A non-convex relaxed version of minimax theorems

TL;DR

This paper presents a relaxed non-convex version of minimax theorems applicable in locally convex spaces, extending classical results and connecting to convex duality and Moreau's theorem.

Contribution

It introduces a non-convex relaxation of minimax theorems, broadening their applicability in convex analysis and duality theory.

Findings

Established a minimax inequality under relaxed conditions.

Connected the results to Moreau's biconjugate theorem.

Provided applications to convex duality.

Abstract

Given a subset $A\times B$ of a locally convex space $X\times Y$ (with $A$ compact) and a function $f:A\times B\rightarrow\overline{\mathbb{R}}$ such that $f(\cdot,y),$ $y\in B,$ are concave and upper semicontinuous, the minimax inequality $\max_{x\in A} \inf_{y\in B} f(x,y) \geq \inf_{y\in B} \sup_{x\in A_{0}} f(x,y)$ is shown to hold provided that $A_{0}$ be the set of $x\in A$ such that $f(x,\cdot)$ is proper, convex and lower semi-contiuous. Moreover, if in addition $A\times B\subset f^{-1}(\mathbb{R})$, then we can take as $A_{0}$ the set of $x\in A$ such that $f(x,\cdot)$ is convex. The relation to Moreau's biconjugate representation theorem is discussed, and some applications to\ convex duality are provided. Key words. Minimax theorem, Moreau theorem, conjugate function, convex optimization.