe.Var()
m.obj = pe.Objective(expr=m.a*m.x**2 + m.b*m.x + m.c)
opt = Gurobi()
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -m.b.value / (2 * m.a.value))
self.assertAlmostEqual(res.best_feasible_objective,
m.a.value * m.x.value ** 2 + m.b.value * m.x.value + m.c.value)
m.a.value = 2
m.b.value = 4
m.c.value = -1
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -m.b.value / (2 * m.a.value))
self.assertAlmostEqual(res.best_feasible_objective,
m.a.value * m.x.value ** 2 + m.b.value * m.x.value + m.c.value)
def test_lp(self):
self.set_params(-1, -2, 0.1, -2)
x, y = self.get_solution()
opt = Gurobi()
res = opt.solve(self.m)
self.assertAlmostEqual(x + y, res.best_feasible_objective)
self.assertAlmostEqual(x + y, res.best_objective_bound)
self.assertEqual(res.termination_condition, TerminationCondition.optimal)
self.assertTrue(res.best_feasible_objective is not None)
self.assertAlmostEqual(x, self.m.x.value)
self.assertAlmostEqual(y, self.m.y.value)
self.set_params(-1.25, -1, 0.5, -2)
opt.config.load_solution = False
res = opt.solve(self.m)
self.assertAlmostEqual(x, self.m.x.value)
self.assertAlmostEqual(y, self.m.y.value)
x, y = self.get_solution()
self.assertNotAlmostEqual(x, self.m.x.value)
self.assertNotAlmostEqual(y, self.m.y.value)
opt.load_vars()
self.assertAlmostEqual(x, self.m.x.value)
self.assertAlmostEqual(y, self.m.y.value)
def test_var_bounds(self):
m = pe.ConcreteModel()
m.x = pe.Var(bounds=(-1, 1))
m.obj = pe.Objective(expr=m.x)
opt = Gurobi()
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -1)
m.x.setlb(-3)
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, -3)
del m.obj
m.obj = pe.Objective(expr=m.x, sense=pe.maximize)
opt = Gurobi()
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, 1)
m.x.setub(3)
res = opt.solve(m)
self.assertAlmostEqual(m.x.value, 3)
def test_solution_number(self):
m = create_pmedian_model()
opt = Gurobi()
opt.gurobi_options['PoolSolutions'] = 3
opt.gurobi_options['PoolSearchMode'] = 2
res = opt.solve(m)
num_solutions = opt.get_model_attr('SolCount')
self.assertEqual(num_solutions, 3)
res.solution_loader.load_vars(solution_number=0)
self.assertAlmostEqual(pe.value(m.obj.expr), 6.431184939357673)
res.solution_loader.load_vars(solution_number=1)
self.assertAlmostEqual(pe.value(m.obj.expr), 6.584793218502477)
res.solution_loader.load_vars(solution_number=2)
self.assertAlmostEqual(pe.value(m.obj.expr), 6.592304628123309)
