def solve_nonlinear(self, params, unknowns, resids):
x = params['x']
y = params['y']
A = params['A']
unknowns['f_xy'] = x**2 + y**2 + np.sin(A).sum()
unknowns['g_xy'] = np.linalg.dot(A**2, np.array([y, x**2]))
def test_dg_dA(self):
B = 2*self.A * np.array([self.y, self.x**2])
actual = np.kron(B, np.array([[1],[0],[0]]))[:4].flatten()
computed = self.deriv['g_xy', 'A']['J_fwd'].flatten()
self.assertAlmostEquals(np.linalg.norm(actual - computed), 0)
