DC programming and saddle-point problems

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Is there any relation between two?

DC programming problems $$\min_{x\in \mathbb{R}^n} f(x)-g(x),$$ where $f,g$ are both differentiable and convex functions.

Saddle-point programming aim to solve the following convex-concave saddle-point problem $$\min_{x\in \mathbb{R}^n} \max_{y\in \mathbb{R}^n} h(x,y),$$ where $h:\mathbb{R}^n \times \mathbb{R}^n \to \mathbb{R}\cup\{-\infty,+\infty\}$ satisfies that $(\forall y\in Y)h(\cdot,y):\mathbb{R}^n \to \mathbb{R} \cup \{+\infty\}$ is proper and convex, and that $(\forall x \in X) h(x,\cdot) : Y \to \mathbb{R} \cup \{-\infty\}$ is proper and concave.