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Feng Guo(fguodlut.edu.cn) Abstract: We consider the semiinfinite 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 $\mathbf{K}$. These semidefinite representation sets are indexed by two indices which respectively bound the order of some moment matrices and the degree of sums of squares representations of some polynomials in the construction. As two indices increase, these semidefinite representation sets expand and contract, respectively, and can approximate $\K$ as closely as possible under some assumptions. Some special cases when we can fix one of the two indices or both are also investigated. 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. Keywords: semiinfinite systems, convex polynomials, semidefinite representations, semidefinite programming relaxations, sum of squares, polynomial optimization Category 1: Infinite Dimensional Optimization (Semiinfinite Programming ) Category 2: Linear, Cone and Semidefinite Programming (Semidefinite Programming ) Citation: Download: [PDF] Entry Submitted: 12/29/2018 Modify/Update this entry  
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