An approximation proximal gradient algorithm for nonconvex-linear minimax problems with nonconvex nonsmooth terms

Nonconvex minimax problems have attracted significant attention in machine learning, wireless communication and many other fields. In this paper, we propose an efficient approximation proximal gradient algorithm for solving a class of nonsmooth nonconvex-linear minimax problems with a nonconvex nonsmooth term, and the number of iteration to find an \(\varepsilon \) $\epsilon$ -stationary point is upper bounded by \({\mathcal {O}}(\varepsilon ^{-3})\) $\mathcal{O}({\epsilon }^{-3})$ . Some numerical results on one-bit precoding problem in massive MIMO system and a distributed non-convex optimization problem demonstrate the effectiveness of the proposed algorithm.