Before revision: Min-max optimization , with a nonconvex-nonconcave objective function f: R^d \times R^d \rightarrow R , arises in many areas, including optimization, economics, and deep learning.
After revision: Min-max optimization , with a nonconvex-nonconcave objective function f: R^d \times R^d \rightarrow R is an important model for robustness in an adversarial setting, with applications to many areas including optimization, economics, and deep learning.