def grad_stability(X):
(dim, _) = np.shape(X)
G = np.zeros(np.shape(X))
A_1 = get_entries(X, A_1_cds)
diff1 = 2 * np.dot(A_1, mu)
grad1 = np.tile(mu.T, (block_dim, 1))
# (block_dim,1) * (block_dim,block_dim) across rows
grad1 = diff1 * grad1
set_entries(G, A_1_cds, grad1)
A_T_1 = get_entries(X, A_T_1_cds)
diffT1 = 2 * np.dot(A_T_1.T, mu)
gradT1 = np.tile(mu.T, (block_dim, 1))
gradT1 = diffT1 * gradT1
set_entries(G, A_T_1_cds, gradT1.T)
A_2 = get_entries(X, A_2_cds)
diff2 = 2 * np.dot(A_2, mu)
grad2 = np.tile(mu.T, (block_dim, 1))
grad2 = diff2 * grad2
set_entries(G, A_2_cds, grad2)
A_T_2 = get_entries(X, A_T_2_cds)
diffT2 = 2 * np.dot(A_T_2.T, mu)
gradT2 = np.tile(mu.T, (block_dim, 1))
gradT2 = diffT2 * gradT2
set_entries(G, A_T_2_cds, gradT2.T)
return (1./dim**2) * G
def grad_l2_batch_equals(X, A, coord):
# Upper right
grad_piece = 2*(get_entries(X,coord) - A)
grad = np.zeros(np.shape(X))
set_entries(grad, coord, grad_piece)
return grad
