def test_mfista_solver_graph_net_no_l1_term():
w = np.zeros(2)
X = np.array([[1, 0], [0, 4]])
y = np.array([-10, 20])
f1 = lambda w: 0.5 * np.dot(np.dot(X, w) - y, np.dot(X, w) - y)
f1_grad = lambda w: np.dot(X.T, np.dot(X, w) - y)
f2_prox = lambda w, l, *args, **kwargs: (w, dict(converged=True))
lipschitz_constant = _squared_loss_derivative_lipschitz_constant(
X, (np.eye(2) == 1).astype(np.bool), 1)
estimate_solution, _, _ = mfista(
f1_grad, f2_prox, f1, lipschitz_constant, w.size, tol=1e-8)
solution = np.array([-10, 5])
assert_almost_equal(estimate_solution, solution, decimal=4)
def test_mfista_solver_graph_net_no_l1_term():
w = np.zeros(2)
X = np.array([[1, 0], [0, 4]])
y = np.array([-10, 20])
f1 = lambda w: 0.5 * np.dot(np.dot(X, w) - y, np.dot(X, w) - y)
f1_grad = lambda w: np.dot(X.T, np.dot(X, w) - y)
f2_prox = lambda w, l, *args, **kwargs: (w, dict(converged=True))
lipschitz_constant = _squared_loss_derivative_lipschitz_constant(
X, (np.eye(2) == 1).astype(bool), 1)
estimate_solution, _, _ = mfista(
f1_grad, f2_prox, f1, lipschitz_constant, w.size, tol=1e-8)
solution = np.array([-10, 5])
assert_almost_equal(estimate_solution, solution, decimal=4)