def test_get_random_like():
np.random.seed(0)
A = sparse.eye(5)
B = utils.get_random_like(A, lambda n: np.random.normal(0, 1e-6, size=n))
A_r, A_c = [list(x) for x in A.nonzero()]
B_r, B_c = [list(x) for x in B.nonzero()]
assert A_r == B_r
assert A_c == B_c
def test_get_random_like(self):
A = sparse.eye(5)
B = utils.get_random_like(A,
lambda n: np.random.normal(0, 1e-6, size=n))
A_r, A_c = [list(x) for x in A.nonzero()]
B_r, B_c = [list(x) for x in B.nonzero()]
self.assertListEqual(A_r, B_r)
self.assertListEqual(A_c, B_c)
def test_solve_and_derivative(self):
np.random.seed(0)
m = 20
n = 10
A, b, c, cone_dims = utils.least_squares_eq_scs_data(m, n)
for mode in ["lsqr", "dense"]:
x, y, s, derivative, adjoint_derivative = cone_prog.solve_and_derivative(
A, b, c, cone_dims, eps=1e-10, mode=mode, solve_method="SCS")
dA = utils.get_random_like(
A, lambda n: np.random.normal(0, 1e-6, size=n))
db = np.random.normal(0, 1e-6, size=b.size)
dc = np.random.normal(0, 1e-6, size=c.size)
dx, dy, ds = derivative(dA, db, dc)
x_pert, y_pert, s_pert, _, _ = cone_prog.solve_and_derivative(
A + dA,
b + db,
c + dc,
cone_dims,
eps=1e-10,
solve_method="SCS")
np.testing.assert_allclose(x_pert - x, dx, atol=1e-8)
np.testing.assert_allclose(y_pert - y, dy, atol=1e-8)
np.testing.assert_allclose(s_pert - s, ds, atol=1e-8)
x, y, s, derivative, adjoint_derivative = cone_prog.solve_and_derivative(
A, b, c, cone_dims, eps=1e-10, mode=mode, solve_method="SCS")
objective = c.T @ x
dA, db, dc = adjoint_derivative(c, np.zeros(y.size),
np.zeros(s.size))
x_pert, _, _, _, _ = cone_prog.solve_and_derivative(
A + 1e-6 * dA,
b + 1e-6 * db,
c + 1e-6 * dc,
cone_dims,
eps=1e-10,
solve_method="SCS")
objective_pert = c.T @ x_pert
np.testing.assert_allclose(objective_pert - objective,
1e-6 * dA.multiply(dA).sum() +
1e-6 * db @ db + 1e-6 * dc @ dc,
atol=1e-8)
def test_solve_and_derivative(self):
m = 20
n = 10
A, b, c, cone_dims = utils.least_squares_eq_scs_data(m, n)
x, y, s, derivative, _ = cone_prog.solve_and_derivative(A,
b,
c,
cone_dims,
eps=1e-8)
dA = utils.get_random_like(A,
lambda n: np.random.normal(0, 1e-6, size=n))
db = np.random.normal(0, 1e-6, size=b.size)
dc = np.random.normal(0, 1e-6, size=c.size)
dx, dy, ds = derivative(dA, db, dc)
x_pert, y_pert, s_pert, _, _ = cone_prog.solve_and_derivative(
A + dA, b + db, c + dc, cone_dims, eps=1e-8)
np.testing.assert_allclose(x_pert - x, dx, atol=1e-6, rtol=1e-6)
import time
import diffcp.cone_program as cone_prog
import diffcp.cones as cone_lib
import diffcp.utils as utils
m = 100
n = 50
A, b, c, cone_dims = utils.least_squares_eq_scs_data(m, n)
for mode in ["lsqr", "dense"]:
x, y, s, derivative, adjoint_derivative = cone_prog.solve_and_derivative(
A, b, c, cone_dims, eps=1e-10, mode=mode)
dA = utils.get_random_like(
A, lambda n: np.random.normal(0, 1e-2, size=n))
db = np.random.normal(0, 1e-2, size=b.size)
dc = np.random.normal(0, 1e-2, size=c.size)
derivative_time = 0.0
for _ in range(10):
tic = time.time()
dx, dy, ds = derivative(dA, db, dc)
toc = time.time()
derivative_time += (toc - tic) / 10
adjoint_derivative_time = 0.0
for _ in range(10):
tic = time.time()
dA, db, dc = adjoint_derivative(
c, np.zeros(y.size), np.zeros(s.size))