def test_linear_constraint(self):
# The LinearConstraint constructor is overloaded to accept the A matrix
# as either sparse or dense. If the user doesn't have scipy installed
# they should still be able to call the dense overload, even when the
# matrix requires conversion (from numpy's row-major default to the
# col-major requirement for passing a `const Eigen::MatrixXd`).
A = np.array([[1, 3, 4], [2., 4., 5]])
lb = np.array([1, 2.])
ub = np.array([3., 4.])
dut = LinearConstraint(A=A, lb=lb, ub=ub)
self.assertEqual(dut.num_constraints(), 2)
self.assertEqual(dut.num_vars(), 3)
# UpdateCoefficients is similarily overloaded for sparse or dense, and
# the user should be able to invoke the dense overload without scipy.
dut.UpdateCoefficients(
new_A=np.array([[1E-10, 0, 0], [0, 1, 1]]),
new_lb=np.array([2, 3]), new_ub=np.array([3, 4]))
dut.RemoveTinyCoefficient(tol=1E-5)
np.testing.assert_array_equal(
dut.GetDenseA(), np.array([[0, 0, 0], [0, 1, 1]]))
# When testing in Drake CI, scipy will not be installed. When drake
# developers run this test locally, they might have scipy installed
# for use outside of Drake. In that case, we should double-check
# that our Bazel logic for disabling scipy is working properly.
try:
import scipy
self.assertNotIn(
"/scipy_stub/", scipy.__file__,
"The BUILD dependencies for this test MUST NOT depend on"
" :scipy_stub_py, either directly or transitively.")
self.fail("Somehow, the scipy_none dependency isn't working?!")
except ModuleNotFoundError:
pass
def test_mixed_integer_optimization(self):
prog = mp.MathematicalProgram()
x = prog.NewBinaryVariables(3, "x")
c = np.array([-1.0, -1.0, -2.0])
prog.AddLinearCost(c.dot(x))
a = np.array([1.0, 2.0, 3.0])
prog.AddLinearConstraint(a.dot(x) <= 4)
prog.AddLinearConstraint(x[0] + x[1], 1, np.inf)
prog.AddConstraint(
LinearConstraint(np.array([[1., 1.]]), np.array([1]),
np.array([np.inf])), [x[0], x[1]])
solver = GurobiSolver()
result = solver.Solve(prog, None, None)
self.assertTrue(result.is_success())
# Test that we got the right solution for all x
x_expected = np.array([1.0, 0.0, 1.0])
self.assertTrue(np.all(np.isclose(result.GetSolution(x), x_expected)))
# Also test by asking for the value of each element of x
for i in range(3):
self.assertAlmostEqual(result.GetSolution(x[i]), x_expected[i])