def test_violation():
def cons_f(x):
return np.array([x[0]**2 + x[1], x[0]**2 - x[1]])
nlc = NonlinearConstraint(cons_f, [-1, -0.8500], [2, 2])
pc = PreparedConstraint(nlc, [0.5, 1])
assert_array_equal(pc.violation([0.5, 1]), [0., 0.])
np.testing.assert_almost_equal(pc.violation([0.5, 1.2]), [0., 0.1])
np.testing.assert_almost_equal(pc.violation([1.2, 1.2]), [0.64, 0])
np.testing.assert_almost_equal(pc.violation([0.1, -1.2]), [0.19, 0])
np.testing.assert_almost_equal(pc.violation([0.1, 2]), [0.01, 1.14])
def test_violation():
def cons_f(x):
return np.array([x[0] ** 2 + x[1], x[0] ** 2 - x[1]])
nlc = NonlinearConstraint(cons_f, [-1, -0.8500], [2, 2])
pc = PreparedConstraint(nlc, [0.5, 1])
assert_array_equal(pc.violation([0.5, 1]), [0., 0.])
np.testing.assert_almost_equal(pc.violation([0.5, 1.2]), [0., 0.1])
np.testing.assert_almost_equal(pc.violation([1.2, 1.2]), [0.64, 0])
np.testing.assert_almost_equal(pc.violation([0.1, -1.2]), [0.19, 0])
np.testing.assert_almost_equal(pc.violation([0.1, 2]), [0.01, 1.14])
def test_prepare_constraint_infeasible_x0():
lb = np.array([0, 20, 30])
ub = np.array([0.5, np.inf, 70])
x0 = np.array([1, 2, 3])
enforce_feasibility = np.array([False, True, True], dtype=bool)
bounds = Bounds(lb, ub, enforce_feasibility)
pytest.raises(ValueError, PreparedConstraint, bounds, x0)
pc = PreparedConstraint(Bounds(lb, ub), [1, 2, 3])
assert (pc.violation([1, 2, 3]) > 0).any()
assert (pc.violation([0.25, 21, 31]) == 0).all()
x0 = np.array([1, 2, 3, 4])
A = np.array([[1, 2, 3, 4], [5, 0, 0, 6], [7, 0, 8, 0]])
enforce_feasibility = np.array([True, True, True], dtype=bool)
linear = LinearConstraint(A, -np.inf, 0, enforce_feasibility)
pytest.raises(ValueError, PreparedConstraint, linear, x0)
pc = PreparedConstraint(LinearConstraint(A, -np.inf, 0),
[1, 2, 3, 4])
assert (pc.violation([1, 2, 3, 4]) > 0).any()
assert (pc.violation([-10, 2, -10, 4]) == 0).all()
def fun(x):
return A.dot(x)
def jac(x):
return A
def hess(x, v):
return sps.csr_matrix((4, 4))
nonlinear = NonlinearConstraint(fun, -np.inf, 0, jac, hess,
enforce_feasibility)
pytest.raises(ValueError, PreparedConstraint, nonlinear, x0)
pc = PreparedConstraint(nonlinear, [-10, 2, -10, 4])
assert (pc.violation([1, 2, 3, 4]) > 0).any()
assert (pc.violation([-10, 2, -10, 4]) == 0).all()
def test_prepare_constraint_infeasible_x0():
lb = np.array([0, 20, 30])
ub = np.array([0.5, np.inf, 70])
x0 = np.array([1, 2, 3])
enforce_feasibility = np.array([False, True, True], dtype=bool)
bounds = Bounds(lb, ub, enforce_feasibility)
pytest.raises(ValueError, PreparedConstraint, bounds, x0)
pc = PreparedConstraint(Bounds(lb, ub), [1, 2, 3])
assert (pc.violation([1, 2, 3]) > 0).any()
assert (pc.violation([0.25, 21, 31]) == 0).all()
x0 = np.array([1, 2, 3, 4])
A = np.array([[1, 2, 3, 4], [5, 0, 0, 6], [7, 0, 8, 0]])
enforce_feasibility = np.array([True, True, True], dtype=bool)
linear = LinearConstraint(A, -np.inf, 0, enforce_feasibility)
pytest.raises(ValueError, PreparedConstraint, linear, x0)
pc = PreparedConstraint(LinearConstraint(A, -np.inf, 0),
[1, 2, 3, 4])
assert (pc.violation([1, 2, 3, 4]) > 0).any()
assert (pc.violation([-10, 2, -10, 4]) == 0).all()
def fun(x):
return A.dot(x)
def jac(x):
return A
def hess(x, v):
return sps.csr_matrix((4, 4))
nonlinear = NonlinearConstraint(fun, -np.inf, 0, jac, hess,
enforce_feasibility)
pytest.raises(ValueError, PreparedConstraint, nonlinear, x0)
pc = PreparedConstraint(nonlinear, [-10, 2, -10, 4])
assert (pc.violation([1, 2, 3, 4]) > 0).any()
assert (pc.violation([-10, 2, -10, 4]) == 0).all()