Minimax problems have gained tremendous attentions across the optimization and machine learning community recently. In this paper, we introduce a new quasi-Newton method for minimax problems, which call J-symmetric method. The is obtained by exploiting structure of second-order derivative objective function in problem. We show that Hessian estimation (as well as its inverse) can be updated rank...