def test_get_value_and_get_approx_value_nonlin_constr(self):
"""
min x^2 -2x + 1 st. x^2 == 4
when convexified at x = 1,
min x^2 -2x + 1 + penalty_coeff*|2x-5|
when penalty_coeff == 0.5, solution is x = 1.5 and the value is 1.25
(according to Wolfram Alpha)
approx value should be 1.25
value should be 1.125
"""
quad = QuadExpr(2 * np.eye(1), -2 * np.ones((1, 1)), np.ones((1, 1)))
quad_cnt = QuadExpr(2 * np.eye(1), np.zeros((1, 1)), np.zeros((1, 1)))
eq = EqExpr(quad_cnt, np.array([[4]]))
model = grb.Model()
prob = Prob(model)
grb_var = model.addVar(lb=-1 * GRB.INFINITY, ub=GRB.INFINITY, name='x')
grb_vars = np.array([[grb_var]])
var = Variable(grb_vars, np.array([[1.0]]))
model.update()
obj = BoundExpr(quad, var)
prob.add_obj_expr(obj)
bexpr = BoundExpr(eq, var)
prob.add_cnt_expr(bexpr)
prob.convexify()
prob.update_obj(penalty_coeff=0.5)
prob.optimize()
self.assertTrue(np.allclose(var.get_value(), np.array([[1.5]])))
self.assertTrue(
np.allclose(prob.get_approx_value(0.5), np.array([[1.25]])))
self.assertTrue(np.allclose(prob.get_value(0.5), np.array([[1.125]])))
def test_get_value_and_get_approx_value_nonlin_constr(self):
"""
min x^2 -2x + 1 st. x^2 == 4
when convexified at x = 1,
min x^2 -2x + 1 + penalty_coeff*|2x-5|
when penalty_coeff == 0.5, solution is x = 1.5 and the value is 1.25
(according to Wolfram Alpha)
approx value should be 1.25
value should be 1.125
"""
quad = QuadExpr(2*np.eye(1), -2*np.ones((1,1)), np.ones((1,1)))
quad_cnt = QuadExpr(2*np.eye(1), np.zeros((1,1)), np.zeros((1,1)))
eq = EqExpr(quad_cnt, np.array([[4]]))
model = grb.Model()
prob = Prob(model)
grb_var = model.addVar(lb=-1 * GRB.INFINITY, ub=GRB.INFINITY, name='x')
grb_vars = np.array([[grb_var]])
var = Variable(grb_vars, np.array([[1.0]]))
model.update()
obj = BoundExpr(quad, var)
prob.add_obj_expr(obj)
bexpr = BoundExpr(eq, var)
prob.add_cnt_expr(bexpr)
prob.convexify()
prob.update_obj(penalty_coeff=0.5)
prob.optimize()
self.assertTrue(np.allclose(var.get_value(), np.array([[1.5]])))
self.assertTrue(np.allclose(prob.get_approx_value(0.5), np.array([[1.25]])))
self.assertTrue(np.allclose(prob.get_value(0.5), np.array([[1.125]])))
def test_convexify_leq(self):
model = grb.Model()
prob = Prob(model)
grb_var = model.addVar(lb=-1 * GRB.INFINITY, ub=GRB.INFINITY, name='x')
grb_vars = np.array([[grb_var]])
var = Variable(grb_vars)
model.update()
grb_cnt = model.addConstr(grb_var, GRB.EQUAL, 0)
model.optimize()
var.update()
e = Expr(f)
eq = LEqExpr(e, np.array([[4]]))
bexpr = BoundExpr(eq, var)
prob.add_cnt_expr(bexpr)
prob.convexify()
self.assertTrue(len(prob._penalty_exprs) == 1)
self.assertTrue(isinstance(prob._penalty_exprs[0].expr, HingeExpr))
def test_convexify_leq(self):
model = grb.Model()
prob = Prob(model)
grb_var = model.addVar(lb=-1 * GRB.INFINITY, ub=GRB.INFINITY, name='x')
grb_vars = np.array([[grb_var]])
var = Variable(grb_vars)
model.update()
grb_cnt = model.addConstr(grb_var, GRB.EQUAL, 0)
model.optimize()
var.update()
e = Expr(f)
eq = LEqExpr(e, np.array([[4]]))
bexpr = BoundExpr(eq, var)
prob.add_cnt_expr(bexpr)
prob.convexify()
self.assertTrue(len(prob._penalty_exprs) == 1)
self.assertTrue(isinstance(prob._penalty_exprs[0].expr, HingeExpr))
def test_get_approx_value_lin_constr(self):
"""
min x^2 st. x == 4
when convexified,
min x^2 + penalty_coeff*|x-4|
when penalty_coeff == 1, solution is x = 0.5 and the value is 3.75
(according to Wolfram Alpha)
when penalty_coeff == 2, solution is x = 1.0 and the value is 7.0
(according to Wolfram Alpha)
"""
quad = QuadExpr(2 * np.eye(1), np.zeros((1, 1)), np.zeros((1, 1)))
e = Expr(f)
eq = EqExpr(e, np.array([[4]]))
model = grb.Model()
prob = Prob(model)
grb_var = model.addVar(lb=-1 * GRB.INFINITY, ub=GRB.INFINITY, name='x')
grb_vars = np.array([[grb_var]])
var = Variable(grb_vars)
model.update()
obj = BoundExpr(quad, var)
prob.add_obj_expr(obj)
bexpr = BoundExpr(eq, var)
prob.add_cnt_expr(bexpr)
prob.optimize() # needed to set an initial value
prob.convexify()
prob.update_obj(penalty_coeff=1.0)
prob.optimize()
self.assertTrue(np.allclose(var.get_value(), np.array([[0.5]])))
self.assertTrue(
np.allclose(prob.get_approx_value(1.0), np.array([[3.75]])))
prob.update_obj(penalty_coeff=2.0)
prob.optimize()
self.assertTrue(np.allclose(var.get_value(), np.array([[1.0]])))
self.assertTrue(
np.allclose(prob.get_approx_value(2.0), np.array([[7]])))
def test_get_approx_value_lin_constr(self):
"""
min x^2 st. x == 4
when convexified,
min x^2 + penalty_coeff*|x-4|
when penalty_coeff == 1, solution is x = 0.5 and the value is 3.75
(according to Wolfram Alpha)
when penalty_coeff == 2, solution is x = 1.0 and the value is 7.0
(according to Wolfram Alpha)
"""
quad = QuadExpr(2*np.eye(1), np.zeros((1,1)), np.zeros((1,1)))
e = Expr(f)
eq = EqExpr(e, np.array([[4]]))
model = grb.Model()
prob = Prob(model)
grb_var = model.addVar(lb=-1 * GRB.INFINITY, ub=GRB.INFINITY, name='x')
grb_vars = np.array([[grb_var]])
var = Variable(grb_vars)
model.update()
obj = BoundExpr(quad, var)
prob.add_obj_expr(obj)
bexpr = BoundExpr(eq, var)
prob.add_cnt_expr(bexpr)
prob.optimize() # needed to set an initial value
prob.convexify()
prob.update_obj(penalty_coeff=1.0)
prob.optimize()
self.assertTrue(np.allclose(var.get_value(), np.array([[0.5]])))
self.assertTrue(np.allclose(prob.get_approx_value(1.0), np.array([[3.75]])))
prob.update_obj(penalty_coeff=2.0)
prob.optimize()
self.assertTrue(np.allclose(var.get_value(), np.array([[1.0]])))
self.assertTrue(np.allclose(prob.get_approx_value(2.0), np.array([[7]])))