def compute_alphas(gradients, nu):
    # TODO: Look into both versions with minus and without minus sign
    H = compute_dot_product_matrix(gradients)
    m = len(gradients)
    n = len(gradients[0])


    #Converting into cvxopt format
    P = cvxopt_matrix(H)
    q = cvxopt_matrix(-np.zeros((m, 1)))

    G = np.vstack((np.eye(m), -np.eye(m)))
    print('G:', G)
    G = cvxopt_matrix(G)

    h = np.append(np.full((m,), fill_value=1/m),  np.zeros(m))
    print('h:', h)
    h = cvxopt_matrix(h)


    A = cvxopt_matrix(np.ones((1, m)))

    b = cvxopt_matrix(np.ones(1)*nu)

    #Setting solver parameters (change default to decrease tolerance)
    cvxopt_solvers.options['show_progress'] = True
    cvxopt_solvers.options['abstol'] = 1e-10
    cvxopt_solvers.options['reltol'] = 1e-10
    cvxopt_solvers.options['feastol'] = 1e-10

    #Run solver
    sol = cvxopt_solvers.qp(P, q, G, h, A, b)
    alphas = np.array(sol['x'])
    return alphas