1. Introduction and Related Work
Many applications related to signal processing, machine learning, and robust optimization naturally take the form of min-max problems [1]–[8]. The objective is to simultaneously minimize and maximize the cost function with respect to specific variables such that we find the point of equilibrium (or the saddle-point). In this paper, we consider the cost function : ; and assume that is convex in x and concave in y, where and . Thus, the objective is to find the saddle-point by minimizing with respect to x and maximizing with respect to y, i.e., \begin{equation*}\min_{\mathbf{x}\in \mathbb{R}^{p_{x}}}\max_{\mathbf{y}\in\mathbb{R}^{p_{y}}}F(\mathbf{x,y}).\end{equation*}