First-order methods such as stochastic gradient descent (SGD) have recently become popular optimization to train deep neural networks (DNNs) for good generalization; however, they need a long training time. Second-order which can lower the time are scarcely used on account of their overpriced computing cost obtain second-order information. Thus, many works approximated Hessian matrix cut while ...