# On semi-infinite systems of convex polynomial inequalities and polynomial

## TL;DR

This paper develops a method to approximate semi-infinite convex polynomial inequality systems using semidefinite programming, enabling solutions to related convex polynomial optimization problems with guarantees on accuracy and exactness in special cases.

## Contribution

It introduces a procedure for constructing approximate semidefinite representations of semi-infinite convex polynomial inequality systems and applies this to convex polynomial optimization.

## Key findings

- Constructed semidefinite representations that approximate the feasible set.
- Provided an SDP relaxation method for convex polynomial optimization over these sets.
- Achieved exact SDP relaxation and minimizer extraction in special cases.

## Abstract

We consider the semi-infinite system of polynomial inequalities of the form \[ \mathbf{K}:=\{x\in\mathbb{R}^m\mid p(x,y)\ge 0,\ \ \forall y\in S\subseteq\mathbb{R}^n\}, \] where $p(x,y)$ is a real polynomial in the variables $x$ and the parameters $y$, the index set $S$ is a basic semialgebraic set in $\mathbb{R}^n$, $-p(x,y)$ is convex in $x$ for every $y\in S$. We propose a procedure to construct approximate semidefinite representations of $\mathbf{K}$. There are two indices to index these approximate semidefinite representations. As two indices increase, these semidefinite representation sets expand and contract, respectively, and can approximate $\mathbf{K}$ as closely as possible under some assumptions. In some special cases, we can fix one of the two indices or both. Then, we consider the optimization problem of minimizing a convex polynomial over $\mathbf{K}$. We present an SDP relaxation method for this optimization problem by similar strategies used in constructing approximate semidefinite representations of $\mathbf{K}$. Under certain assumptions, some approximate minimizers of the optimization problem can also be obtained from the SDP relaxations. In some special cases, we show that the SDP relaxation for the optimization problem is exact and all minimizers can be extracted.