def METHOD_NAME(n, seed=1):
    """
    Generate sparse problem in dense QP format
    """
    np.random.seed(seed)
    m = int(n / 4) + int(n / 4)
    # m  = n
    n_eq = int(n / 4)
    n_in = int(n / 4)
    P = spa.random(n, n, density=0.75, data_rvs=np.random.randn, format="csc").toarray()
    P = (P + P.T) / 2.0
    s = max(np.absolute(np.linalg.eigvals(P)))
    P += (abs(s) + 1e-02) * spa.eye(n)
    P = spa.coo_matrix(P)
    # print("sparsity of P : {}".format((P.nnz) / (n**2)))
    q = np.random.randn(n)
    A = spa.random(m, n, density=0.95, data_rvs=np.random.randn, format="csc").toarray()
    v = np.random.randn(n)  # Fictitious solution
    delta = np.random.rand(m)  # To get inequality
    u = A @ v
    l = -1.0e20 * np.ones(m)
    return P.toarray(), q, A[:n_eq, :], u[:n_eq], A[n_in:, :], u[n_in:], l[n_in:]